Feedforward Neural Networks - Architecture Optimization and Knowledge Extraction
Zuzana Reitermanová · 2008
Abstract. Feedforward neural networks represent a well-established computational model, which can be used for solving complex tasks requiring large data sets. When dealing with this kind of problems, the main requirements will be the speed of the learning process and the ability to generalize well the extracted knowledge. To satisfy these demands, adequate initial parameters of the model – like number of layers and number of neurons – are essential. For a given problem, especially the architecture of the model impacts its generalization capabilities. Optimal network architecture speeds up recall and may also improve efficiency of further retraining. Some of the techniques also enable or simplify further knowledge extraction. The main goal of this article is to provide a survey of some existing techniques that optimize architecture of BP-networks.