ABSTRACT
This chapter discusses the Compensatory Neuro-Fuzzy Filter (CNFF), as an alternate technique for channel equalization. CNFF can be constructed by learning from training examples. It can be contrasted with traditional fuzzy logic control systems in their network structure and learning ability. A fuzzy logic system is unique in that it is able to simultaneously handle numerical data and linguistic knowledge. It is a nonlinear mapping of an input data vector into a scalar output, that is, it maps numbers into numbers. The chapter considers a CNFF based equalizer whose high performance makes it suitable for high-speed channel equalization. The compensatory fuzzy reasoning method is used in adaptive fuzzy operations that can make the fuzzy logic system more adaptive and effective. Zimmermann first defined the essence of compensatory operations. Zhang and Kandel have proposed more extensive compensatory operations based on the pessimistic operation and the optimistic operation.
