The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kertas kerja ini membincangkan penghampiran dinamik huru-hara berbilang dimensi dengan menggunakan sistem inferens kabur berbilang peringkat. Bilangan peraturan yang termasuk dalam sistem inferens kabur berbilang peringkat adalah lebih kecil berbanding sistem inferens kabur konvensional di mana bilangan peraturan adalah berkadar dengan eksponen bilangan pembolehubah input. Kami juga mencadangkan kaedah untuk mengoptimumkan bentuk fungsi keahlian dan pemilihan pembolehubah input yang sesuai berdasarkan algoritma genetik (GA). Kaedah ini digunakan untuk menghampiri dinamik huru-hara berbilang dimensi biasa. Dengan membahagikan sistem inferens kepada beberapa peringkat, jumlah bilangan peraturan adalah cukup tertekan berbanding dengan sistem satu peringkat. Dalam setiap peringkat inferens hanya sebahagian pembolehubah input digunakan sebagai input, dan output peringkat dianggap sebagai input ke peringkat seterusnya. Untuk memberikan prestasi yang lebih baik, bentuk fungsi keahlian peraturan inferens dioptimumkan dengan menggunakan GA. Setiap individu sepadan dengan sistem inferens, dan kesesuaiannya ditakrifkan dengan menggunakan ralat ramalan. Keputusan eksperimen membawa kita kepada pemilihan bilangan pembolehubah input yang berkaitan dan bilangan peringkat dengan mengambil kira kos pengiraan dan keperluan. Selain GA dalam pengoptimuman fungsi keahlian, kami menggunakan GA untuk menentukan pembolehubah input dan bilangan input. Pemilihan pembolehubah input untuk setiap peringkat, dan bilangan peringkat juga dibincangkan. Kajian simulasi untuk dinamik huru-hara berbilang dimensi menunjukkan bahawa sistem inferens memberikan ramalan yang lebih baik berbanding ramalan oleh rangkaian saraf.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Salinan
Yoshinori KISHIKAWA, Shozo TOKINAGA, "Approximation of Multi-Dimensional Chaotic Dynamics by Using Multi-Stage Fuzzy Inference Systems and the GA" in IEICE TRANSACTIONS on Fundamentals,
vol. E84-A, no. 9, pp. 2128-2137, September 2001, doi: .
Abstract: This paper deals with the approximation of multi-dimensional chaotic dynamics by using the multi-stage fuzzy inference system. The number of rules included in multi-stage fuzzy inference systems is remarkably smaller compared to conventional fuzzy inference systems where the number of rules are proportional to an exponential of the number of input variables. We also propose a method to optimize the shape of membership function and the appropriate selection of input variables based upon the genetic algorithm (GA). The method is applied to the approximation of typical multi-dimensional chaotic dynamics. By dividing the inference system into multiple stages, the total number of rules is sufficiently depressed compared to the single stage system. In each stage of inference only a portion of input variables are used as the input, and output of the stage is treated as an input to the next stage. To give better performance, the shape of the membership function of the inference rules is optimized by using the GA. Each individual corresponds to an inference system, and its fitness is defined by using the prediction error. Experimental results lead us to a relevant selection of the number of input variables and the number of stages by considering the computational cost and the requirement. Besides the GA in the optimization of membership function, we use the GA to determine the input variables and the number of input. The selection of input variable to each stage, and the number of stages are also discussed. The simulation study for multi-dimensional chaotic dynamics shows that the inference system gives better prediction compared to the prediction by the neural network.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e84-a_9_2128/_p
Salinan
@ARTICLE{e84-a_9_2128,
author={Yoshinori KISHIKAWA, Shozo TOKINAGA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Approximation of Multi-Dimensional Chaotic Dynamics by Using Multi-Stage Fuzzy Inference Systems and the GA},
year={2001},
volume={E84-A},
number={9},
pages={2128-2137},
abstract={This paper deals with the approximation of multi-dimensional chaotic dynamics by using the multi-stage fuzzy inference system. The number of rules included in multi-stage fuzzy inference systems is remarkably smaller compared to conventional fuzzy inference systems where the number of rules are proportional to an exponential of the number of input variables. We also propose a method to optimize the shape of membership function and the appropriate selection of input variables based upon the genetic algorithm (GA). The method is applied to the approximation of typical multi-dimensional chaotic dynamics. By dividing the inference system into multiple stages, the total number of rules is sufficiently depressed compared to the single stage system. In each stage of inference only a portion of input variables are used as the input, and output of the stage is treated as an input to the next stage. To give better performance, the shape of the membership function of the inference rules is optimized by using the GA. Each individual corresponds to an inference system, and its fitness is defined by using the prediction error. Experimental results lead us to a relevant selection of the number of input variables and the number of stages by considering the computational cost and the requirement. Besides the GA in the optimization of membership function, we use the GA to determine the input variables and the number of input. The selection of input variable to each stage, and the number of stages are also discussed. The simulation study for multi-dimensional chaotic dynamics shows that the inference system gives better prediction compared to the prediction by the neural network.},
keywords={},
doi={},
ISSN={},
month={September},}
Salinan
TY - JOUR
TI - Approximation of Multi-Dimensional Chaotic Dynamics by Using Multi-Stage Fuzzy Inference Systems and the GA
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2128
EP - 2137
AU - Yoshinori KISHIKAWA
AU - Shozo TOKINAGA
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E84-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2001
AB - This paper deals with the approximation of multi-dimensional chaotic dynamics by using the multi-stage fuzzy inference system. The number of rules included in multi-stage fuzzy inference systems is remarkably smaller compared to conventional fuzzy inference systems where the number of rules are proportional to an exponential of the number of input variables. We also propose a method to optimize the shape of membership function and the appropriate selection of input variables based upon the genetic algorithm (GA). The method is applied to the approximation of typical multi-dimensional chaotic dynamics. By dividing the inference system into multiple stages, the total number of rules is sufficiently depressed compared to the single stage system. In each stage of inference only a portion of input variables are used as the input, and output of the stage is treated as an input to the next stage. To give better performance, the shape of the membership function of the inference rules is optimized by using the GA. Each individual corresponds to an inference system, and its fitness is defined by using the prediction error. Experimental results lead us to a relevant selection of the number of input variables and the number of stages by considering the computational cost and the requirement. Besides the GA in the optimization of membership function, we use the GA to determine the input variables and the number of input. The selection of input variable to each stage, and the number of stages are also discussed. The simulation study for multi-dimensional chaotic dynamics shows that the inference system gives better prediction compared to the prediction by the neural network.
ER -