Neural Network

Neural Network | Workshop on Artificial Neural Network & Fuzzy Logic using MATLAB

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.

Topics to be covered in Workshop 

  1. Introduction
  • Biological Neuron
  • Dendrites
  •  Axon
  • Synapse
  1. Introduction Neural Network
  • BASIC introduction Neuron
  • Activation function      
  • The Neuron Diagram
  • Neuron Models
  • step function
  • ramp function
  • sigmoid function
  • Gaussian function
  1. Network Architectures
  • single-layer feed-forward                                
  • multi-layer   feed-forward               
  • recurrent
  1. Neural Network Learning Rules 
  • Supervised and Unsupervised Learning
  • Hebbian Learning Rule
  • Perceptron Learning Rule
  • Delta Learning Rule
  • Winner Take All Learning Rule 
  1. 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.
  • Introduction of MATLAB Screen.
  • Command Window.
  • Current Directory.
  • 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 

Duration: The duration of this workshop will be two consecutive days, with eight hour session each day in a total of sixteen hours properly divided into theory and hands on sessions.

Certification Policy:

  • Certificate of Participation for all the workshop participants.
  • At the end of this workshop, a small competition will be organized among the participating students and winners will be awarded with a 'Certificate of Excellence'.
  • Certificate of Coordination for the coordinators of the campus workshops.

Eligibility: There are no prerequisites. Anyone interested, can join this workshop.

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