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Features of Biological Neural Networks
1) Robustness and Fault Tolerance.
2) Flexibility.
3) Ability to deal with variety of Data situations.
4) Collective Computation.
Neural Networks Basics
Biological Neural Netwroks.
Parts of Biological Neuron
1) Cell Body 2) Dendrites
3) Axon Hillock
4) Axon
5) Synapse
6) Nucleous
Comparison of Computer and Biological Neural Networks.
1) Speed
2) Processing
3) Size and Complexity
4) Storage
5) Fault Tolerance
6) Controll Mechanism
Benefits of Artificial Neural Networks 1) Non Linearity 2) Input/ Output Map 3) Adaptivity 4) Evidential Response 5) Contextual Information 6) Fault Tolearnce 7) VLSI Implementability 8) Uniformity in Analysis and Design 9) Neurobiological Analogy
Define ANN
Artificial Neural Network ( Terminology)
1)Processing Unit ( Activation values and Activation functions) 2) Interconnections ( defined by weight) 3) Operations
1) Activation Dynamics: Activation states : Activation State Space. 2) Output States: Output State Space. 4) Weights 1) Set of all weights : Weight Space. 2) Adjustment of Weights: Learning. 3) Updation of Weights: Learning Algorithm. 5) Update: Output can be updated synchronously or asynchronosly.
s is called activation function.
Activation function used in MP Model.
Graph for Activation function for MP Model.
We cannot readjust the weights.
Rosenblatt’s Perceptron Model.
b is desired/ target output, s is actual output
Widrow’s Adaline Model
b is desired/ target output, s is actual output.
Types of Activation Functions
Neural Network Architecture
Multi Layer Feed Forward Network
Recurrent Neural Network
Given Logic Gates ( Truth Tables): Given Circuits: Realize it.
Given Circuit: Find Truth Tables, Find Logic Function using K Map.
Given Logical Function: Find Truth Table and Circuits, using Basic Circuits.
Problems
Learning
This is called Index of Performance, leads to Widrow Hoff Rule, Delta Rule.
Incremental Change
New Weights
Consider
The first parameter, is input vector, second parameter is target output
Hebb’s Learning
Yagnanarayana Page 15- Page 31
Simon Haykin Page 23- Page 37
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