Digital Design of Radial Basis Function Neural Network and Recurrent Neural Network

P Sahithya,M Arulmozhi,Nandini Praveen

doi:10.1109/wispnet45539.2019.9032759

Digital Design of Radial Basis Function Neural Network and Recurrent Neural Network

P Sahithya, M Arulmozhi + Show 1 more

https://doi.org/10.1109/wispnet45539.2019.9032759

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Publication Date: Mar 1, 2019

Citations: 5

#Radial Basis Function Neural Network #Term Memory Recurrent Neural Network + Show 8 more

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Abstract

Artificial Neural Network (ANN) are significantly used for fast and highly accurate computation in various fields. This paper addresses digital design for two Machine learning Algorithms, Radial Basis Function Neural Network (RBFNN) and the Long Short Term Memory Recurrent Neural Network (LSTM-RNN). The stochastic gradient descent (SGD) method is used as a learning algorithm for the former and Simultaneous Perturbation Stochastic Approximation (SPSA) method is used for the latter. The design are implemented in MATLAB simulink and further field tested for speech recognition.

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