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 menunjukkan reka bentuk sistem inferens kabur berbilang peringkat dengan bilangan peraturan yang lebih kecil berdasarkan pengoptimuman peraturan dengan menggunakan algoritma genetik. Oleh kerana bilangan peraturan sistem inferens kabur meningkat secara eksponen berkadaran dengan bilangan pembolehubah input yang dikuasakan oleh bilangan fungsi keahlian, adalah lebih baik untuk membahagikan sistem inferens kepada beberapa peringkat (sistem inferens kabur berbilang peringkat) dan mengurangkan bilangan peraturan berbanding sistem satu peringkat. Dalam setiap peringkat inferens hanya sebahagian pembolehubah input digunakan sebagai input, dan output peringkat dianggap sebagai input ke peringkat seterusnya. Jika kita menggunakan skema inferens dipermudahkan dan menganggap bentuk fungsi keahlian diberikan, algoritma perambatan belakang yang sama tersedia untuk mengoptimumkan berat setiap peraturan seperti yang biasanya digunakan dalam sistem inferens satu peringkat. Sebaliknya, bentuk fungsi keahlian dioptimumkan dengan menggunakan GA (algoritma genetik) di mana ciri-ciri fungsi keahlian diwakili sebagai satu set rentetan yang mana operasi silang dan mutasi digunakan. Dengan menggabungkan algoritma perambatan belakang dan GA, kami mempunyai skema pembelajaran pengoptimuman komprehensif untuk sistem inferens kabur berbilang peringkat. Sistem inferens digunakan pada penarafan bon automatik berdasarkan nisbah kewangan yang diperoleh daripada penyata kewangan dengan menggunakan penilaian yang ditetapkan bagi penarafan yang diterbitkan oleh institusi penarafan. Hasilnya, kami mempunyai prestasi yang sama bagi sistem inferens kabur berbilang peringkat sebagai sistem satu peringkat dengan bilangan peraturan yang sangat kecil.
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Salinan
Kangrong TAN, Shozo TOKINAGA, "The Design of Multi-Stage Fuzzy Inference Systems with Smaller Number of Rules Based upon the Optimization of Rules by Using the GA" in IEICE TRANSACTIONS on Fundamentals,
vol. E82-A, no. 9, pp. 1865-1873, September 1999, doi: .
Abstract: This paper shows the design of multi-stage fuzzy inference system with smaller number of rules based upon the optimization of rules by using the genetic algorithm. Since the number of rules of fuzzy inference system increases exponentially in proportion to the number of input variables powered by the number of membership function, it is preferred to divide the inference system into several stages (multi-stage fuzzy inference system) and decrease the number of rules compared to the single stage system. In each stage of inference only a portion of input variables are used as the input, and the output of the stage is treated as an input to the next stage. If we use the simplified inference scheme and assume the shape of membership function is given, the same backpropagation algorithm is available to optimize the weight of each rule as is usually used in the single stage inference system. On the other hand, the shape of the membership function is optimized by using the GA (genetic algorithm) where the characteristics of the membership function is represented as a set of string to which the crossover and mutation operation is applied. By combining the backpropagation algorithm and the GA, we have a comprehensive optimization scheme of learning for the multi-stage fuzzy inference system. The inference system is applied to the automatic bond rating based upon the financial ratios obtained from the financial statement by using the prescribed evaluation of rating published by the rating institution. As a result, we have similar performance of the multi-stage fuzzy inference system as the single stage system with remarkably smaller number of rules.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e82-a_9_1865/_p
Salinan
@ARTICLE{e82-a_9_1865,
author={Kangrong TAN, Shozo TOKINAGA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={The Design of Multi-Stage Fuzzy Inference Systems with Smaller Number of Rules Based upon the Optimization of Rules by Using the GA},
year={1999},
volume={E82-A},
number={9},
pages={1865-1873},
abstract={This paper shows the design of multi-stage fuzzy inference system with smaller number of rules based upon the optimization of rules by using the genetic algorithm. Since the number of rules of fuzzy inference system increases exponentially in proportion to the number of input variables powered by the number of membership function, it is preferred to divide the inference system into several stages (multi-stage fuzzy inference system) and decrease the number of rules compared to the single stage system. In each stage of inference only a portion of input variables are used as the input, and the output of the stage is treated as an input to the next stage. If we use the simplified inference scheme and assume the shape of membership function is given, the same backpropagation algorithm is available to optimize the weight of each rule as is usually used in the single stage inference system. On the other hand, the shape of the membership function is optimized by using the GA (genetic algorithm) where the characteristics of the membership function is represented as a set of string to which the crossover and mutation operation is applied. By combining the backpropagation algorithm and the GA, we have a comprehensive optimization scheme of learning for the multi-stage fuzzy inference system. The inference system is applied to the automatic bond rating based upon the financial ratios obtained from the financial statement by using the prescribed evaluation of rating published by the rating institution. As a result, we have similar performance of the multi-stage fuzzy inference system as the single stage system with remarkably smaller number of rules.},
keywords={},
doi={},
ISSN={},
month={September},}
Salinan
TY - JOUR
TI - The Design of Multi-Stage Fuzzy Inference Systems with Smaller Number of Rules Based upon the Optimization of Rules by Using the GA
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1865
EP - 1873
AU - Kangrong TAN
AU - Shozo TOKINAGA
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E82-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 1999
AB - This paper shows the design of multi-stage fuzzy inference system with smaller number of rules based upon the optimization of rules by using the genetic algorithm. Since the number of rules of fuzzy inference system increases exponentially in proportion to the number of input variables powered by the number of membership function, it is preferred to divide the inference system into several stages (multi-stage fuzzy inference system) and decrease the number of rules compared to the single stage system. In each stage of inference only a portion of input variables are used as the input, and the output of the stage is treated as an input to the next stage. If we use the simplified inference scheme and assume the shape of membership function is given, the same backpropagation algorithm is available to optimize the weight of each rule as is usually used in the single stage inference system. On the other hand, the shape of the membership function is optimized by using the GA (genetic algorithm) where the characteristics of the membership function is represented as a set of string to which the crossover and mutation operation is applied. By combining the backpropagation algorithm and the GA, we have a comprehensive optimization scheme of learning for the multi-stage fuzzy inference system. The inference system is applied to the automatic bond rating based upon the financial ratios obtained from the financial statement by using the prescribed evaluation of rating published by the rating institution. As a result, we have similar performance of the multi-stage fuzzy inference system as the single stage system with remarkably smaller number of rules.
ER -