Introduction to artificial neural systems
Introduction to artificial neural systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Genetic Algorithm for Multiobjective Robust Design
Applied Intelligence
Expert Systems with Applications: An International Journal
A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms
Expert Systems with Applications: An International Journal
Genetic evolution of the topology and weight distribution of neural networks
IEEE Transactions on Neural Networks
A transaction pattern analysis system based on neural network
Expert Systems with Applications: An International Journal
An efficient hybrid Taguchi-genetic algorithm for protein folding simulation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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Improving the performance of neural networks is of considerable importance. Although previous studies have investigated how to design the optimal neural network, the heuristic algorithms developed to support the optimization process contain flaws. These heuristic algorithms do not perform efficiently and they require prior expert knowledge. This study commences by employing an orthogonal array using the Taguchi method to calibrate the factor levels of a heuristic algorithm and to estimate the percent contribution from various individual factors. Subsequently, the calibrated heuristic algorithm is used to optimize a back-propagation network (BPN). Changing the level of each individual factor systematically and then analyzing the main and interactive effects of the design factors by using the analysis of variance (ANOVA) leads to the optimal heuristic algorithm factor levels with regard to experimental cost. The proposed optimization procedure is demonstrated on the classification problems using the University of California's Department of Information and Computer Science (ICS) server. The results indicate that the quality of the solution from the proposed approach is superior to that from a non-calibrated conventional design.