In this paper, an innovative Genetic Algorithms (GA)-based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact infor...In this paper, an innovative Genetic Algorithms (GA)-based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact information (inexact non-linear operation programming). GAINLP was developed based on a GA-based inexact quadratic solving method. The Genetic Algorithm Solver of the Global Optimization Toolbox (GASGOT) developed by MATLABTM was adopted as the implementation environment of this study. GAINLP was applied to a municipality solid waste management case. The results from different scenarios indicated that the proposed GA-based heuristic optimization approach was able to generate a solution for a complicated nonlinear problem, which also involved uncertainty.展开更多
This paper presents the application of a neural network rule extraction algorithm,called the piecewise linear artificial neural network or PWL-ANN algorithm,on a carbon capture process system dataset.The objective of ...This paper presents the application of a neural network rule extraction algorithm,called the piecewise linear artificial neural network or PWL-ANN algorithm,on a carbon capture process system dataset.The objective of the application is to enhance understanding of the intricate relationships among the key process parameters.The algorithm extracts rules in the form of multiple linear regression equations by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network(ANN).The PWL-ANN algorithm overcomes the weaknesses of the statistical regression approach,in which accuracies of the generated predictive models are often not satisfactory,and the opaqueness of the ANN models.The results show that the generated PWL-ANN models have accuracies that are as high as the originally trained ANN models of the four datasets of the carbon capture process system.An analysis of the extracted rules and the magnitude of the coefficients in the equations revealed that the three most significant parameters of the CO_(2) production rate are the steam flow rate through reboiler,reboiler pressure,and the CO_(2) concentration in the flue gas.展开更多
文摘In this paper, an innovative Genetic Algorithms (GA)-based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact information (inexact non-linear operation programming). GAINLP was developed based on a GA-based inexact quadratic solving method. The Genetic Algorithm Solver of the Global Optimization Toolbox (GASGOT) developed by MATLABTM was adopted as the implementation environment of this study. GAINLP was applied to a municipality solid waste management case. The results from different scenarios indicated that the proposed GA-based heuristic optimization approach was able to generate a solution for a complicated nonlinear problem, which also involved uncertainty.
基金The first author is grateful for the scholarships and generous support from the Faculty of Graduate Studies and Research,University of Regina and from the Canada Research Chair Program.
文摘This paper presents the application of a neural network rule extraction algorithm,called the piecewise linear artificial neural network or PWL-ANN algorithm,on a carbon capture process system dataset.The objective of the application is to enhance understanding of the intricate relationships among the key process parameters.The algorithm extracts rules in the form of multiple linear regression equations by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network(ANN).The PWL-ANN algorithm overcomes the weaknesses of the statistical regression approach,in which accuracies of the generated predictive models are often not satisfactory,and the opaqueness of the ANN models.The results show that the generated PWL-ANN models have accuracies that are as high as the originally trained ANN models of the four datasets of the carbon capture process system.An analysis of the extracted rules and the magnitude of the coefficients in the equations revealed that the three most significant parameters of the CO_(2) production rate are the steam flow rate through reboiler,reboiler pressure,and the CO_(2) concentration in the flue gas.
