This paper presents a particle swarm optimization (PSO)-based fuzzy expert system for the diagnosis of coronary artery disease (CAD). The designed system is based on the Cleveland and Hungarian Heart Disease datasets. Since the datasets consist of many input attributes, decision tree (DT) was used to unravel the attributes that contribute towards the diagnosis. The output of the DT was converte...

fuzzy rule-based classification systems (frbcs) are highly investigated by researchers due to their noise-stability and  interpretability. unfortunately, generating a rule-base which is sufficiently both accurate and interpretable, is a hard process. rule weighting is one of the approaches to improve the accuracy of a pre-generated rule-base without modifying the original rules. most of the pro...

Fuzzy logic is a mathematical approach towards the human way of thinking and learning. Based on if-then rules, we can design fuzzy controllers with the intuitive experience of human beings. However, it is not practical for a designer to find necessary number of rules and determine appropriate parameters by hand. Hence, we incorporate a reinforcement learning method with basic fuzzy rules so tha...

Due to its high performance and comprehensibility, fuzzy modelling is becoming more and more popular in dealing with nonlinear, uncertain and complex systems for tasks such as signal processing, medical diagnosis and financial investment. However, there are no principal routine methods to obtain the optimum fuzzy rule base which is not only compact but also retains high prediction (or classific...

This paper presents a novel boosting algorithm for genetic learning of fuzzy classification rules. The method is based on the iterative rule learning approach to fuzzy rule base system design. The fuzzy rule base is generated in an incremental fashion, in that the evolutionary algorithm optimizes one fuzzy classifier rule at a time. The boosting mechanism reduces the weight of those training in...

Sophisticated fuzzy rule systems are supposed to be flexible, complete, consistent and compact (FC). Flexibility, completeness and consistency are essential for fuzzy systems to exhibit an excellent performance and to have a clear physical meaning, while compactness is crucial when the number of the input variables increases. However, the completeness and consistency conditions are often violat...

in the last decade, cellular automata models have been widely used for simulation spatial phenomena; however, exact nature of these models have been lead to various limitations for essential simulation of these phenomena. in this research, traditional cellular automata models have been developed through fuzzy theory, in which, transition rules are expressed based on fuzzy logic control methodol...

Fuzzy Rules have been shown to be very useful in modeling relationships between variables that have a high degree of uncertainty or ambiguity. A major question in regards to learning fuzzy rule bases is how to handle interactions between rules of overlapping coverage. Structures, such as Yager’s HPS (Hierarchical Prioritized Structure), have been proposed to answer this question. In this paper,...

In this paper some problems arising in the interface between two different areas, Decision Support Systems and Fuzzy Sets and Systems, are considered. The Model-Base Management System of a Decision Support System which involves some fuzziness is considered, and in that context the questions on the management of the fuzziness in some optimisation models, and then of using fuzzy rules for termina...

A rule base reduction and tuning algorithm is proposed as a design tool for the knowledge-based fuzzy control of a vacuum cleaner. Given a set of expert-based control rules in a fuzzy rule base structure, proposed algorithm computes the inconsistencies and redundancies in the overall rule set based on a newly proposed measure of equality of the individual fuzzy sets. An inconsistency and redund...