artificial neural network1

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SEMINAR REPORT

ON

Artificial Neural Network

SUBMITTED BY:-

SURAJ AGARWAL

107170

EC-3

B-TECH 3RD YEAR /5TH SEM

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Index

1) Introduction...4

2) Definition..4

3) Structure of human brain..5

4) Neurons....5

5) Basics of ANN model..7

6) Artificial neural network..8

6.1) How ANN differs from Conventional Computer...9

6.2) ANN vs Von Neumann Computer..9

7) Perceptron...10

8) Learning laws..11

8.1) Hebbs Rule.12

8.2) Hopefields Rule..12

8.3) Delta Rule12

8.4) Gradient Descent Rule.13

8.5) Kohonens Learning Rule....13

9) Basic structure of ANN13

10) Network architectures...14

10.1) Single Layer Feed Forward ANN14

10.2) Multi Layer Feed Forward ANN.15

10.3) Recurrent ANN16

11) Learning of ANN..17

11.1) Learning with a Teacher...17

11.2) Learning without a Teacher..18

11.3) Learning tasks...20

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12) Control.22

13) Adaptation22

14) Generalization..23

15) Probabilistic ANN23

16) Advantages of ANN.24

17) Limitations of ANN.25

18) Applications of ANN...25

19) Conclusion...29

20) References...30

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1. Introduction

Ever since eternity, one thing that has made human beings stand apart from the rest of the

animal kingdom is, its brain .The most intelligent device on earth, the Human brain is the

driving force that has given us the ever-progressive species diving into technology and

development as each day progresses.

Due to his inquisitive nature, man tried to make machines that could do intelligent job

processing, and take decisions according to instructions fed to it. What resulted was the

machine that revolutionized the whole world, the Computer (more technically speaking

the Von Neumann Computer). Even though it could perform millions of calculations every

second, display incredible graphics and 3-dimentional animations, play audio and video

but it made the same mistake every time.

Practice could not make it perfect. So the quest for making more intelligent device

continued. These researches lead to birth of more powerful processors with high-tech

equipments attached to it, supercomputers with capabilities to handle more than one

task at a time and finally networks with resources sharing facilities. But still the problem

of designing machines with intelligent self-learning, loomed large in front of mankind. Then

the idea of initiating human brain stuck the designers who started their researches one of

the technologies that will change the way computer work Artificial Neural Networks.

2. Definition

Neural Network is the specified branch of the Artificial Intelligence.

In general, Neural Networks are simply mathematical techniques designed to accomplish a

variety of tasks. Neural Networks uses a set of processing elements (or nodes) loosely

analogues to neurons in the brain (hence the same, neural networks). These nodes are

interconnected in a network that can then identify patterns in data as it is exposed to the data.

In a sense, the network learns from the experience just as people do. Neural networks can be

configured in various arrangements to perform a range of tasks including pattern recognition,

data mining, classification, and process modeling.

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3. Structure of human brain

As stated earlier, Neural Networks is very much similar to the biological structure of

human Brain. Following is the biological structure of brain is given.

Sequential Parallel

Functions: Functions:

Rules Images

Concepts Pictures

Calculations Control

Expert Systems Neural Networks

Learn by Rules Learn by experience

Functions of Brain

As shown in table, left part of the brain consists of rules, concepts and calculations. It

follows Rule Based Learning and hence solves the problem by passing them through

rules. It has Sequential pairs of Neurons. Therefore, this part of brain is similar to the expert

systems. Right part of the brain, as shown below in the figure; consist of functions, images,

pictures, and controls. It follows parallel learning and hence learns through experience. It has

parallel pairs of Neurons. Therefore, this brain is similar to the Neural Network.

