IJE TRANSACTIONS A: Basics Vol. 28, No. 10 (October 2015) 1423-1429   

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S. M. A. Hashemi, H. Haji Kazemi and A. Karamodin
( Received: April 12, 2015 – Accepted: October 16, 2015 )

Abstract    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.


Keywords    Wavelet neural network, Evolutionary learning algorithm, Nonlinear function approximation.


چکیده    تقریب توابع غیر خطی یکی از موارد مهم در تشخیص و آنالیز سیستم ها می باشد. مدلهای مختلفی به منظور تقریب دقیق توابع غیر خطی ریاضی ارائه شده است، هر چند که عمده این مدلها، برای مسائل و سیستم های خاص تعریف شده اند. در این مقاله یک شبکه عصبی-موجکی تکاملی به منظور تعیین ساختار و بهینه سازی سیستم های غیر خطی پیشنهاد شده است. مدل پیشنهاد شده از یک مکانیزم شناسایی ساختار و تنظیم پارامترهای ساختار برای مدلسازی توابع و سیستم های غیرخطی دلخواه استفاده می نماید. ساختار پیشنهادی و الگوریتم آموزشی به کار گرفته شده برای آن از طریق مقایسه با سایر روشهای معمول و متداول راستی آزمایی و اعتبارسنجی می شود. شبیه سازی های انجام شده، نمایانگر کارایی و توانایی مدل پیشنهادی در تقریب توابع چندمتغیره غیرخطی ریاضی می باشد.



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