14594_artificial neural network
Post on 28-Dec-2015
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Artificial Neural Network
Learning ObjectiveThe fundamentals of artificial neural network.The evolution of neural network.Comparison between biological neuron and artificial neuron.
Artificial Neural NetworkAn efficient information processing system which resembles in characteristics with a biological NN.Consists of processing elements known as neurons.Information is contained in the form of weights associated with each connection link.ANN is having the ability to learn, recall and generalize training patterns.Each neuron has an internal state i.e. activation or activity level of neuron, which is the function of the inputs the neuron inputs.
Architecture of Simple ANN
y x2 w2
Biological Neural NetworkHuman brain consists of a huge number of neurons approximately 1011.
Soma or Cell Body: where cell nucleus is located.Dendrites: where the nerve is connected to the cell body. Axon: which carries the impulses of the neuron
Terminology relationships b/w biological and artificial neurons
Biological NeuronArtificial NeuronCellNeuronDendritesWeights or interconnectionsSomaNet InputAxonOutput
Brain vs. ComputerSpeedProcessingSize and ComplexityStorage Capacity (memory)ToleranceControl Mechanism
Evolution of Neural Network
Basic Models of Neural NetworkThree basic entitiesThe models synaptic interconnection.The training or learning rules adopted for updating and adjusting the connection weights.Their activation functions
ConnectionsThere exist five basic types of neuron connection architectures:Single Layer feed forward networkMultilayer feed forward networkSingle node with its own feedbackSingle layer recurrent networkMultilayer recurrent network.
LearningParameter LearningStructure Learning
Supervised LearningUnsupervised LearningReinforcement Learning
- Activation FunctionsIdentity Function: f(x)=x for all xBinary Step Function: f(x)=1 if x>=t =0 if x=t =-1 if x1x if o
Important Terminologies of ANNWeightsBiasThresholdLearning RateMomentum Factor: Vigilance Parameter: control the degree of similarity required for patterns to be assigned to the same cluster unit.
Ex: For the network shown in figure, calculate the net input to the output neuron.
0.5 0.1 y
Ex. Calculate the net input for the network shown in figure with bias included in the network
0.2 0.45 0.3 y 0.7 0.6x1yx2
Ex.: Obtain the output of the neuron y for the network shown in figure using activation functions as:1. binary sigmoidal2. bipolar sigmodial
0.8 0.35 0.1
0.6 0.3 y