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International Journal of Engineering، جلد ۲۸، شماره ۱۰، صفحات ۱۴۲۳-۱۴۲۹
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| کلیدواژه‌های فارسی مقاله |
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| عنوان انگلیسی |
Verification of an Evolutionary-based Wavelet Neural Network Model for Nonlinear Function Approximation |
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| چکیده انگلیسی مقاله |
Nonlinear function approximation is one of the most important tasks in system analysis and identification. Several models have been presented to achieve an accurate approximation on nonlinear mathematics functions. However, the majority of the models are specific to certain problems and systems. In this paper, an evolutionary-based wavelet neural network model is proposed for structure definition and optimization of nonlinear systems. The proposed model involves structure identification and also a parameter tuning phase to be adapted for modeling of an arbitrary system. The proposed structure and the learning algorithm are validated by comparing with some other most commonly used alternatives. The simulation shows the performance and adaptability of the proposed model in approximating multivariate nonlinear mathematics functions. |
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| نویسندگان مقاله |
Abbas Karamodin |
Civil, Ferdowsi Universty
H. Haji Kazemi |
Civil Eng., Ferdowsi University of Mashhad
Seyyed Mohammad Ali Hashemi |
Civil Engineering, Ferdowsi University of Mashad
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| نشانی اینترنتی |
http://www.ije.ir/article_72594_4cffc662e2ed0390ac4ee7fba23c224b.pdf |
| فایل مقاله |
اشکال در دسترسی به فایل - ./files/site1/rds_journals/409/article-409-2062429.pdf |
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| زبان مقاله منتشر شده |
en |
| موضوعات مقاله منتشر شده |
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