Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective.

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Presentation transcript:

Features of Biological Neural Networks 1)Robustness and Fault Tolerance. 2)Flexibility. 3)Ability to deal with variety of Data situations. 4)Collective Computation.

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

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.

This is called Index of Performance, leads to Widrow Hoff Rule, Delta Rule.