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...

Ontology has a powerful expressive ability on knowledge. In order to share and deal with the fuzzy knowledge on the semantic web, the linguistic variable ontology is proposed as the basis of the Flu Fuzzy Diagnosis System. After that, Protégé is introduced to build the fuzzy rule base. The paper concludes that linguistic variable ontology-based fuzzy diagnosis system can achieve the function of...

In the last thirty years fuzzy logic became very popular. One can find solutions based on it in several fields from industrial systems to house appliances. Recently a new category of fuzzy systems gained more attention, the so called fuzzy rule interpolation (FRI) based systems. Owing to the low complexity of their rule bases, i.e. they can infer as well when only the relevant rules are known, ...

System reliability assessment is one of the major acts in the operation and maintenance of every industrial and service sector, which also holds true for maritime transportation system. The complexity of the maritime transportation system is a prime obstacle in the evaluation of the operational reliability of the system; mainly due to the fact that statistical data on the important parameters a...

A method to extract a fuzzy rule based system from a trained artificial neural network for classification is presented. The fuzzy system obtained is equivalent to the corresponding neural network. In the antecedents of the fuzzy rules, it uses the similarity between the input datum and the weight vectors. This implies rules highly understandable. Thus, both the fuzzy system and a simple analysi...

Understand the behavior of Fuzzy Rule-based Systems (FRBSs) at inference level is a complex task that allows the designer to produce simpler and powerful systems. The fuzzy inference-grams –known as fingrams– establish a novel and mighty tool for understanding the structure and behavior of fuzzy systems. Fingrams represent FRBSs as social networks made of nodes representing fuzzy rules and edge...

In this paper we present an application of rule-based expert systems to a farming problem. Concretely the prediction of the breeding value in Manchego ewes is studied for the early stage of their life in which the standard (BLUP) methodology cannot be applied. In this case the pedigree index (arithmetical mean between parents’ breeding value) is used to make the estimation. An alternative to th...

In this study, a fuzzy logic controller is developed using a new methodology for designing its rule-base. This controller consists of two rule-base blocks and a logical switch in between. The rule-base blocks admit two inputs one of which is newly devised and called “normalized acceleration” and the other one is the classical “error”. The newly devised input gives a relative value about the “fa...

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 ...

The ‘hnique selling point” of fuzzy systems is usually the interpretability of its rule base. However, very often only the U C C U T U C ~ of the rule base is measured and used to compare a fuzzy system to other solutions. We have suggested an index to measurz the interpretability of fuzzy rule bases for classification problems. However, the index can be used to describe the interpretability of...