1. An optimization methodology for neural network weights and architectures.
- Author
-
Ludermir TB, Yamazaki A, and Zanchettin C
- Subjects
- Algorithms, Information Storage and Retrieval methods, Neural Networks, Computer, Pattern Recognition, Automated methods, Signal Processing, Computer-Assisted
- Abstract
This paper introduces a methodology for neural network global optimization. The aim is the simultaneous optimization of multilayer perceptron (MLP) network weights and architectures, in order to generate topologies with few connections and high classification performance for any data sets. The approach combines the advantages of simulated annealing, tabu search and the backpropagation training algorithm in order to generate an automatic process for producing networks with high classification performance and low complexity. Experimental results obtained with four classification problems and one prediction problem has shown to be better than those obtained by the most commonly used optimization techniques.
- Published
- 2006
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