Neural network architectures for industrial applications

Abstract

An overview of the present industrial scenario with regard to the application of neural network approaches is reported. A brief summary of practical applications is presented. Neural network architectures capable of resolving various types of industrial problems are also illustrated. The first application presented deals with the problem of digit recognition. The proposed architecture is composed of different multilayer networks trained by back-propagation, organized in a hierarchical structure. Such a structure as opposed to a single network structure, allows the avoidance of typical errors occurring on comparison of certain couples of digits. The second application deals with a texture-based segmentation of an image. In order to solve this problem, a structure composed of two different networks is proposed. The first network, a simplified BCS model, is a “feature extractor”; the second network, Learning Vector Quantization model, is able to classify different textures on the basis of the parameters extracted by BCS. The third application deals with a fault-diagnosis system for the process state monitoring of complex industrial plants. An innovative approach is presented. The proposed model analyzes process parameters and predicts possible malfunctions on the basis of self-organizing networks.