4. Neurons

The conceptual constructs of a neural network stemmed from our early understanding of

the human brain. The brain is comprised of billion and billions of interconnected neurons

(some experts estimate upwards of 1011 neurons in the human brain). The fundamental

building blocks of this massively parallel cellular structure are really quite simply when

studied in isolation. A neuron receives incoming electrochemical signals from its

dendrites and collects these signals at the neuron nucleus. The neuron nucleus has a

internal threshold that determines if neuron itself fires in response to the incoming

information. If the combined incoming signals exceeds this threshold then neuron fires and

an electrochemical signal is sent to all neurons connected to the firing neuron on its output

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connections or axons. Otherwise the incoming signals are ignored and the neuron remains

dormant.

There are many types of neurons or cells. From a neuron body

(soma) many fine branching fibers, called dendrites, protrude. The dendrites conduct

signals to the soma or cell body. Extending from a neurons soma, at a point called

axon hillock (initial segment), is a long giber called an axon, which generally splits into

the smaller branches of axonal arborization. The tips of these axon branches (also called

nerve terminals, end bulbs, telondria) impinge either upon the dendrites, somas or axons of

other neurons or upon effectors.

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The axon-dendrite (axon-soma, axon-axon) contact between end bulbs and the cell it

impinges upon is called a synapse. The signal flow in the neuron is (with some exceptions

when the flow could be bi-directional) from the dendrites through the soma converging at

the axon hillock and then down the axon the end bulbs.

A neuron typically has many dendrites but only a single axon. Some neurons lack axons,

such as the amacrine cells.

5. Basics of Artificial Neural Models

The human brain is made up of computing elements, called neurons, coupled with sensory

receptors (affecters) and effectors. The average human brain, roughly three pounds in

weight and 90 cubic inches in volume, is estimated to contain about 100 billion cells of

various types.

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A neuron is a special cell that conducts and electrical signal, and there are about 10 billion

neurons in the human brain. The remaining 90 billion cells are called glial or glue cells,

and these serve as support cells for the neurons.

According to a simplified account, the human brain consists of about ten billion neurons --

and a neuron is, on average, connected to several thousand other neurons. By way of these

connections, neurons both send and receive varying quantities of energy. One very

important feature of neurons is that they don't react immediately to the reception of energy.

Instead, they sum their received energies, and they send their own quantities of energy to

other neurons only when this sum has reached a certain critical threshold. The brain learns

by adjusting the number and strength of these connections. Even though this picture is a

simplification of the biological facts, it is sufficiently powerful to serve as a model for the

neural net.

The motivation for artificial neural network (ANN) researches is the belief that a humans

capabilities, particularly in real-time visual perception, speech understanding, and sensory

information processing and in adaptively as well as intelligent decision making in general,

come from the organizational and computational principles exhibited in the highly

complex neural network of the human brain.

Expectations of faster and better solution provide us with the challenge to build machines

using the same computational and organizational principles, simplified and

abstracted from neurobiological of the brain.

Artificial Neural Network Model

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6. Artificial Neural Network

Artificial neural network (ANNs), also called parallel distributed processing systems

(PDPs) and connectionist systems, are intended for modeling the organization principles of

the central neurons system, with the hope that the biologically inspired computing

capabilities of the ANN will allow the cognitive and sensory tasks to be performed more

easily and more satisfactory than with conventional serial processors. Because of the

limitation of serial computers, much effort has devoted to the development of the parallel

processing architecture; the function of single processor is at a level comparable to

that of a neuron. If the interconnections between the simplest fine-grained processors

are made adaptive, a neural network results.

ANN structures, broadly classified as recurrent (involving feedback) or non-

recurrent (without feedback), have numerous processing

elements (also dubbed neurons, neurodes, units or cells) and connections (forward and

backward interlayer connections between neurons in different layers, forward and backward

interlayer connections or lateral connections between neurons in the same layer, and self-

connections between the input and output layer of the same neuron. Neural networks may

not have differing structures or topology but are also distinguished from one anotherby the

way they learn, the manner in which computations are performed (rule-based, fuzzy, even

nonalorithmic), and the component characteristic of the neurons or the input/output

description of the synaptic dynamics).These networks are required to perform

significant processing tasks through collective local interaction that produces global

properties.

Since the components and connections and their packaging under stringent spatial

constraints make the system large-scale, the role of graph theory, algorithm, and

neuroscience is pervasive.

