Fuzzy rule-based networks for control

Higgins, Charles M.; Goodman, R.M.

doi:10.1109/91.273129

Cited by 51 publications

(31 citation statements)

References 10 publications

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“…Two identification methods are considered in the table: W&M and the method based on subtractive clustering (SC) proposed by Chiu [11]. The W&M method was found to consistently provide better results than other grid partition based methods, such as the algorithm by Higgins and Goodman [18]. The SC method was found to consistently provide better accuracy than other clustering based identification alternatives using the Gath-Geva [1,15], Gustafson-Kessel [17], hard and fuzzy C-means [13] clustering methods.…”

Section: B Comparison Of Different Neuro-fuzzy Methodsmentioning

confidence: 99%

“…Note that in some cases (as for example in the algorithm by Higgins and Goodman [18]), these two substages can be integrated into a standalone algorithm. The tuning process is driven by one or more error metrics.…”

Section: Substage 22: System Tuningmentioning

confidence: 99%

“…For small (roughly up to around [10][11][12][13][14][15][16][17][18][19][20] regressor sizes, an exhaustive evaluation of DT for all the possible selections (a total of 2 M − 1) is feasible. We will call this procedure exhaustive DT search.…”

Section: Variable Selectionmentioning

confidence: 99%

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Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation

Pouzols¹,

Lendasse²,

Barriga³

2010

Fuzzy Sets and Systems

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We propose an automatic methodology framework for short-and long-term prediction of time series by means of fuzzy inference systems. In this methodology, fuzzy techniques and statistical techniques for nonparametric residual variance estimation are combined in order to build autoregressive predictive models implemented as fuzzy inference systems. Furthermore, fuzzy models are shown to be consistently more accurate for prediction in the case of time series coming from real-world applications

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Section: B Comparison Of Different Neuro-fuzzy Methodsmentioning

confidence: 99%

Section: Substage 22: System Tuningmentioning

confidence: 99%

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Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation

Pouzols¹,

Lendasse²,

Barriga³

2010

Fuzzy Sets and Systems

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show abstract

“…Note that in some cases (as in the H&G [9] algorithm), these two substages can be integrated into a standalone algorithm. The tuning process is driven by one or more error metrics.…”

Section: ) Stage 22: System Tuningmentioning

confidence: 99%

Fuzzy inference based autoregressors for time series prediction using nonparametric residual variance estimation

Pouzols

Lendasse²,

Barriga

2008

2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence)

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Abstract-We apply fuzzy techniques for system identification and supervised learning in order to develop fuzzy inference based autoregressors for time series prediction. An automatic methodology framework that combines fuzzy techniques and statistical techniques for nonparametric residual variance estimation is proposed. Identification is performed through the learn from examples method introduced by Wang and Mendel, while the Marquard-Levenberg supervised learning algorithm is then applied for tuning. Delta test residual noise estimation is used in order to select the best subset of inputs as well as the number of linguistic labels for the inputs. Experimental results for three time series prediction benchmarks are compared against LS-SVM based autoregressors and show the advantages of the proposed methodology in terms of approximation accuracy, generalization capability and linguistic interpretability.

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“…As the main goal is to minimize the overall error, the increase in the number of membership functions and rules will be accomplished by an overall analysis of the error in the whole function domain instead of examining only the point where the greatest error occurs as in [34]- [36]. To perform this overall analysis, we consider the final distribution of the SCMF index associated with each of the membership functions of each of the input variables.…”

Section: Determination Of a More Suitable Topology For The Fuzzy Smentioning

confidence: 99%

Self-organized fuzzy system generation from training examples

Rojas

Pomares

Ortega

et al. 2000

IEEE Trans. Fuzzy Syst.

129

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Abstract-In the synthesis of a fuzzy system two steps are generally employed: the identification of a structure and the optimization of the parameters defining it. This paper presents a methodology to automatically perform these two steps in conjunction using a three-phase approach to construct a fuzzy system from numerical data. Phase 1 outlines the membership functions and system rules for a specific structure, starting from a very simple initial topology. Phase 2 decides a new and more suitable topology with the information received from the previous step; it determines for which variable the number of fuzzy sets used to discretize the domain must be increased and where these new fuzzy sets should be located. This, in turn, decides in a dynamic way in which part of the input space the number of fuzzy rules should be increased. Phase 3 selects from the different structures obtained to construct a fuzzy system the one providing the best compromise between the accuracy of the approximation and the complexity of the rule set. The accuracy and complexity of the fuzzy system derived by the proposed self-organized fuzzy rule generation procedure (SOFRG) are studied for the problem of function approximation. Simulation results are compared with other methodologies such as artificial neural networks, neuro-fuzzy systems, and genetic algorithms.Index Terms-Function approximation, fuzzy system design, generation of membership functions and rules.

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Fuzzy rule-based networks for control

Cited by 51 publications

References 10 publications

Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation

Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation

Fuzzy inference based autoregressors for time series prediction using nonparametric residual variance estimation

Self-organized fuzzy system generation from training examples

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