Genetic fuzzy learning

Russo, Marco

doi:10.1109/4235.873236

Cited by 152 publications

(67 citation statements)

References 29 publications

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“…After several trials with varying learning options, a four-rule model was obtained, which gave three errors in learning the data. Recently, Russo [11] applied a hybrid GA NN approach to learn fuzzy models. He present a five-rule fuzzy model with 18 fuzzy sets and 0 misclassifications.…”

Section: B Example: Iris Datamentioning

confidence: 99%

“…With a population size , we encode the parameters of each fuzzy model (solution) in a chromosome , as a sequence of elements describing the fuzzy sets in the rule antecedents followed by the parameters of the rule consequents. For a model of fuzzy rules, triangular fuzzy sets (each given by three parameters), a -dimensional premise and parameters in each consequent function, a chromosome of length is encoded as (11) where contains the consequent parameters of rule , and contains the parameters of the antecedent fuzzy sets , according to (4). In the initial population is the initial model, and are created by random variation (uniform distribution) around within the defined search space.…”

Section: Proposed Fuzzy Modeling Schemementioning

confidence: 99%

“…The Iris data is a common benchmark in classification and pattern recognition studies [21], [22], [11], [1]. It contains 50 measurements of four features from each of the three species Iris setosa, Iris versicolor, and Iris virginica [23] 1 .…”

Section: B Example: Iris Datamentioning

confidence: 99%

“…Most approaches, however, utilize only the function approximation capabilities of fuzzy systems and little attention is paid to the qualitative aspects. This makes them less suited for applications in which emphasis is not only on accuracy, but also on interpretability, computational complexity, and maintainability [7]- [11].…”

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confidence: 99%

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Compact and transparent fuzzy models and classifiers through iterative complexity reduction

Roubos

Setnes

2001

IEEE Trans. Fuzzy Syst.

241

122

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Abstract-In our previous work we showed that genetic algorithms (GAs) provide a powerful tool to increase the accuracy of fuzzy models for both systems modeling and classification. In addition to these results, we explore the GA to find redundancy in the fuzzy model for the purpose of model reduction. An aggregated similarity measure is applied to search for redundancy in the rule base description. As a result, we propose an iterative fuzzy identification technique starting with data-based fuzzy clustering with an overestimated number of local models. The GA is then applied to find redundancy among the local models with a criterion based on maximal accuracy and maximal set similarity. After the reduction steps, the GA is applied with another criterion searching for minimal set similarity and maximal accuracy. This results in an automatic identification scheme with fuzzy clustering, rule base simplification and constrained genetic optimization with low-human intervention. The proposed modeling approach is then demonstrated for a system identification and a classification problem. Results are compared to other approaches in the literature. Attractive models with respect to compactness, transparency and accuracy, are the result of this symbiosis.Index Terms-Fuzzy classifier, genetic algorithm (GA), Iris data, rule base reduction, Takagi-Sugeno (T-S) fuzzy model, transparency and accuracy.

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Section: B Example: Iris Datamentioning

confidence: 99%

Section: Proposed Fuzzy Modeling Schemementioning

confidence: 99%

Section: B Example: Iris Datamentioning

confidence: 99%

mentioning

confidence: 99%

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Compact and transparent fuzzy models and classifiers through iterative complexity reduction

Roubos

Setnes

2001

IEEE Trans. Fuzzy Syst.

241

122

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“…Genetic Algorithm based methods have been proposed for extracting fuzzy rules and tuning membership function for classification problems. These methods can be categorized into the following four types: Learning fuzzy rules with fixed fuzzy membership functions [9][10][11][12], Learning fuzzy membership functions with fixed fuzzy rules [13], Learning fuzzy rules and membership functions in stages (i.e., first evolving good fuzzy rule sets using fixed membership function, then tuning membership functions using the derived fuzzy rule sets [14,15], Learning fuzzy rules and membership functions simultaneously [16,17].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Classifier Design using Modified Genetic Algorithm

Kumar¹

2010

IJCIS

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Development of fuzzy if-then rules and formation of membership functions are the important consideration in designing a fuzzy classifier system. This paper presents a Modified Genetic Algorithm (ModGA) approach to obtain the optimal rule set and the membership function for a fuzzy classifier. In the genetic population, the membership functions are represented using real numbers and the rule set is represented by the binary string. A modified form of cross over and mutation operators are proposed to deal with the mixed string. The proposed genetic operators help to improve the convergence speed and quality of the solution. The performance of the proposed approach is demonstrated through development of fuzzy classifier for Iris, Wine and Tcpdump data. From the simulation study it is found that the proposed Modified Genetic Algorithm produces a fuzzy classifier which has minimum number of rules and whose classification accuracy is better than the results reported in the literature.

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An asymmetry subsethood‐based neural fuzzy network

Lin

Lee

2008

Asian Journal of Control

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This paper proposes a novel asymmetric subsethood‐based neural fuzzy network (ASNFN) that identifies and controls nonlinear dynamic systems. ASNFN has the flexibility to handle both numeric and linguistic inputs. The numeric inputs in ASNFN are fuzzified by input nodes as tunable feature fuzzifiers. Connections in ASNFN are represented by pseudo‐Gaussian fuzzy sets which provide the neural fuzzy network with higher flexibility and attain more accurate optimization. An online self‐constructing learning algorithm that is constructed and implemented in ASNFN consists of structural learning and parametric learning, and would create adaptive fuzzy logic rules. Computer simulations illustrate the performance and capability of the proposed model in identifying a dynamic system, in Iris data classification, and in approximating a nonlinear function. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

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Genetic fuzzy learning

Cited by 152 publications

References 29 publications

Compact and transparent fuzzy models and classifiers through iterative complexity reduction

Compact and transparent fuzzy models and classifiers through iterative complexity reduction

Fuzzy Classifier Design using Modified Genetic Algorithm

An asymmetry subsethood‐based neural fuzzy network

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