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

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

Research output: Contribution to conferencePaperpeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages107-112
Number of pages6
Publication statusPublished - 1994 Dec 1
EventProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
Duration: 1994 Jun 271994 Jun 29

Other

OtherProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period94-06-2794-06-29

All Science Journal Classification (ASJC) codes

  • Software

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