Logo image
A novel multicriteria optimization algorithm for the structure determination of multilayer feedforward neural networks
Journal article   Peer reviewed

A novel multicriteria optimization algorithm for the structure determination of multilayer feedforward neural networks

K. Kottathra and Y. Attikiouzel
Journal of Network and Computer Applications, Vol.19(2), pp.135-147
1996
url
Link to Published Version *Subscription may be requiredView

Abstract

We propose in this paper a novel prescriptive solution to decide the optimum number of neurons in the hidden-layer of multilayer feedforward neural networks. Our approach uses the unconstrained mixed integer nonlinear multicriteria optimization technique. We validate the algorithm using numerical examples. We extend the above results using fuzzy reasoning and constrained optimization techniques to solve the cross-validation problem in a more effective way than the traditional back propagation algorithm. The main features of our approach are that it is a formal method and it draws results from many fields to combine them for the solution of this NP-complete problem.

Details

Metrics

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Citation topics
4 Electrical Engineering, Electronics & Computer Science
4.61 Artificial Intelligence & Machine Learning
4.61.493 Neural-Fuzzy Integration
Web Of Science research areas
Computer Science, Hardware & Architecture
Computer Science, Interdisciplinary Applications
Computer Science, Software Engineering
ESI research areas
Computer Science
Logo image