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...

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...

The “Double Fuzzy Point” rule representation opens a new dimension for expressing changes of fuzziness in fuzzy rule-based systems. In the case of standard “Fuzzy Point” rule representations, it is difficult to describe fuzzy functions in which crisp observations are required to have fuzzy conclusions, or in which an increase in the fuzziness of observations leads to reduced fuzziness in conclu...

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...

This paper presents an application of the novel theory of fuzzy networks for optimising models of systems characterised by uncertainty, non-linearity, modular structure and interactions. The application of the theory is demonstrated for retail price models in the context of converting a multiple rule base fuzzy system (MRBFS) into an equivalent single rule base fuzzy system (SRBFS) by linguisti...

the proposed iafc neural networks have both stability and plasticity because theyuse a control structure similar to that of the art-1(adaptive resonance theory) neural network.the unsupervised iafc neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. this fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. the supervised iafc ...

Designing an effective criterion for selecting the best rule is a major problem in theprocess of implementing Fuzzy Learning Classifier (FLC) systems. Conventionally confidenceand support or combined measures of these are used as criteria for fuzzy rule evaluation. In thispaper new entities namely precision and recall from the field of Information Retrieval (IR)systems is adapted as alternative...

Fuzzy rule based systems have been very popular in many engineering applications. In petroleum engineering, fuzzy rules are normally constructed using some fuzzy rule extraction techniques to establish the petrophysical properties prediction model. However, when generating fizzy rules from the available information, it may result in a sparse fuzzy rule base. The use of more than one input varia...

There are many methods introduced to solve the credit scoring problem such as support vector machines, neural networks and rule based classifiers. Rule bases are more favourite in credit decision making because of their ability to explicitly distinguish between good and bad applicants.In this paper multi-objective particle swarm is applied to optimize fuzzy apriori rule base in credit scoring. ...