|
Course Highlights
Neural and Fuzzy Computing with MATLAB is a two-day course that presents the concepts of neural network and fuzzy logic. In particular, the course will focus on practical aspects of designing neural network and fuzzy logic to solve real-work science and engineering problems. The ultimate aim of this course is to instill full appreciation of the powerful capability of MATLAB, Neural Network Toolbox and Fuzzy Logic Toolbox for the implementation of intelligent-based systems.
Duration
2 days
Prerequisites
”Comprehensive MATLAB” or equivalent and an understanding of data acquisition hardware and instrument control. “Building Graphical User Interfaces with MATLAB” is highly recommended.
Course Outline
Day 1
Topic 1: Introduction to MATLAB
Introduction to basic features and user-interfaces of MATLAB
- The MATLAB desktop environment and interface
- Help features in MATLAB
Topic 2: Neural Network – A Brief Introduction
Presents a brief history of neural network and its MATLAB terminology
- What is neural network
- Neural network application
- Definition of neural network
- Simple neuron model
- MATLAB representation of simple neuron model
- Architecture of neural network
Topic 3: Backpropagation Networks
Learn about one of the most popular neural networks, renowned for its strength in solving highly complex and non-linear problems
- Non-linear problems
- Feedforward BP network
- The feedforward BP network architecture
- Transfer function of BP network
- Learning algorithms for BP network
- Training of backpropagation network
- Bath gradient descent training
- Faster training
- Comparison of training algorithms
- Improve generalization with early stopping
- Pre- and post processing
- Examples
- Exercises
Day 2
Topic 4: Fuzzy Logic – A Brief Introduction
Introduction to fundamental concepts of fuzzy logic as well as implementation in MATLAB
- Fuzzy sets
- Membership functions
- Logical operators
- If-Then rules
Topic 5: Fuzzy Inference System
Learn how to implement the fuzzy inference system using MATLAB Fuzzy Logic Toolbox
- Introduction to the FIS Editor
- Working from the command line
- Examples
- Exercises
Topic 6: Adaptive Neuro-Fuzzy Inference System
Learn how to implement a hybrid neural network and fuzzy logic system
- Takagi-Sugeno fuzzy inference
- Introduction to the ANFIS Editor
- Working from the command line
- Examples
- Exercises
|
