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

被引：0
|
作者：
Sahithya, P. [1 ]
Arulmozhi, M. [1 ]
Praveen, Nandini [1 ]
机构：
[1] Rajalakshmi Engn Coll, Dept Elect & Elect, Chennai, Tamil Nadu, India
来源：
2019 INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, SIGNAL PROCESSING AND NETWORKING (WISPNET 2019): ADVANCING WIRELESS AND MOBILE COMMUNICATIONS TECHNOLOGIES FOR 2020 INFORMATION SOCIETY | 2019年
关键词：
RBFNN; LSTM-RNN; SGD; SPSA;
D O I：
10.1109/wispnet45539.2019.9032759
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
引用
收藏
页码：393 / 397
页数：5
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