This chapter describes the basics of fuzzy logic, a group of theories and techniques that uses values in the interval [0,1] where traditional theories use either 0 or 1. Basically, to apply the term fuzzy logic to a system means to break somewhere inside that system the all-pervasive laws

Applications of fuzzy logic in heuristic control have been highly successful, but which aspects of fuzzy logic are essential to its practical usefulness? This paper shows that an apparently reasonable version of fuzzy logic collapses mathematically to two-valued logic. Moreover, there are few

been introduced in ML such as: neural networks, genetic algorithms, genetic programming, fuzzy logic and also many hybrid approaches as a combination of some aforementioned ones. Using Machine Learning with Fuzzy techniques we implement Fuzzy Q Learning Algorithm.

to handwritten mathematical expression recognition is presented. Our approach is built upon traditional shiftreduce parsing methods which have linear complexity, thus providing the necessary syntax checking and also the basis for an efficient system. Fuzzy logic is introduced to the basic parsing framework

proposed an intelligent fuzzy logic based computer vision system for classification of whole cashew kernels by assigning the grades. Proposed ed approach is divided into various phases. Image acquisition phase captures images of the cashew kernel under investigation. Pre-processing Pre phase smoothes

Fuzzy Logic was intruded by Lotfi A. Zadeh in 1965. It is for solving uncertainty and ambiguity problems. Fuzzy logic is a multivalued logic defined in [0,1]. However, there are a lot of paradoxes, as proposition, can not be described in Fuzzy Logic. In this paper, we will define an extended fuzzy

The paper proposes a formal delimitation of the class of ‘fuzzy logics ’ and answers some objections that can be raised against the definition; the focus is put on informal and motivational aspects of the problem. Key words: Non-classical logics, formal fuzzy logic, weakly implicative fuzzy logics

The validation set of a formula in a fuzzy logic is the set of all truth values which this formula may achieve. We summarize characterizations of validation sets of S-fuzzy logics and extend them to the case of R-fuzzy logics.

A framework for tissue discrimination in Magnetic Resonance brain images based on predicates analysis and Compensatory

based trend classification for fault diagnosis of chemical processes