A Rule - Based Fuzzy Model for Nonlinear
نویسندگان
چکیده
This article discusses a rule-based fuzzy model for the identiication of nonlinear MISO (multiple input, single output) systems. The diss cussed method of fuzzy modeling consists of two parts: structure modeling, i.e. determing the numm ber of rules and input variables involved respecc tively, and parameter optimization, i.e. optimizing the location and form of the curves which describe the fuzzy sets. For structure modeling we use a modiied TSK-model. The TSK-model was rst proposed by Takagi, Sugeno, and Kang in 1], 2]. For parameter optimization we propose the use of RPROP, a powerful optimization technique origg inally designed for Neural Network training (see also 3], 4]). We applied RPROP to the modiied version of the TSK-model, implemented the algoo rithm, and tested its performance 5], 6]. In this article we focus on the structure modeling part and show by an example how this structuring algorithm performs an input space partitioning.
منابع مشابه
SECURING INTERPRETABILITY OF FUZZY MODELS FOR MODELING NONLINEAR MIMO SYSTEMS USING A HYBRID OF EVOLUTIONARY ALGORITHMS
In this study, a Multi-Objective Genetic Algorithm (MOGA) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, se- curing low-level and high-level ...
متن کاملA QUADRATIC MARGIN-BASED MODEL FOR WEIGHTING FUZZY CLASSIFICATION RULES INSPIRED BY SUPPORT VECTOR MACHINES
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 fu...
متن کاملA Margin-based Model with a Fast Local Searchnewline for Rule Weighting and Reduction in Fuzzynewline Rule-based Classification Systems
Fuzzy Rule-Based Classification Systems (FRBCS) are highly investigated by researchers due to their noise-stability and interpretability. Unfortunately, generating a rule-base which is sufficiently both accurate and interpretable, is a hard process. Rule weighting is one of the approaches to improve the accuracy of a pre-generated rule-base without modifying the original rules. Most of the pro...
متن کاملReliability Measures Measurement under Rule-Based Fuzzy Logic Technique
In reliability theory, the reliability measures contend the very important and depreciative role for any system analysis. Measurement of reliability measures is not easy due to ambiguity and vagueness which exist within reliability parameters. It is also very difficult to incorporate a large amount of uncertainty in well-established methodologies and techniques. However, fuzzy logic provides an...
متن کاملEdge Detection Based On Nearest Neighbor Linear Cellular Automata Rules and Fuzzy Rule Based System
Edge Detection is an important task for sharpening the boundary of images to detect the region of interest. This paper applies a linear cellular automata rules and a Mamdani Fuzzy inference model for edge detection in both monochromatic and the RGB images. In the uniform cellular automata a transition matrix has been developed for edge detection. The Results have been compared to the ...
متن کاملAn Executive Approach Based On the Production of Fuzzy Ontology Using the Semantic Web Rule Language Method (SWRL)
Today, the need to deal with ambiguous information in semantic web languages is increasing. Ontology is an important part of the W3C standards for the semantic web, used to define a conceptual standard vocabulary for the exchange of data between systems, the provision of reusable databases, and the facilitation of collaboration across multiple systems. However, classical ontology is not enough ...
متن کامل