Neural Networks and Learning Algorithms in MATLAB [electronic resource] /
by Ardashir Mohammadazadeh, Mohammad Hosein Sabzalian, Oscar Castillo, Rathinasamy Sakthivel, Fayez F. M. El-Sousy, Saleh Mobayen.
- 1st ed. 2022.
- IX, 117 p. 105 illus., 87 illus. in color. online resource.
- Synthesis Lectures on Intelligent Technologies, 2731-6920 .
- Synthesis Lectures on Intelligent Technologies, .
Chapter 1. Introduction -- Chapter 2. Multilayer Perceptron (MLP) Neural Networks -- Chapter 3- Neural Networks Training Based on Recursive Least Squares (RLS) -- Chapter 4. Neural Networks Training Based on Second-Order Optimization Technique -- Chapter 5. Neural Networks Training Based on Genetic Algorithm -- Chapter 6. Neural Network Training Based on Particle Swarm Optimization (PSO) -- Chapter 7- Neural Network Training Based on UKF -- Chapter 8. Designing Neural-Fuzzy PID Controller through Multiobjective Optimization.
This book explains the basic concepts, theory and applications of neural networks in a simple unified approach with clear examples and simulations in the MATLAB programming language. The scripts herein are coded for general purposes to be easily extended to a variety of problems in different areas of application. They are vectorized and optimized to run faster and be applicable to high-dimensional engineering problems. This book will serve as a main reference for graduate and undergraduate courses in neural networks and applications. This book will also serve as a main basis for researchers dealing with complex problems that require neural networks for finding good solutions in areas, such as time series prediction, intelligent control and identification. In addition, the problem of designing neural network by using metaheuristics, such as the genetic algorithms and particle swarm optimization, with one objective and with multiple objectives, is presented.