A comprehensive review for industrial applicability of artificial neural networks

Abstract

Abstract-This paper presents a comprehensive review of the industrial applications of artificial neural networks (ANNs), in the last 12 years. Common questions that arise to practitioners and control engineers while deciding how to use NNs for specific industrial tasks are answered. Workable issues regarding implementation details, training and performance evaluation of such algorithms are also discussed, based on a judiciously chronological organization of topologies and training methods effectively used in the past years. The most popular ANN topologies and training methods are listed and briefly discussed, as a reference to the application engineer. Finally, ANN industrial applications are grouped and tabulated by their main functions and what they actually performed on the referenced papers. The authors prepared this paper bearing in mind that an organized and normalized review would be suitable to help industrial managing and operational personnel decide which kind of ANN topology and training method would be adequate for their specific problems.

Numeric Character Recognition System for Chilean License Plates in semicontrolled scenarios

“…The selected classification method is that of Support Vector Machines (SVM), and its selection was based on its efficacy recorded in the literature and on a laboratory test comparing the SVM and the Artificial Neural Networks (ANN) methods, which among the systems based on learning are also a verified effective strategy [12,13] and used in OCR [14].…”

View-based teaching/playback for robotic manipulation

“…This is useful to switch robot motion (for example, from approaching to grasping) in some cases. The weights of the neural network can be computed from image data and motion data in demonstrations by backpropagation with momentum (BPM) [16].…”

Operation of Compressor and Electronic Expansion Valve via Different Controllers

“…Since ANNs define, in general, a nonlinear algebraic function, they can cope with nonlinearities inherent in control systems possessing complex dynamics. As in the general ANN literature, the mostly widely used ANN model in identification and control is the Multi Layer Perceptron (MLP) due to its function approximation capability and the existence of an efficient learning algorithm (Ahmed, 2000;Lightbody & Irwin, 1995;Meireles et al, 2003;Noriega & Wang, 1998;Omidvar & Elliott, 1997). MLP is a multilayer, algebraic neural network of neurons, called as perceptrons, which are multi-input, single-output functional units taking firstly a weighted sum of their inputs and then pass it through a sigmoidal nonlinearity to produce its output shown in Fig.…”