6.1 How Neural Networks differ from Conventional Computer?

Neural Networks perform computation in a very different way than conventional computers,

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where a single central processing unit sequential dictates every piece of the action. Neural

Networks are built from a large number of very simple processing elements that

individually deal with pieces of a big problem. A processing element (PE) simply

multiplies an output value (table lookup). The principles of neural computation come from

the massive processing tasks, and from the adaptive nature of the parameters (weights)

that interconnected the PEs.

6.2 Similarities and difference between neural net and von Neumann

computer

Neural net Von Neumann computer

Trained ( learning by example) by

adjusting the connection strengths,

threshold and structure.

Programmed with instruction (if-then

analysis based on logic).

Parallel(discrete or continuous), digital,

asynchronous

Sequential or serial

synchronous(with a clock)

May be fault-tolerant because of Distributed

representation and Large -scale redundancy

Not fault-tolerant.

Self-organization during learning Software dependent

Memory & processing elements collocated Memory and processing elements separate

Knowledge stored is adaptable Knowledge stored in address memory

Processing is anarchic and cycle time,

which governs the processing speed, is in

milliseconds range

Processing is autocratic and cycle time,

corresponds to processing one step of

program in the CP during one clock cycle,

is in nanosecond range

7. Perceptron

At the heart of every Neural Network is what is referred to as the perceptron (sometimes

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called processing element or neural node) which is analogous to the neuron nucleus in the

brain. The second layer that is very first hidden layer is known as perceptron. As was the

case in the brain the operation of the perceptron is very simple; however also as is the

case in the brain, when all connected neurons operate as a collective they can provide

some very powerful learning capacity.

Input signals are applied to the node via input connection (dendrites in the case of the brain.)

The connections have strength which changes as

the system learns. In neural networks the strength of the connections are referred to as

weights. Weights can either excite or inhibit the transmission of the incoming

signal. Mathematically incoming signals values are multiplied by the value of those

particular weights.

At the perceptron all weighted input are summed. This sum value is than passed to a

scaling function. The selection of scaling function is part of the neural network design.

The structure of perceptron (Neuron Node) is as follow.

Perceptron

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8. Learning Laws

Many learning laws are in common use. Most of these are some sort of variation of the best

known and oldest learning laws, hebbs rule. Research into different learning functions

continues as new ideas routine show up in trade publication. Some researches have the

modeling of biological learning as their main objective. Others are experimenting with

adaptation of their perceptions of how nature handles learning. Either way, mans

understanding of how neural processing actually works is very limited. Learning is certainly

more complex than the simplification represented by the learning laws currently develop. A

few of the major laws are presented as examples.

8.1 Hebbs Rule

The first, and undoubtedly the best known, learning rule were introduced by Donald Hebb.

The description appeared in his book the Organization ofbehavior in 1949. His basic rule

is: if a neuron receives an input from another neuron and if both are highly active

(mathematically have the same sign), the weight between the neurons should be

strengthened.

8.2 Hopfield Law

It is similar to Hebbs rule with the exception that it specifies the magnitude of the

strengthening or weakening. It states, if the desired output and the input are both active and

both inactive, increment the connection weight by the learning rate, otherwise decrement the

weight by the learning rate.

8.3 The Delta Rule

This rule is a further variation of Hebbs Rule. It is one of the most commonly used.

This rule is based on the simple idea of continuously modifying the strengths of the input

connections to reduce the difference (the delta) between the desired output value and the

actual output of a processing element. Their rule changes the synaptic weights in the way

that minimizes the mean squared error of the network. This rule is also referred to as

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windrows-Hoff Learning rule and the least mean square (LMS) Learning Rule. The way

that the Delta Rule works is that the delta rule error in the output layer is transformed by

the derivative of the transfer function and is then used in the previous neural layer to adjust

input connection weights. In other words, the back-propagated into previous layers one

layer at a time. The process of back-propagating the network errors continues until the first

layer is reached. The network typed called feed forward; back-propagation

derives its name from this method of computing the error term. When using the delta rule,

it is important to ensure that the input data set is well randomized. Well-ordered or

structured presentation of the training set can lend to a network, which cannot converge to

the desired accuracy. If that happens, then network is incapable of learning the problem.

