DISSERTAÇÃO DE MESTRADO - PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA DA COMPUTAÇÃO
URI Permanente para esta coleçãohttps://tedebc-teste.ufma.br/handle/tede/1314
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Automação e Contrôle
Ciência da Computação
Sistemas de Energia Elétrica
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Item COEFICIENTES DE CORRELAÇÃO COMO MÉTRICA DE AVALIAÇÃO DAS ESTRATÉGIAS DE CONTROLE INTELIGENTE FEL E MNFEL.(Universidade Federal do Maranhão, 2018-06-26) SANTOS, Alex Newman Veloso dos; RIBEIRO, Paulo Rogério de Almeida; 016.320.583-32; http://lattes.cnpq.br/0035213619257246; OLIVEIRA, Alexandre César Muniz de; 288.350.993-68; OLIVEIRA, Alexandre César Muniz de; 288.350.933-68; http://lattes.cnpq.br/5225588855422632Control systems have been largely used in many fields such as industrial plants, robotics, medicine and so on. Therefore, new techniques are frequently proposed to enhance these systems. Feedback-Error-Learning (FEL) is an intelligent control strategy which applies a neural network alongside a conventional controller, as an example the ProportionalIntegral-Derivative (PID) that is the most used on the industry. The enhanced control is achieved in FEL by the acquisition of the inverse model or the non-linearity compensation. Moreover, Multi-Network-Feedback-Error-Learning (MNFEL), which is based on FEL, uses multiple neural networks that can lead to a better control. FEL and MNFEL works assume that enhanced controls are achieved by adding neural networks, however, there are few works account for the network’s degree of contribution to the control system. A previous research proposed a metric based on Pearson product-moment correlation coefficient (PC). However, this metric assumes working conditions that may not be met in control systems. This works aims to propose two approaches based on Spearman Coefficient (SC) and PC. The evaluation methodology is comprised of two phases. The first phase, placed before the intelligent control strategy insertion, determines the expected SC behavior based on the initial analysis of the correlation between the squared error and the conventional controller. The second phase evaluates the coefficient behavior during the neural network training. Two industrial plants were used in this work: Burner group of a Pelletizing plant and Cooling Coil plant. The results shown: i) the previous work approach using PC may lead to precipitated conclusions about the system in analysis; ii) the proposed approach using SC demonstrated – in both plants – the neural networks’ degree of contribution while enhancing the control; iii) the SC – during the networks’ training – can preview that those networks will or not significantly enhance the control, i.e. indicating that those networks may not contribute in the control system. Thus, the proposed approach, which uses PC and SC, may calculate the contribution of the neural networks during the improvment of the control system with FEL and MNFEL.