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

• 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
• 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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