8.4 The Gradient Descent Rule

This rule is similar to the Delta rule in that the derivatives of the transfer function is still

used to modify the delta error before it is applied to the connection weights. Here,

however, an additional proportional constant tied to the learning rate is appended to the

final modifying factor acting upon the weights. This rule is commonly used, even

though it converges to a point of stability very slowly.

It has been shown that different learning rates for different layers of network helpthe learning process converge faster. In these tests, the learning rates for those layers

close to the output were set lower than those layers near the input. This especially

important for applications where the input data is not derived from a strong underlying

model.

8.5 Kohonens Learning Law

The procedure, developed by Teuvo Kohonen, was inspired by learning in biologicalsystems. In this procedure, the processing elements complete for the opportunity to learn, or

update their weights. The processing element with the largest output is declared the winner

and has the capabilities of inhibiting its competitors as well as exciting its neighbors. Only

the winner is permitted an output, and only the winner plus its neighbors are allowed to

adjust their connection weights. Further, the size of the neighborhood can vary during

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the training period. The usual paradigm is to start with a larger definition of the

neighborhood, and narrow in as the training process proceeds. Because the winning

element is defined as the one that has the closest match to the input pattern, Kohonen

networks model the distributed of the data and is sometimes referred to as self-

organizing maps or self-organizing topologies.

9. Basic Structure of artificial neural network

Input layer: The bottom layer is known as input neuron network in this case x1 to x5 are

input layer neurons.

Hidden layer: The in-between input and output layer the layers are known as hidden layers

where the knowledge of past experience / training is kept.

Output Layer: The topmost layer which gives the final output. In this case z1 and z2 are

output neurons.

Basic Structure Of Artificial Neural Network

10. Network architectures

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1). Single layer feed forward networks:

In this layered neural network the neurons are organized in the form of layers. In this simplest

form of a layered network, we have an input layer of source nodes those projects on to an

output layer of neurons, but not vise-versa. In other words, this network is strictly a feed

forward or acyclic type. It is as shown in figure:

Such a network is called single layered network, with designation single later referring to the

o/p layer of neurons.

2). Multilayer feed forward networks:

The second class of the feed forward neural network distinguishes itself by one or more

hidden layers, whose computation nodes are correspondingly called neurons or units. The

function of hidden neurons is intervened between the external i/p and the network o/p in

some useful manner. The ability of hidden neurons is to extract higher order statistics is

particularly valuable when the size of i/p layer is large.

The i/p vectors are feed forward to 1 st hidden layer and this pass to 2 nd hidden layer and

so on until the last layer i.e. output layer, which gives actual network response.

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3). Recurrent networks:

A recurrent network distinguishes itself from feed forward neural network, in that it has

least one feed forward loop. As shown in figures output of the neurons is fed back into its

own inputs is referred as self-feedback.

A recurrent network may consist of a single layer of neurons with each neuron feeding its

output signal back to the inputs of all the other neurons. Network may have hidden layers

or not.

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11. Learning of ANNS

The property that is of primary significance for a neural network is the ability of the

network to learn from environment, and to improve its performance through learning.

A neural network learns about its environment through an interactive process of

adjustment applied to its synaptic weights and bias levels. Network becomes more

knowledgeable about its environment after each iteration of the learning process.

11.1 Learning with a teacher:

1). Supervised learning: the learning process in which the teacher teaches the network by

giving the network the knowledge of environment in the form of sets of the inputs

outputs pre-calculated examples. As shown in figure

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Neural network response to inputs is observed and compared with the predefined output.

The difference is calculated refer as error signal and that is feed back to input layers

neurons along with the inputs to reduce the error to get the perfect response of the

network as per the predefined outputs.

