The purpose of this paper is to present a genetic learning process for learning fuzzy control rules from examples. It is developed in three stages: the rst one is a fuzzy rule genetic generating process based on a rule learning iterative approach, the second one combines two kinds of rules, experts rules if there are and the previously generated fuzzy control rules, removing the redundant fuzzy...

In this paper, we propose a novel fuzzy expert system for detection of Coronary Artery Disease, using cuckoo search algorithm. This system includes three phases: firstly, at the stage of fuzzy system design, a decision tree is used to extract if-then rules which provide the crisp rules required for Coronary Artery Disease detection. Secondly, the fuzzy system is formed by setting the intervals ...

This paper presents a Fuzzy Classi er System(FCS) which can discover fuzzy rules e ciently. The system translates human's knowledge into symbolic information, and e ectively limits its search space for the fuzzy rules by utilizing the symbols. The system can also extracts symbolic information from the acquired fuzzy rules for e cient exploration of another new fuzzy rules. Simulations are done ...

In the article the possibility of applying one of the methods of data mining – fuzzy association rules to model fuzzy systems is presented. The algorithm of automatic extracting knowledge base is a modification of the Apriori algorithm. The obtained rules of the model contain weights, that are calculated on the basis of measures of fuzzy association rules and constitute probability of fuzzy eve...

The Chiu’s method which generates a Takagi-Sugeno Fuzzy Inference System (FIS) is a method of fuzzy rules extraction. The rules output is a linear function of inputs. In addition, these rules are not explicit for the expert. In this paper, we develop a method which generates Mamdani FIS, where the rules output is fuzzy. The method proceeds in two steps: first, it uses the subtractive clustering...

The paper considers both knowledge acquisition and knowledge interpretation tasks as tightly connected and continuously interacting processes in a contemporary knowledge engineering system. Fuzzy rules are used here as a framework for knowledge representation. An algorithm REFuNN for fuzzy rules extraction from adaptive fuzzy neural networks (FuNN) is proposed. A case study of Iris classificati...

The problem of finding association rules from a dataset is to find all possible associations that hold among the items, given a minimum support value and a minimum confidence. This involves finding frequent sets first and then the association rules that hold within the items in the frequent sets. The problem of mining temporal association rules from temporal dataset is to find association rules...

An inductive approach for learning fuzzy relational rules is described. Fuzzy relational rules are rules in which fuzzy relations in the antecedent parts of the rules are allowed. These rules allow us to use an extended model of rule with a greather capability to represent knowledge in a similar human way, but with an additional complexity in the search process.

The purpose of this paper is to present a genetic learning process for learning fuzzy control roles from examples. It is developed in three stages: the first one is a fuzzy rule genetic generating process based on a rule learning iterative approach, the second one combines two kinds of rules, experts rules if there are and the previously generated fuzzy control rules, removing the redundant fuz...