Abstract




 
   

IJE TRANSACTIONS A: Basics Vol. 30, No. 10 (October 2017) 1510-1516    Article in Press

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  WAVELET NEURAL NETWORK WITH RANDOM WAVELET FUNCTION PARAMETERS
 
H. Bazoobandi
 
( Received: November 08, 2016 – Accepted in Revised Form: August 25, 2017 )
 
 

Abstract    The training algorithm of Wavelet Neural Networks (WNN) is a bottleneck which impacts on the accuracy of the final WNN model. Several methods have been proposed for training the WNNs. From the perspective of our research, most of these algorithms are iterative and need to adjust all the parameters of WNN. This paper proposes a one-step learning method which changes the weights between hidden layer and output layer of the network; meanwhile, the wavelet function parameters are randomly assigned and kept fixed during the training process. Besides the simplicity and speed of the proposed one-step algorithm, the experimental results verify the performance of the proposed method in terms of final model accuracy and computational time.

 

Keywords    Wavelet Neural Network; training algorithm; Moore-Penrose inverse; random parameter values

 

چکیده    الگوریتم های آموزش در شبکه های عصبی فازی موجک گلوگاه موثر بر دقت مدل نهایی است. تاکنون روشهای مختلفی برای آموزش شبکه های عصبی موجک پیشنهاد شده است. از دیدگاه تحقیق پیش رو، بیشتر این الگوریتم ها مبتنی بر تکرار هستند و بایستی همه پارامترهای شبکه عصبی موجک را تنظیم نمایند. در این مقاله روشی تک مرحله ای برای تنظیم وزن های بین لایه مخفی و لایه خروجی پیشنهاد شده است. همچنین پارامترهای تابع موجک بصورت تصادفی مقداردهی شده است و تا پایان فرایند آموزش ثابت در نظر گرفته شده است. علاوه بر سادگی و سرعت روش پیشنهادی تک مرحله ای، نتایج تجربی کارایی روش پیشنهادی را براساس دقت مدل نهایی و هزینه محاسبات تائید می نماید.

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