TY - GEN
T1 - Parameter optimization in complex industrial process control based on improved fuzzy-GA
AU - Wang, Bin
AU - Wang, Sun An
AU - Du, Hai Feng
AU - Qu, Ping Ge
PY - 2003
Y1 - 2003
N2 - In the modern complex industrial process, the control system generally has characteristics of large inertia, nonlinearity and time-varying, and its control requirements are diverse and uncertain, so it is difficult to smoothly turn the control parameters. To solve the problem, fuzzy evaluating approach is used to improve the SGA (simple genetic algorithms), and a fuzzy fitness function is designed to divide those control requirements into many evaluating factors with different weights. The individual in CA (genetic algorithms) is control parameters. The fitness of the individual reflects the fuzzy evaluating degree of control result, and shows the approximate degree of control result and ideal situation. In the paper, we use the fuzzy-GA to optimize the control parameters of temperature controller in tower type fermenter. Experiments and simulations show that control indexes have been improved and this approach can successfully solve parameter optimization problem in complex industrial process.
AB - In the modern complex industrial process, the control system generally has characteristics of large inertia, nonlinearity and time-varying, and its control requirements are diverse and uncertain, so it is difficult to smoothly turn the control parameters. To solve the problem, fuzzy evaluating approach is used to improve the SGA (simple genetic algorithms), and a fuzzy fitness function is designed to divide those control requirements into many evaluating factors with different weights. The individual in CA (genetic algorithms) is control parameters. The fitness of the individual reflects the fuzzy evaluating degree of control result, and shows the approximate degree of control result and ideal situation. In the paper, we use the fuzzy-GA to optimize the control parameters of temperature controller in tower type fermenter. Experiments and simulations show that control indexes have been improved and this approach can successfully solve parameter optimization problem in complex industrial process.
KW - Complex industrial process
KW - Fuzzy genetic algorithms
KW - Parameter optimization
UR - https://www.scopus.com/pages/publications/1542375176
M3 - 会议稿件
AN - SCOPUS:1542375176
SN - 0780378652
T3 - International Conference on Machine Learning and Cybernetics
SP - 2512
EP - 2515
BT - International Conference on Machine Learning and Cybernetics
T2 - 2003 International Conference on Machine Learning and Cybernetics
Y2 - 2 November 2003 through 5 November 2003
ER -