Application of ANN in control systems Research Papers (original) (raw)

An Artificial Neural Network (ANN) is a mathematical model inspired by the biological behaviour of neurons and by the structure of the brain, which is used to solve a wide range of problems. A network is reached by connecting several neurons with a specific architecture (Hopfield Networks, Kohonen Networks, Perceptron, and so on), in which neurons learn through a process of self-organization. 1 During the learning process of the ANN, when a data is introduced into the network, just the neuron that has a positive activity inside the proximity will be activated at the exit stage. 1, 2 There is a wide variety of ANN models, which depend on the objective by which they were created, as well as the practical problem they solve. 3 During these last decades, several inconveniences about ANN applications have been found in the literature, 4, 5, 6, 7 as the performance of the ANN neither have much relation to the amount of acquired information, nor the way the algorithm detects the information, 8 but also with issues like: the selection of the network model, the variables to incorporate on it and the pre-processing of the information that will form the training group. 9 The ANN depends essentially on the exact information of the system under study, and the methods of training that must be used, as the algorithm of ANN during their training have the ability of identifying unnecessary data. 10 Besides, the routines of training require a huge amount of data to make sure that the results can be statistically precise 11, but a lot of algorithms have been proposed to improve the performance of the ANN. 12, 13 Therefore, it can be seen that it is important to know more about the precision and the sturdiness of the ANN. 14 Given the wide range of applications of the ANN towards different areas of science (financial and economic modelling, market profiles - customer, medical applications, management of knowledge and discovering of data, optimization of industrial processes and quality control, among others) generate excellent results. 2, 15, 16 Given the great versatility of the applications and the uses of the ANN, it is possible that can be used in system for which not necessarily has an optimum performance, they may be redundant or inefficient. So, that would be advisable assess the own characteristics of the nature of the problem to solve, in order to assess the application of an ANN.