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Hybrid Neural Network

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HYBRID NEURAL NETWORK

HYBRID NEURAL NETWORK

Modern research in theoretical neuroscience can be divided into three categories: cellular biophysics, network dynamics, and statistical analysis of neurobiological data

Organization of synaptic connectivity as the basis of neural computation and learning.

Single and multilayer perceptrons. Dynamical theories of recurrent networks: amplifiers, attractors, and hybrid computation. Back propagation and Hebbian learning. Models of perception, motor control, memory, and neural development.

INTRODUCTION

Hybrid neural systems are computational systems which are based mainly on artificial neural networks and allow for symbolic interpretation or interaction with symbolic components.

The term hybrid neural network can have two meanings:

Biological neural network interacting with artificial neuronal models, and

Artificial neural networks with a symbolic part (or, conversely, symbolic computations with a connectionist part).

NEED FOR ANN:

Cellular Manufacturing Systems (CMS) evolved as an efficient production system, with low set up time, low work-in-process inventory (WIP), short manufacturing lead time, high machine utilization and high quality.

Properly designed structure , to managing the manufacturing system in the short run.

Artificial neural Network in cellular manufacturing system design and a hybrid algorithm as a possible solution to the cell design problem.

ARTIFICIAL NEURAL NETWORK

Artificial Neural Networks (ANNs) are biologically inspired. Specifically, they borrow ideas from the manner in which the human brain works.

The human brain is composed of special cells called neurons.

Estimates of the number of neurons in a human brain cover a wide range (up to 150 billion), and there are more than a hundred different kinds of neurons, separated into groups called networks.

Each network contains several thousand neurons that are highly interconnected. Thus, the brain can be viewed as a collection of neural networks.

Neural computing, use a very limited set of concepts from biological neural systems.

The goal is to simulate massive parallel processes that involve processing elements interconnected in a network architecture.

The artificial neuron receives inputs analogous to the electrochemical impulses biological neurons receive from other neurons.

The output of the artificial neuron corresponds to signals sent out from a biological neuron.

NEURAL NETWORK

These artificial signal can be changed, like the signals from the human brain. Neurons in an ANN receive information from other neurons or from external source, transform or process the information, and pass it on to other neurons or as external outputs.

The value of neural network technology includes its usefulness for pattern recognition, learning, and the interpretation of incomplete and noisy inputs.

Neural networks have the potential to provide some of the human characteristics of problem solving that are difficult to simulate using the logical, analytical techniques of DSS or even expert systems.

One of these characteristics is pattern recognition.

Benefits And Applications Of Neural Networks:

Neural networks can analyze large quantities of data to establish patterns and characteristics in situations where the logic and rules are not known.

Neural networks are especially useful for financial applications such as determining when to buy or sell stock, predicting bankruptcy, and predicting exchange rates.

Beyond its role as an alternative computing mechanism, and in data mining, neural computing can be combined with other computer-based information systems to produce powerful hybrid systems.

Neural computing is emerging as an effective technology in pattern recognition. This capability is being translated to many applications and is sometimes integrated with fuzzy logic.

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