The aim of this paper is to introduce a novel statement of fuzzy mathematical programming problems and to provide a method for findig a fair solution to these problems. Suppose we are given a mathematical programming problem in which the functional relationship between the decision variables and the objective function is not completely known. Our knowledge-base consists of a block of fuzzy if-t...

This paper describes a formalism for representing imprecise knowledge which combines traditional frame-based formalisms with fuzzy logic and fuzzy IF-THEN rules. Inference in this formalism is based on unification and the calculus of fuzzy IF-THEN rules, and lends itself to an efficient implementation.

In this paper we describe fuzzy rules used in the developed prototype of a “fuzzy music interpretation system” [4]. The core of this system consists of two essential units, the rule base and the inference machine. The rule base contains general IF–THEN interpretation rules, formulated by an experienced pianist. The inference machine contains both conventional and advanced fuzzy information proc...

The monitoring and control of Road traffic is becoming a major problem in many countries. With the ever increasing number of vehicles on the road, the Monitoring authorities have to find new ways or measures of overcoming such a problem. In this paper we discuss the implementation of an intelligent traffic control system using fuzzy logic technology which has the capability of mimicking human i...

This paper describes the various fuzzy rule based techniques for image segmentation. Fuzzy rule based segmentation techniques can incorporate the domain expert knowledge and manipulate numerical as well as linguistic data. They are also capable of drawing partial inference using fuzzy IF-THEN rules. For these reasons they have been intensively applied in medical imaging. But these rules are app...

In this chapter we focus on three bio-inspired algorithms and their combinations with fuzzy rule based systems. Rule Based systems are widely being used in decision making, control systems and forecasting. In the real world, much of the knowledge is imprecise and ambiguous but fuzzy logic provides for systems better presentations of the knowledge, which is often expressed in terms of the natura...

This paper proposes two different approaches to apply Genetic Algorithms to Fuzzy Logic Controllers whose Rule Base is defined through a set of rules. The first one uses the knowledge base of the system as the population of the genetic system (a single rule containing the description of the corresponding fuzzy sets is an individual of the population), while the second uses the knowledge base (c...

To use fuzzy controllers for automization tasks appropriate fuzzy sets and fuzzy rules have to be deened. This can be diicult in some domains, and the resulting controller has to be tuned. Neuro{fuzzy models can help in this tuning process by adapting fuzzy sets and creating fuzzy rules. Combinations of neural networks and fuzzy controllers are suitable if there is only partial knowledge in the...

A description of quantitative dependencies by a novel type of fuzzy rules like ‘If X is SMALL then Y is QUICKLY INCREASING’ is considered. The use of such rules for representation of perception based and numerical information about dependencies between variables is discussed. These rules are based on a granulation of directions of function change or slope values. Perception based information gi...

This paper introduces a new learning algorithm for artificial neural networks, based on a fuzzy inference system ANBLIR. It is a computationally effective neuro-fuzzy system with parametrized fuzzy sets in the consequent parts of fuzzy if-then rules, which uses a conjunctive as well as a logical interpretation of those rules. In the original approach, the estimation of unknown system parameters...