Tuning of expert systems for structural damage detection through differential evolutionary algorithms

Postprint (author's final draft

Automatically searching near-optimal artificial neural networks

Abstract. The idea of automatically searching neural networks that learn faster and generalize better is becoming increasingly widespread. In this paper, we present a new method for searching near-optimal artificial neural networks that include initial weights, transfer functions, architectures and learning rules that are specially tailored to a given problem. Experimental results have shown that the method is able to produce compact, efficient networks with satisfactory generalization power and shorter training times.