For example, when learning a language, we hear the sound of a word from a teacher.The sound is stored in the memory banks of our brain, and we try to reproduce the

sound. When we hear our own sound, we mentally compare it (actual output) with the

stored sound (target sound) and note the error. If error is large, we try again and again

until it becomes significantly small; then we stop.

11.2 Learning without a teacher:

Unlike supervised learning, in unsupervised learning, the learning process takes place

without teacher that is there are no examples of the functions to be learned by the network.

1).Reinforcement learning / neurodynamic programming: In reinforcement learning, the

learning of an input output mapping is performed through continued interaction with

environment in order to minimize a scalar index of performance. As shown in figure.

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In reinforcement learning, because no information on way the right output should be

provided, the system must employ some random search strategy so that the space of

plausible and rational choices is searched until a correct answer is found.

Reinforcement learning is usually involved in exploring a new environment when some

knowledge (or subjective feeling) about the right response to environmental inputs is

available. The system receives an input from the environment and process an output as

response. Subsequently, it receives a reward or a penalty from the environment. The

system learns from a sequence of such interactions.

In reinforced learning it uses a critic instead of a teacher which helps to indicate whether

the actual input is same as target input or not. The critic does not present the target output

to the network but presents only a pass/fail indication. Thus the error signal produced is

binary: pass or fail.

If the teacher indication is fail the network readjusts its parameter and tries again and

again until it gets its output response right. During this process there is no indication if the

output response is moving in the right direction or how close to the correct output it is.

2). Unsupervised learning: In unsupervised or self-organized learning there is no external

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teacher or critic to over see the learning process. As indicated in figure.

Rather provision is made for a task independent measure of the quality of the

representation that the network is required to learn and the free parameters of thenetwork are optimized with respect to that measure. Once the network has become tuned to

the statistical regularities of the input data, it develops the ability to form internal

representation for encoding features of the input and there by to create the new class

automatically.

For example, show a person a set of different objects. Then ask him/her to separate them

into groups, such that objects in a group have one or more common features that

distinguish them from another group. When this is done, show the same person another

object and ask him/her to place the object in one of the groups. If it does not belong to any

of the existing groups, a new group may be formed.

Even though unsupervised learning does not require a teacher, it requires guidelines to

determine how it will form groups.

11.3 Learning tasks

Pattern recognition:

Humans are good at pattern recognition. We can recognize the familiar face of the person

even though that person has aged since last encounter, identifying a familiar person by

his voice on telephone, or by smelling the fragments comes to know the food etc.

Pattern recognition is formally defined as the process where by a received pattern/signal is

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assigned to one of a prescribed number of classes. A neural network performs pattern

recognition by first undergoing a training session, during which the network is repeatedly

present a set of input pattern along with the category to which each particular pattern

belongs. Later, a new pattern is presented to the network that has not been seen before, but

which belongs to the same pattern category used to train the network.

The network is able to identify the class of that particular pattern because of the information

it has extracted from the training data. Pattern recognition performed by neural network is

statistical in nature, with the pattern being represented by points in a multidimensional

decision space.

The decision space is divided into regions, each one of which is associated with class. The

decision boundaries are determined by the training process.

As shown in figure: in generic terms, pattern-recognition machines using neural network

may take two forms.

1). To extract features through unsupervised network.

2). Features pass to supervised network for pattern classification to give final output.

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12.Control

The control of a plant is another learning task that can be done by a neural network; by a

plant we mean a process or critical part of a system that is to be maintained in a

controlled condition. The relevance of learning to control should not be surprising

because, after all, the human brain is a computer, the output of which as a whole system are

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actions. In the context of control, the brain is living proof that it is possible to build a

generalized controller that takes full advantages of parallel distributed hardware, can

control many thousands of processes as done by the brain to control the thousands of

muscles.

`13. Adaptation

The environment of the interest is no stationary, which means that the statistical parameters

of the information bearing generated by the environment vary with the time. In situation of

the kind, the traditional methods of supervised may learning may prove to be inadequate

because the network is not equipped with the necessary means to track the statistical

variation of the environment in which it operates. To overcome these

shortcomings, it is desirable for a neural network to continually adapt its free parameters tovariation in the incoming signals in a real time fashion. Thus an adaptive system responds

to every distinct input as a novel one. In other words the learning process encountered in

the adaptive system never stops, with learning going on while signal processing is being

performed by the system. This form of learning is called continuous learning or learning on

the fly.

