ABSTRACT
This chapter presents classical set theory, fuzzy set and fuzzy inference with their implementations in MATLAB, and discusses fundamental introduction to rough set and rough set-based attribute reduction with real-life examples. It introduces artificial neural network in general and feedforward neural network in particular with back-propagation algorithms, where MATLAB solutions for network construction, and presents training and generalization using data fitting problems for illustration. Artificial neural networks were originated from studying and understanding the behavior of complicated neural networks of living creatures. After training, the neural network can be tested and validated, then, it can be used as a computation unit. In practical applications, the samples can be divided randomly into two groups, with one group used in training, and the other group used in validation. The chapter presents global optimization algorithms and their application with MATLAB functions.
