A jilzzy layered neural network/or classification and rule generation is proposed using logical neurons. II can handle uncertainty and/or impreciseness in the input as well as the output. Logical operators, namely, t­ norm T and t-conorm S involving And and Or neurons, are employed in place of the weighted sum and sigmoid functions. Various/uzzy implication operators are introduced to incorpora...

A connectionist expert system model, based on a fuzzy version of the multilayer perceptron developed by the authors, is proposed. It infers the output class membership value(s) of an input pattern and also generates a measure of certainty expressing confidence in the decision. The model is capable of querying the user for the more important input feature information, if and when required, in ca...

This paper examines the effect of rule weights in fuzzy rule-based classification systems. Each fuzzy IF–THEN rule in our classification system has antecedent linguistic values and a single consequent class. We use a fuzzy reasoning method based on a single winner rule in the classification phase. The winner rule for a new pattern is the fuzzy IF–THEN rule that has the maximum compatibility gra...

Fuzzy rule-based systems have been very popular in many engineering applications. In mineral engineering, fuzzy rules are normally constructed using some fuzzy rule extraction techniques to establish the determination model in predicting the d50c of hydrocyclones. However, when generating fuzzy rules from the available information, it may result in a sparse fuzzy rule base. The use of more than...

Inverse kinematics is computationally expensive and can result in significant control delays in real lime. Fur a redundant robot, additional computations are required for the inversekinematic solution through optimization schemes. Based on h e fact that humans do not compute exact inverse kinematics, but can do precise positioning from heuristics. we developed an inverse kinematic mapping &ugh ...

A methodology is described for evolving a Rough-fuzzy multi layer perceptron with modular concept using a genetic algorithm to obtain a structured network suitable for both classification and rule extraction. The modular concept, based on “divide and conquer” strategy, provides accelerated training and a compact network suitable for generating a minimum number of rules with high certainty value...

This paper is concerned with the learning of basic behaviors in autonomous robots. In this way, we present a method for the adaptation of basic reactive behaviors implemented as fuzzy controllers applying a genetic algorithm to the evolution of the fuzzy rule system. In this sense, we show our experiments in the evolution of control rules based on symbolic concepts represented as linguistic lab...

A new scheme of knowledge-based classification and rule generation using a fuzzy multilayer perceptron (MLP) is proposed. Knowledge collected from a data set is initially encoded among the connection weights in terms of class a priori probabilities. This encoding also includes incorporation of hidden nodes corresponding to both the pattern classes and their complementary regions. The network ar...

Fuzzy rule based systems have been very popular in many engineering applications. However, when generating fuzzy rules from the available information, it may result in a sparse fuzzy rule base. Fuzzy rule interpolation techniques have been established to solve the problems encountered by sparse rule bases. In most engineering applications, the use of more than one input variable is common. This...

Fuzzy controllers with variable universe of discourse (VUD) have been applied in many fields of intelligent controlling because of their high-accuracy performance. This paper provides a lookup table method to design backing-up fuzzy controllers based on VUD. By setting a set of random start points, input–output data pairs are obtained using test-driving method. One data pair defines one fuzzy r...