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Self-recurrent wavelet neural network observer for a class of nonlinear systems using adaptive learning rate

عنوان مقاله: Self-recurrent wavelet neural network observer for a class of nonlinear systems using adaptive learning rate
شناسه ملی مقاله: CBCONF01_0249
منتشر شده در اولین کنفرانس بین المللی دستاوردهای نوین پژوهشی در مهندسی برق و کامپیوتر در سال 1395
مشخصات نویسندگان مقاله:

Milad Malekzadeh - Babol University. of Technology Babol,Iran
Shima Ahangar - Babol University. of Technology Babol,Iran
Roozbeh Ashabi - Babol University. of Technology Babol,Iran

خلاصه مقاله:
this paper presents a novel nonlinearobserver scheme based on Self-recurrent wavelet neuralnetwork (SRWNN). The proposed network is combinedwith linear observer to estimate unavailable states ofnonlinear dynamic and handle uncertainty in systemparameters. SRWNN is a new structure of wavelet neuralnetwork that has a self-feedback loop in its hidden layersand this characteristic enhances the performance of theproposed observer to overcome severe nonlinearitybehavior in complex systems. Also, this network is tunedonline by backpropagation algorithm and its fast responsedemonstrates the capability of the method in the practicalcase study. The effectiveness of the observer is given insimulation results.

کلمات کلیدی:
nonlinear observer, wavelet neural network,learning rate, chaos

صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/496705/