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|Title:||Increasing The Speed of Convergence of an Artificial Neural Network based ARMA Coefficients Determination Technique|
|Authors:||Aibinu, Abiodun M.|
Salami, Momoh-Jimoh E.
Shafie, Amir A.
Najeeb, Athaur R.
|Keywords:||Adaptive Learning rate|
|Publisher:||World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering|
|Citation:||Aibinu, A. M., Salami, M. J., Shafie, A. A., & Najeeb, A. R. (2008). Increasing The Speed of Convergence of an Artificial Neural Network based ARMA Coefficients Determination Technique. World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 2(6), 1839-1845.|
|Abstract:||In this paper, novel techniques in increasing the accuracy and speed of convergence of a Feed forward Back propagation Artificial Neural Network (FFBPNN) with polynomial activation function reported in literature is presented. These technique was subsequently used to determine the coefficients of Autoregressive Moving Average (ARMA) and Autoregressive (AR) system. The results obtained by introducing sequential and batch method of weight initialization, batch method of weight and coefficient update, adaptive momentum and learning rate technique gives more accurate result and significant reduction in convergence time when compared t the traditional method of back propagation algorithm, thereby making FFBPNN an appropriate technique for online ARMA coefficient determination.|
|Appears in Collections:||Research Articles|
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