A neural network using single multiplicative spiking neuron for function approximation and classification

被引：0
|
作者：
Mishra, Deepak [1 ]
Yadav, Abhishek [1 ]
Dwivedi, Ashutosh [1 ]
Kalra, Prem K. [1 ]
机构：
[1] Indian Inst Technol, Dept Elect Engn, Kanpur 208016, Uttar Pradesh, India
来源：
2006 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORK PROCEEDINGS, VOLS 1-10 | 2006年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, learning algorithm for a single multiplicative spiking neuron (MSN) is proposed and tested for various applications where a multilayer perceptron (MLP) neural network is conventionally used. It is found that a single MSN is sufficient for the applications that require a number of neurons in different hidden layers of a conventional neural network. Several benchmark and real-life problems of classification and function-approximation are illustrated. It has been observed that the inclusion of few more biological phenomenon in artificial neural networks can make them more prevailing.
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收藏
页码：396 / +
页数：3
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