Artificial Intelligence with Scilab
Scilab for Neural Network and Fuzzy Logic
Start your first artifical intelligence journey by learning how to use Scilab and modules for neural network and fuzzy logic.
“With Scilab as the engine, create and design your own fuzzy inference system or neural network would no longer a pain.”
Course Synopsis
Neural network and fuzzy logic are two main fields in artificial intelligence to simulate human intelligence in machine.
Neural network is a computational and engineering methodology based on emulating how nature has implemented biological brain (in particular, the brain's massively parallel and learning aspects). As such, it holds promise for significant impact on how important classes of scientific and engineering problems are solved. Neural networks are an adaptable system that approximates the operation of the human brain and the central nervous system.
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.
Current applications of neural networks and fuzzy logic include: oil exploration data analysis, weather prediction, pattern recognition, the interpretation of nucleotide sequences in biology labs, the exploration of models of thinking and consciousness in addition to many other applications.
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.
Who Must Attend
Researchers, Lecturers, Scientists, Engineers and Managers that are keen to use artificial intellegence systems for your application. This hands-on application course with case studies is designed for Scilab users who intend to use Scilab in the areas of Neural Network.
Prerequisites
Candidates must have experience with basic computer operation. Preferably attended our Numerical Computation with SCILAB course.
Course 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
Backpropagation 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