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14. Generalization

In back propagation learning we typically starts with a training sample and uses the back

propagation algorithm to compute the synaptic weights of a multiplayer preceptor by

loading (encoding) as many as of the training example as possible into the network. The

hope is that the neural network so design will generalize. A network is said generalize well

when the input output mapping computed by the network is correct or nearly so for the test

data never used in creating or training the network; the term generalization is borrowed

from psychology.

A neural network that is design to generalize well will produced a correct input output

mapping even when the input is slightly different from the examples used to train the

network. When however a neural network learns too many input output examples the

network may end up memorizing the training data. It may do so by finding a feature that

is present in training data but not true for the underlining function that is to be modeled.

Such aphenomena is referred to as an over fitting or over training. When the network

is over trained it looses the ability to generalize between similar input output pattern.

15.The probabilistic neural network

Another multilayer feed forward network is the probabilistic neural network (PNN). In

addition to the input layer, the PNN has two hidden layers and an output layer. The

major difference from a feed forward network trained by back propagation is that it can

be constructed after only a single pass of the training exemplars in its original form and two

passes is a modified version. The activation function of a neural in the case of the PNN is

statistically derived from estimating of probability density functions (PDFs) based on training

patterns.

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16.Advantages of Neural Networks

1) Networks start processing the data without any preconceived hypothesis. They start

random with weight assignment to various input variables. Adjustments are made based

on the difference between predicted and actual output. This allows for unbiased and better

understanding of data.

2) Neural networks can be retained using additional input variables and number of

individuals. Once trained they can be called on to predict in a new patient.

3) There are several neural network models available to choose from in a particular

problem.

4) Once trained, they are very fast.

5) Due to increased accuracy, results in cost saving.

6) Neural networks are able to represent any functions. Therefore they are called Universal

Approximators.

7) Neural networks are able to learn representative examples by back propagating errors.

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17.Limitations of Neural Network

Low Learning Rate:- For problems requiring a large and complex networkarchitecture or having a large number of training examples, the time needed to

train the network can become excessively long.

Forgetfulness : - The network tends to forget old training e xamples as it is

presented with new ones. A previously trained neural network that must be updated

with new information must be trained using the old and new examples - there is

currently no known way to incrementally train the network.

Imprecision: - Neural networks do not provide precise numerical answer, but

rather relate an input pattern to the most p robable output state.

Black box approach: - Neural networks can be trained to transform an input pattern

to output but provide no insights to the physics behind the transformation.

Limited Flexibility: - The ANNS is designed and implemented for only one

particular system. It is not applicable to another system.

18. Application of Artificial Neural Network

In parallel with the development of theories and architectures for neural networks the

scopes for applications are broadening at a rapid pace. Neural networks may develop

intuitive concepts but are inherently ill suited for implementing rules precisely, as in the

case of rule based computing. Some of the decision making tools of the human brain such

as the seats ofconsciousness, thought, and intuition, do not seem to be within our

capabilities for comprehension in the near future and are dubbed by some to be essentially

no algorithmic. Following are a few applications where neural networks are employedpresently:

1) Time Series Prediction: Predicting the future has always been one of humanitys

desires. Time series measurements are the means for us to characterize and

understand a system and to predict in future behavior. Gershenfield and weighed

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characterization. Forecasting is predicting the

short-term evolution of the system. Modeling involves finding a description that

accurately captures the features of the long-term behavior. The goal of

characterization is to determined the fundamental properties of the system, such as

the degrees of freedom or the amount of randomness. The traditional methods

used for time series prediction are the moving average (ma), autoregressive (ar), or

the combination of the two, the ARMA model. Neural network approaches

produced some of the best short-term predictions. However, methods that

reconstruct the state space by time delay embedding and develop a representation

for the geometry in the systems state space yielded better longer-term predictions

than neural networks in some cases.

