GMDP: a novel unified neuron model for multilayer feedforward neural networks

Sheng-Tun Li, Yiwei Chen, Ernst L. Leiss

研究成果: Paper

3 引文 斯高帕斯(Scopus)

摘要

A variety of neural models, especially higher-order networks, are known to be computationally powerful for complex applications. While they have advantages over traditional multilayer perceptrons, the non-uniformity in their network structures and learning algorithms creates practical problems. Thus there is a need for a framework that unifies these various models. This paper presents a novel neuron model, called generalized multi-dendrite product (GMDP) unit. Multi-layer feedforward neural networks with GMDP units are shown to be capable of realizing higher-order neural networks. The standard backpropagation learning rule is extended to this neural network. Simulation results show that single layer GMDP networks provide an efficient model for solving general problems on function approximation and pattern classification.

原文English
頁面107-112
頁數6
出版狀態Published - 1994 十二月 1
事件Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
持續時間: 1994 六月 271994 六月 29

Other

OtherProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
城市Orlando, FL, USA
期間94-06-2794-06-29

All Science Journal Classification (ASJC) codes

  • Software

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  • 引用此

    Li, S-T., Chen, Y., & Leiss, E. L. (1994). GMDP: a novel unified neuron model for multilayer feedforward neural networks. 107-112. 論文發表於 Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7), Orlando, FL, USA, .