Class of neural networks that consists of a feed-forward fully connected network with an input layer of neurons, one or more hidden layers and an output layer. The output value is obtained through the sequence of activation functions defined in each hidden layer. Usually, in this kind of network, the supervised learning process is the backpropagation algorithm
Published in Chapter:
Evolutionary Approaches for ANNs Design
Antonia Azzini (University of Milan, Italy) and Andrea G.B. Tettamanzi (University of Milan, Italy)
Copyright: © 2009
|Pages: 6
DOI: 10.4018/978-1-59904-849-9.ch088
Abstract
Artificial neural networks (ANNs) are computational models, loosely inspired by biological neural networks, consisting of interconnected groups of artificial neurons which process information using a connectionist approach. ANNs are widely applied to problems like pattern recognition, classification, and time series analysis. The success of an ANN application usually requires a high number of experiments. Moreover, several parameters of an ANN can affect the accuracy of solutions. A particular type of evolving system, namely neuro-genetic systems, have become a very important research topic in ANN design. They make up the so-called Evolutionary Artificial Neural Networks (EANNs), i.e., biologicallyinspired computational models that use evolutionary algorithms (EAs) in conjunction with ANNs. Evolutionary algorithms and state-of-the-art design of EANN were introduced first in the milestone survey by Xin Yao (1999), and, more recently, by Abraham (2004), by Cantu-Paz and Kamath (2005), and then by Castellani (2006). The aim of this article is to present the main evolutionary techniques used to optimize the ANN design, providing a description of the topics related to neural network design and corresponding issues, and then, some of the most recent developments of EANNs found in the literature. Finally a brief summary is given, with a few concluding remarks.