Rule base generation from numerical data has been a dynamic research topic within the fuzzy community in the last decades, and several well-established methods have been proposed. While some authors presented simple, empirical approaches, but which generally show high error rates, others turned to complex heuristic techniques to improve accuracy. In this paper, an extension of the classical Wan...

To real-time management of the bridges under dynamic conditions, this paper develops a rule-based decision support framework to extract the necessary rules from simulation results made by Aimsun. In this rule-based system, the supervised and the unsupervised learning algorithms are applied to generalize the rules where the initial set of rules are provided by the aid of fuzzy rule generation al...

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 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 ...

This paper presents a Genetic Algorithms (GA) based rule generation method for Fuzzy Power System Stabilizer (FPSS) to enhance damping of the power system low frequency oscillations. This proposed controller is more efficient because it cope with oscillations and different operating points. There is no doubt that fuzzy controller is tuned on line from the knowledge base and fuzzy interference. ...

ÐA fuzzy decision tree is constructed by allowing the possibility of partial membership of a point in the nodes that make up the tree structure. This extension of its expressive capabilities transforms the decision tree into a powerful functional approximant that incorporates features of connectionist methods, while remaining easily interpretable. Fuzzification is achieved by superimposing a fu...

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...

In this paper, simplified ozone models for potential use in integrated assessment are developed from the EMEP ozone model, which is a single-layer Lagrangian trajectory model. The simplification method uses fuzzy rule generation methodology to represent numerous results of the EMEP model as a response surface describing the source-receptor relationships between ozone precursor emissions and dai...