Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

A prototype fuzzy system is quite easy to set up and modify with the techniques within fuzzy system theory when linguistic rules can be given for the desired behavior. Fine-tuning of such a system proves more difficult as well as building a system when no rules can be given. Because this tuning is a very specialistic job, during construction as well as in maintenance, ways were examined to do the tuning automatically. Function approximation is described as a general technique to tune parts of a fuzzy systems based on the desired input and output behavior. Two distinct function approximation techniques were taken into consideration: Techniques based on B-splines (analytically as well as numerically) and techniques based on sigmoids (only numerically, e.g. with the backpropagation procedure used in neural networks). Experiments were done to determine which technique gives the best results when inhomogeneously scattered data points with noise are used. The sigmoid technique seemed give the best approximation in areas were no data points were available (generalization). Some constraints could be given for the position and number of B-splines with respect to the distribution and the amount of the data respectively.

Original language

English

Title of host publication

Proceedings of the 4th International Conference on Neural Networks and their Applications (NEURAP 1996)

4th International Conference on Neural Networks and their Applications, NEURAP 1996

Abbreviated title

NEURAP

Country

France

City

Marseilles

Period

20/03/96 → 22/03/96

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Keywords

METIS-119210

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APA

Author

BIBTEX

Harvard

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RIS

Vancouver

Bijwaard, D. JA., Poel, M., & Ulder, N. (1996). Tuning Fuzzy Systems by Function Approximation. In Proceedings of the 4th International Conference on Neural Networks and their Applications (NEURAP 1996) (pp. 231-237). Marseille, France.

Bijwaard, D.JA. ; Poel, M. ; Ulder, N. / Tuning Fuzzy Systems by Function Approximation. Proceedings of the 4th International Conference on Neural Networks and their Applications (NEURAP 1996). Marseille, France, 1996. pp. 231-237

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abstract = "A prototype fuzzy system is quite easy to set up and modify with the techniques within fuzzy system theory when linguistic rules can be given for the desired behavior. Fine-tuning of such a system proves more difficult as well as building a system when no rules can be given. Because this tuning is a very specialistic job, during construction as well as in maintenance, ways were examined to do the tuning automatically. Function approximation is described as a general technique to tune parts of a fuzzy systems based on the desired input and output behavior. Two distinct function approximation techniques were taken into consideration: Techniques based on B-splines (analytically as well as numerically) and techniques based on sigmoids (only numerically, e.g. with the backpropagation procedure used in neural networks). Experiments were done to determine which technique gives the best results when inhomogeneously scattered data points with noise are used. The sigmoid technique seemed give the best approximation in areas were no data points were available (generalization). Some constraints could be given for the position and number of B-splines with respect to the distribution and the amount of the data respectively.",

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year = "1996",

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Bijwaard, DJA, Poel, M & Ulder, N 1996, Tuning Fuzzy Systems by Function Approximation. in Proceedings of the 4th International Conference on Neural Networks and their Applications (NEURAP 1996). Marseille, France, pp. 231-237, 4th International Conference on Neural Networks and their Applications, NEURAP 1996, Marseilles, France, 20/03/96.

Proceedings of the 4th International Conference on Neural Networks and their Applications (NEURAP 1996). Marseille, France, 1996. p. 231-237.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AB - A prototype fuzzy system is quite easy to set up and modify with the techniques within fuzzy system theory when linguistic rules can be given for the desired behavior. Fine-tuning of such a system proves more difficult as well as building a system when no rules can be given. Because this tuning is a very specialistic job, during construction as well as in maintenance, ways were examined to do the tuning automatically. Function approximation is described as a general technique to tune parts of a fuzzy systems based on the desired input and output behavior. Two distinct function approximation techniques were taken into consideration: Techniques based on B-splines (analytically as well as numerically) and techniques based on sigmoids (only numerically, e.g. with the backpropagation procedure used in neural networks). Experiments were done to determine which technique gives the best results when inhomogeneously scattered data points with noise are used. The sigmoid technique seemed give the best approximation in areas were no data points were available (generalization). Some constraints could be given for the position and number of B-splines with respect to the distribution and the amount of the data respectively.

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Bijwaard DJA, Poel M, Ulder N. Tuning Fuzzy Systems by Function Approximation. In Proceedings of the 4th International Conference on Neural Networks and their Applications (NEURAP 1996). Marseille, France. 1996. p. 231-237