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Neural Network

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Workshop Overview:


The objective of this hands-on workshop is to give insight to MATLAB for Artificial Neural Network & Fuzzy Logic and provide hands-on experience in selected applications. This leads to solve the complex and dynamic real time problems. This workshop provide s a vibrant opportunity for researchers and faculty members.


1. Introduction

  • Biological Neuron
  • Dendrites
  • Axon
  • Synapse

2. Introduction Neural Network

  • BASIC introduction Neuron
  • Activation function
  • The Neuron Diagram
  • Neuron Models
  • step function
  • ramp function
  • sigmoid function
  • Gaussian function

3. Network Architectures

  • Supervised and Unsupervised Learning
  • Hebbian Learning Rule
  • recurrent

4. Neural Network Learning Rules

  • Supervised and Unsupervised Learning
  • Hebbian Learning Rule
  • Perceptron Learning Rule
  • Delta Learning Rule
  • Winner Take All Learning Rule

5. Fuzzy Logic

  • Definition of fuzzy
  • Fuzzy Logic Representation
  • Fuzzy Logic Example

Introduction of MATLAB

  • About MATLAB.
  • MATLAB Screen
  • Variable , array , Matrix , Indexing
  • Operators (Arithmetic, relational, Logical ).
  • Display Facilities
  • Flow Control (IF, Switch ,For ,While ,Break) .
  • Command line
  • M-File
  • Mat-file.
  • Scripts and Functions.
  • Data storage.
  • Input/output capability.

Working On MATLAB Environment

  • How to open, quit and work on command window.
  • Command Window.
  • Workspace.
  • Command history.
  • Introduction of useful command

Getting Started with Neural Network Toolbox

  • Classify Patterns with a Neural Network
  • Neural Network Pattern Recognition Tool.
  • Neural Network Fitting Tool.
  • Network Time Series Tool.
  • Parallel Computing on CPUs and GPUs

Neural Networks: MATLAB examples

  • Calculate the output of a simple neuron
  • Classification of linearly separable data with a perceptron
  • Classification of a 4-class problem with a 2-neuron perceptron
  • ADALINE time series prediction with adaptive linear filter
  • Classification of an XOR problem with a multilayer perceptron
  • Classification of a 4-class problem with a multilayer perceptron
  • Radial basis function networks for classification of XOR problem
  • 1D and 2D Self Organized Map

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