Fuzzy Model Identification Using Support Vector Clustering Method

PRZEGLĄD ELEKTROTECHNICZNY, 2018

In this paper, we propose a new classification approach which combines the advantages of both Gaussian-kernel Support Vector Machine and Adaptive Fuzzy Inference System. Instead of generating a large number of candidate rules as in fuzzy classification, the proposed method adopts the decision trees to generate rules directly from training data. Decision trees provide architecture to generate fuzzy IF-THEN rules from the training data where the fuzzy parameters of the rules would be optimized using Genetic Algorithm. The Gaussian-kernel SVM will be used in the classification phase using the parameters obtained from Particle Swarm Optimization. Experimental results of the proposed approach has proved significantly better accuracy than other state-of-the-art classification methods by testing it on benchmark UCI datasets.

2004

Abstract: This paper introduces a new method for fuzzy modeling based on set of input-output data pairs. The method consists of a sequence of steps aiming towards developing a Sugeno-type fuzzy model of optimal structure. In the first place, the algorithm uses the fuzzy c-means to classify all the input training data vectors into a predefined number of clusters. The centers of these clusters are further processed by using optimal fuzzy clustering, which is based on the weighted fuzzy c-means algorithm.

2015

Data clustering is a well known technique for fuzzy model identification or fuzzy modelling for apprehending the system behavior in the form of fuzzy if-then rules based on experimental data. Fuzzy c- Means (FCM) clustering and subtractive clustering (SC) are efficient techniques for fuzzy rule extraction in fuzzy modeling of Adaptive Neuro-fuzzy Inference System (ANFIS). In this paper we have employed a novel technique to build the rule base of ANFIS based on the kernel based variants of these two clustering techniques which have shown better clustering accuracy. In kernel based clustering approach, the kernel functions are used to calculate the distance measure between the data points during clustering which enables to map the data to a higher dimensional space. This generalization makes data set more distinctly separable which results in more accurate cluster centers and therefore a more precise rule base for the ANFIS can be constructed which increases the prediction performance...

Fuzzy Sets and Systems, 2007

The relationship between support vector machines (SVMs) and Takagi-Sugeno-Kang (TSK) fuzzy systems is shown. An exact representation of SVMs as TSK fuzzy systems is given for every used kernel function. Restricted methods to extract rules from SVMs have been previously published. Their limitations are surpassed with the presented extraction method. The behavior of SVMs is explained by means of fuzzy logic and the interpretability of the system is improved by introducing the -fuzzy rule-based system ( -FRBS). The -FRBS exactly approximates the SVM's decision boundary and its rules and membership functions are very simple, aggregating the antecedents with uninorms as compensation operators. The rules of the -FRBS are limited to two and the number of fuzzy propositions in each rule only depends on the cardinality of the set of support vectors. For that reason, the -FRBS overcomes the course of dimensionality and problems with high-dimensional data sets are easily solved with the -FRBS.

1996

The main problem in design fuzzy models is to identify their structure. This means recognise the variables that better characterise the system dynamics, the number of membership functions partitioning each variable, as well as their distribution and fuzziness degree. This work presents two pre-processing methods for structure identification of fuzzy models. The first approach uses the statistical method of Principal Component Analysis (PCA). The second one uses a clustering technique called autonomous mountain-clustering method. The statistical method of Principal Component Analysis helps to select the variables that dominate the system dynamics. Besides, this method contributes to design fuzzy models with better performance. The second approach identifies the fuzzy model order. That is, the method identifies the number of membership functions attributed to each variable, as well as their position and width. So, the autonomous mountain-clustering eliminates the usual "trial-and-error" mechanism. The pre-processing methods can be used to initialize the neuro-fuzzy techniques and therefore accelerate their learning process. We test these methods using a simple learning process applied to extract the fuzzy model of an experimental electro-hydraulic system. The results show that a good modeling capability is achieved without employ any complicated optimisation procedure to structure identification.

Fifth International Conference on Fuzzy …, 2008

This paper proposes an architecture to extract fuzzy rules based on support vector machines (SVMs). Firstly, support vectors are obtained from the training data set to generate fuzzy IF-THEN rules with membership functions described in terms of kernel functions via support vector machine learning procedure. Then, a combined fuzzy rule base is created based on both the generated rules and linguistic rules of human experts. Thus, it has the inherent advantages that the rule base is optimized automatically during the SVM learning procedure, and, takes both "subjective" experts' prior knowledge and "objective" training data into account. An example is given to show the effectiveness of the proposed method.

2011 1st International eConference on Computer and Knowledge Engineering (ICCKE), 2011

The epsilon-SVR has two limitations. Firstly, the tube radius (epsilon) or noise rate along the -axis must be already specified. Secondly, this method is suitable for function estimation according to training data in which noise is independent of input (is constant). To resolving these limitations, in approaches like -SVIRN, the tube radius or the radius of estimated interval function which can be variable with respect to input , is determined automatically. Then, for the test sample , the centre of interval function is reported as the most probable value of output according to training samples. This method is useful when the noise of data along the -axis has a symmetric distribution. In such situation, the centre of interval function and the most probable value of function are identical. In practice, the noise of data along the -axis may be from an asymmetric distribution. In this paper, we propose a novel approach which estimates simultaneously an interval function and a triangular fuzzy function. The estimated interval function of our proposed method is similar to the estimated function of -SVIRN. The center of triangular fuzzy function is the most probable value of function according to training samples which is important when the noise of training data along the -axis is from an asymmetric distribution.

… : proceedings of the 6th International FLINS …, 2004

Le Centre pour la Communication Scientifique Directe - HAL - Université Paris Descartes, 2022

2004

Abstract: Fuzzy rules have a simple structure within a multidimensional vector space and they are produced by dismembering this space into fuzzy subspaces. The most efficient way to produce fuzzy partitions in a vector space is the use of fuzzy clustering analysis. This paper proposes a fuzzy clustering-based algorithm, which generates fuzzy rules from a set of input-output data.