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Artificial Neural Network
Learning Objective
The fundamentals of artificial neural network.The evolution of neural network.Comparison between biological neuron and
artificial neuron.
Artificial Neural Network
An 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
x1
w1
y x2 w2
Yin=x1w1+x2w2
y=f(yin)
X1
Y
X2
Biological Neural Network
Human 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 Neuron
Artificial Neuron
Cell Neuron
Dendrites Weights or interconnections
Soma Net Input
Axon Output
Brain vs. Computer
SpeedProcessingSize and ComplexityStorage Capacity (memory)ToleranceControl Mechanism
Evolution of Neural Network
Basic Models of Neural Network
Three basic entities The model’s synaptic interconnection. The training or learning rules adopted for updating
and adjusting the connection weights. Their activation functions
Connections
There exist five basic types of neuron connection architectures: Single Layer feed forward network Multilayer feed forward network Single node with its own feedback Single layer recurrent network Multilayer recurrent network.
Learning
Parameter LearningStructure Learning
Supervised Learning Unsupervised Learning Reinforcement Learning
Activation Functions
Identity Function: f(x)=x for all xBinary Step Function:
f(x)=1 if x>=t =0 if x<t
Bipolar Step Function: f(x)=1 if x>=t
=-1 if x<tSigmoidal FunctionRamp Functions
f(x)= 1 if x>1x if o<=x<=10 if x<0
Important Terminologies of ANN
WeightsBiasThresholdLearning 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.
0.3 0.2
0.5 0.1 y
-0.3 0.6
x1
y
x3
x2
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 0.6
x1
y
x2
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
-0.2 0.4