2) Speech Generation : One of the earliest successful applications of the back

propagation algorithm for training multiplayer feed forward networks were in a

speech generation system called NET talk, developed by Sejnowski and

Rosenberg. Net talk is a fully connected layered feed forward network with only

one hidden layer. It was trained to pronounce written English text. Turning a

written English text into speech is a difficult task, because most phonological rules

have exceptions that are context-sensitive. Net talk is a simplest network that

learns the function in several hours using exemplars.

3) Speech Recognition : Kohonen used his self-organizing map for inverse

problem to that addressed by Net talk: speech recognition. He developed a

phonetic typewriter for the Finnish language. The phonetic typewriter takes as input

a speech as input speech and converts it into written text. Speech

recognition in general is a much harder problem that turning text into speech.

Current state-of-the-art English speech recognition systems arebased on hidden

Markov Model (HMM). The HMM, which is a Markov process; consist of a

number of states, the transitions between which depend on the occurrence of

some symbol.

4) Autonomous Vehicle Navigation: Vision-based autonomous vehicle and robot

guidance have proven difficult for algorithm-based computer vision methods,

mainly because of the diversity of the unexpected cases that must be explicitly

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dealt with in the algorithms and the real-time constraint. Pomerleau successfully

demonstrated the potential of neural networks for overcoming these difficulties.

His ALVINN (Autonomous Land Vehicle in Neural Networks) set a worked

record for autonomous navigation distance. After training on a two-mile

stretch of highway, it drove the CMU Navlab, equipped with video cameras

and laser range sensors, for 21.2 miles with an average speed of 55 mph on a

relatively old highway open to normal traffic. ALVINN was not distributed by

passing cars while it was driven autonomously. ALVINN nearly doubled the

previous distance world record for autonomous navigation. A network in ALVINN

for each situation consists of a single hidden layer of only four units, an output layer

of 30 units and a 30 X 32 retina for the 960 possible input variables. The retina is fully

connected to the hidden layer, and the hidden layer is fully connected to the output

layer. The graph

of the feed forward network is a node-coalesced cascade version of

bipartite graphs.

5) Handwriting Recognition: Members of a group at AT&T Bell Laboratories have

been working in the area of neural networks for many years. One of their projects

involves the development of a neural network recognizer for handwritten digits. A

feed forward layered network with three hidden layers is used. One of the key

features in this network that reduces the number of free parameters to enhance the

probability of valid generalization by the network. Artificial neural network is also

applied for image processing.

6) In Robotics Field: With the help of neural networks and artificial Intelligence.

Intelligent devices, which behave like human, are designed which are helpful to

human in performing various tasks.

Following are some of the application of Neural Networks in various fields:

Business

o Marketing

o Real Estate

Document and Form Processing

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o Machine Printed Character Recognition

o Graphics Recognition

o Hand printed Character Recognition

o Cursive Handwriting Character Recognition

Food Industry

o Odor/Aroma Analysis

o Product Development

o Quality Assurance

Financial Industry

o Market Trading

o Fraud Detection

o Credit Rating

Energy Industry

o Electrical Load Forecasting

o Hydroelectric Dam Operation

o Oil and Natural Gas Company

Manufacturing

o Process Control

o Quality Control

Medical and Health Care

o Image Analysis

o Drug Development

19.Conclusion

At last it can be said that after 20 or 30 years neural networks will be so developed that it

can find the errors of even human beings and will be able to rectify that errors and

make human being more intelligent .

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20.References

Neural Network by: - Simon Haykin

Understanding neural networks and fuzzy logic by: - Stamatios V. Kartalopoulos

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Artificial Neural Network by:- Robert J. Schalkoff

Internet: -

en.wikipedia.org/wiki/Artificial_neural_network

www.learnartificialneuralnetworks.com

http://www.ibm.com/developerworks/library/l-neural/
http://www.ibm.com/developerworks/library/l-neural/http://www.ibm.com/developerworks/library/l-neural/

artificial neural network1

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