Article

Fuzzy modeling using generalized neural networks and Kalman filter algorithm

Author:

Jyh-Shing R. JangAuthors Info & Claims

AAAI'91: Proceedings of the ninth National conference on Artificial intelligence - Volume 2

Pages 762 - 767

Published: 14 July 1991 Publication History

Abstract

We propose a new approach to build a, fuzzy inference system of which the parameters can be updated to achieve a desired input-output mapping. The structure of the proposed fuzzy inference system is called generalized neural networks, and its learning procedure (rules to update parameters) is basically composed of a gradient descent algorithm and Kalman filter algorithm. Specifically, we first introduce the concept of generalized neural networks (GNN's) and develop a gradient-descent-based supervised learning procedure to update the GNN's parameters. Secondly, we observe that if the overall output of a GNN is a linear combination of some of its parameters, then these parameters can be identified by one-time application of Kalman filter algorithm to minimize the squared error, According to the simulation results, it is concluded that the proposedl new fuzzy inference system can not only incorporate prior knowledge about the original system but also fine-tune the membership functions of the fuzzy rules as the training data set varies.

References

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Ikeda, S.; Ochiai, M.; and Sawaragi, Y. 1976. Sequential GMDH algorithm and its application to river flow prediction. IEEE Trans. on Systems, Man, and Cybernetics 6(7):473-479.

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Kalman, R. E. 1960. A new approach to linear filtering and prediction problems. Journal of Basic Engineering 35-45.

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Balmat JLafont FAli APessel NFernández J(2019)Evaluation of the reference evapotranspiration for a greenhouse crop using an Adaptive-Network-Based Fuzzy Inference System (ANFIS)Proceedings of the 3rd International Conference on Machine Learning and Soft Computing10.1145/3310986.3310987(203-205)Online publication date: 25-Jan-2019
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Fuzzy modeling using generalized neural networks and Kalman filter algorithm
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Machine learning approaches

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cover image Guide Proceedings

AAAI'91: Proceedings of the ninth National conference on Artificial intelligence - Volume 2

July 1991

931 pages

ISBN:0262510596

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AAAI: American Association for Artificial Intelligence

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AAAI Press

Publication History

Published: 14 July 1991

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Cited By

Eappen GShankar T(2020)A Survey on Soft Computing Techniques for Spectrum Sensing in a Cognitive Radio NetworkSN Computer Science10.1007/s42979-020-00372-z1:6Online publication date: 20-Oct-2020
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Balmat JLafont FAli APessel NFernández J(2019)Evaluation of the reference evapotranspiration for a greenhouse crop using an Adaptive-Network-Based Fuzzy Inference System (ANFIS)Proceedings of the 3rd International Conference on Machine Learning and Soft Computing10.1145/3310986.3310987(203-205)Online publication date: 25-Jan-2019
https://dl.acm.org/doi/10.1145/3310986.3310987
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Boulkaibet IBelarbi KBououden SMarwala TChadli M(2017)A new T-S fuzzy model predictive control for nonlinear processesExpert Systems with Applications: An International Journal10.1016/j.eswa.2017.06.03988:C(132-151)Online publication date: 1-Dec-2017
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Al-Dunainawi YAbbod MJizany A(2017)A new MIMO ANFIS-PSO based NARMA-L2 controller for nonlinear dynamic systemsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2017.04.01662:C(265-275)Online publication date: 1-Jun-2017
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Bakırcıoğlu VŞen MKalyoncu M(2016)Adaptive Neural-Network Based Fuzzy Logic (ANFIS) Based Trajectory Controller Design for One Leg of a Quadruped RobotProceedings of the 5th International Conference on Mechatronics and Control Engineering10.1145/3036932.3036954(82-85)Online publication date: 14-Dec-2016
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