ABSTRACT
This chapter considers mainly two equalizers: the Type-2 Fuzzy Adaptive Filter based Transversal Equalizer and the Decision Feedback Equalizer. Fuzzy logic and neural networks are complementary technologies in the design of intelligent systems. Fuzzy logic is based on the way the brain deals with inexact information, while neural networks are modeled after the physical architecture of the brain. Fuzzy systems and neural networks are both numerical model-free estimators and dynamic systems. A fuzzy set whose membership function is itself a fuzzy set is called a type-2 fuzzy set. A type-1 fuzzy set is an ordinary fuzzy set. Hence a type-2 fuzzy set is a fuzzy set whose membership values are type-1 fuzzy sets on. Simulations are performed for channel in which a 1000 symbol sequence is used. The first 289 symbols are used for training and the remaining 711 symbols are used for testing. After training, the parameters in all four fuzzy filters are fixed and then testing is performed.
