Recently, tuning the weights of the rules in Fuzzy Rule-Base Classification Systems is researched in order to improve the accuracy of classification. In this paper, a margin-based optimization model, inspired by Support Vector Machine classifiers, is proposed to compute these fuzzy rule weights. This approach not only considers both accuracy and generalization criteria in a single objective fun...

this paper presents a design procedure for an adaptive power management control strategy based on a driving cycle recognition algorithm. the design goal of the control strategy is to minimize fuel consumption and engine-out nox, hc and co emissions on a set of diversified driving schedules. seven facility-specific drive cycles are considered to represent different driving scenarios. for each fa...

Fuzzy rule based systems are increasingly being used to deal with time series processes that may lack stochastic stability due to non-stationarity, multiscaling and persistent autocorrelations. Wavelet filtering can be used to deal with such phenomenon. A method for creating a fuzzy-rule base from a time series, where the first difference (returns) of the preprocessed series is used, and high f...

Given the central role that software development plays in the delivery and application of information technology, managers have been focusing on process improvement in the software development area. This improvement has increased the demand for software measures, or metrics to manage the process. This metrics provide a quantitative basis for the development and validation of models during the s...

Genetic Programming can be used to evolve Fuzzy Rulebased classifiers [7]. Fuzzy GP depends on a grammar defining valid expressions of fuzzy classifiers, and guarantees that all individuals in the population are valid instances of it all along the evolution process. This is accomplished by restricting crossover and mutation so that they only take place at points of the derivation tree represent...

Sparse rule base and interpolation have been proposed as possible solution to alleviate the geometric complexity problem of large fuzzy set. So far, however, there's no formal method available to extract sparse rule base. This paper combines the recently introduced Cartesian representation of membership functions and a mountain method-based clustering technique for extraction. A case study is i...

This project of dissertation presents a method to generate fuzzy rules based on the conditional fuzzy clustering algorithm, that aims at handling the issue of interpretability of the rule base. The balance between interpretability and accuracy of fuzzy rules is addressed by means of the definition of contexts formed with a small number of attributes and the generation of clusters conditioned by...

We report on experiments designed to highlight the strengths and weaknesses of an autonomous rule acquisition algorithm for the fuzzy controller of a simulated mobile robot. The algorithm is a Pittsburghstyle Fuzzy Classifier System. The highly cross-coupled and co-operative nature of fuzzy inference systems makes autonomous creation of an optimal rule-base a tough proposition. However, our res...

This paper shows possibility of extraction Social, Group and Individual Mind from Multiple Agents Rule Bases. Types those Rule bases are selected as two fuzzy systems, namely Mambdani and Takagi-Sugeno fuzzy system. Their rule bases are describing (modeling) agent behavior. Modifying of agent behavior in the time varying environment will be provided by learning fuzzyneural networks and optimiza...

—Takagi-Sugeno-Kang fuzzy model to assist with real estate appraisals is described and optimized using evolutionary algorithms. Two approaches were compared in the paper. The first one consisted in learning the rule base and the second one in combining learning the rule base and tuning the membership functions in one process. Moreover two model variants with three and five triangular and trapez...