کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
156256 456926 2011 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Optimization of nonlinear process based on sequential extreme learning machine
موضوعات مرتبط
مهندسی و علوم پایه مهندسی شیمی مهندسی شیمی (عمومی)
پیش نمایش صفحه اول مقاله
Optimization of nonlinear process based on sequential extreme learning machine
چکیده انگلیسی

In this paper, a new approach to the optimal control with constraints is proposed to achieve a desired end product quality for nonlinear processes based on new kernel extreme learning machine (KELM). The contributions of the paper are as follows: (1) In existing ILC algorithm, the model was built only between manipulated input variables U and output variables Y without considering the state variables. However, the states variables Xstate are important in the industrial processes, which are usually constrained. In this paper, the variables are divided into state variables Xstate, manipulated input variables U and output Y in the process of modeling. Then ΔU can be obtained by batch-to-batch iterative learning control separately. Kernel algorithm is added to ELM. (2) Constraints of state variables Xstate and the input variables U are considered in the current version. PSO is used to solve the optimization problem. (3) Kernel trick is introduced to improve accuracy of ELM modeling. New KELM algorithm is proposed in the current version. The input trajectory for the next batch is accommodated by searching for the optimal value through the error feedback at a minimum cost. The particle swarm optimization algorithm is used to search for the optimal value based on the iterative learning control (ILC). The proposed approach has been shown to be effective and feasible by applying bulk polymerization of the styrene batch process and fused magnesium furnace.


► The states variables stateX are important in the industrial processes, which are usually constrained.
► The variables are divided into state variable stateX, manipulated input variables U and output Y in the process of modeling.
► Then U can be obtained by batch-to-batch iterative learning control separately. Kernel algorithm is added to ELM.
► Constraints of state variables stateX and the input variables U are considered in the current version.
► PSO is used to solve the optimization problem; Kernel trick is introduced.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Chemical Engineering Science - Volume 66, Issue 20, 15 October 2011, Pages 4702–4710
نویسندگان
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