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Artificial Intelligence with Scilab:
Scilab for Neural Network and Fuzzy Logic
 

 
Training Information & Outline


Date: 21-22 November 2017 @Trity Technologies, Puchong, Selangor

Early bird fee: email tina@tritytech.com 
by providing your particulars and contact number, or click on the Enquire Here icon

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Description
A neural network usually involves a large number of processors operating in parallel, each with its own small sphere of knowledge and access to data in its local memory. Neural Networks can learn from noisy data and generalize on unseen data to provide a very powerful machine learning paradigm.

Fuzzy logic is a form of multiple-valued logic which deals with reasoning that is approximate rather than fixed and exact. It represent the human brain better Compared to binary sets (where variables may wither true or false. Fuzzy logic variables may have a value that ranges in degree between 0 and 1. Fuzzy logic has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false.

This is a hands-on application course that provides step-by-step description while concentrating on useful tips and tricks to construct Neural Network and fuzzy inference system, fundamentals and its applications.

Who Should Attend
This training is suitable for participants, particulartly Researchers, Lecturers, Scientists, Engineers and Managers that are keen to use artificial neural network for their application. This hands-on application course with case studies is designed for Scilab users who intend to explore/use Scilab for Artificial Intelligence.

Human Resource Development Fund (HRDF)
Our courses may be submitted to HRDF for SBL claims. Kindly check with your Human Resource Department or Training Unit.  Alternatively, we could also assist you in your application.

Course Objectives
This is a hands-on application course that provides step-by-step description while concentrating on useful tips and tricks to construct Neural Network and fuzzy inference system, fundamentals and its applications.


Outline

Introduction to Neural Network
•    Definition of neural network
•    Biological perspective of neural network
•    Neural network applications
•    Simple neuron model
•    Components of simple neuron
•    SCILAB representation of neural network
•    Single neuron model
•    Neural network with single-layer of neurons
•    Neural network with multiple-layer of neurons

Perceptrons
•    Introduction
•    The perceptron architecture
•    Training of perceptrons
•    Application examples

Linear Networks
•    Introduction
•    Architecture of linear networks
•    The Widrow-Hoff learning algorithm
•    Back propagation Networks

Back propagation Networks
•    Introduction
•    Architecture of backpropagation network
•    The backpropagation algorithm
•    Training algorithms
•    Pre- and post-processing
•    Application examples

Self-Organizing Maps
•    Introduction
•    Competitive learning
•    Self-organizing maps
•    Application examples

Introduction to Fuzzy Logic
•    What is fuzzy logic?
•    Fuzzy sets and Membership functions
•    If-then rules

Fuzzy Inference Systems (FIS)
•    Introduction to FIS
•    Programming FIS
•    Building FIS with user interface
•    Application examples



Trity Technologies Sdn Bhd 874125T
26-3 Jalan Puteri 2/4 Bandar Puteri 47100 Puchong Selangor Malaysia
Tel. 603-80637737 Email tina@tritytech.com

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