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

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 nonuniformity in their network structures and learning algorithms creates practical problems. Thus...

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Bibliographic Details
Main Authors: Shengtun Li, Yiwei Chen, Leiss, E.L.
Format: Conference Proceeding
Language:English
Subjects:
Backpropagation algorithms
Computer networks
Feedforward neural networks
Function approximation
Multi-layer neural network
Multilayer perceptrons
Neural networks
Neurons
Nonhomogeneous media
Pattern classification
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Summary: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 nonuniformity 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. Multilayer 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.< >
DOI:10.1109/ICNN.1994.374147