Multi-objective genetic programming approach for robust modeling of complex manufacturing processes having probabilistic uncertainty in experimental data
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- 作者：A. Jamali ; E. Khaleghi ; I. Gholaminezhad…
- 关键词：Robust modeling ; Genetic programming ; Multi ; objective ; MCS
- 刊名：Journal of Intelligent Manufacturing
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：28
- 期：1
- 页码：149-163
- 全文大小：
- 刊物类别：Business and Economics
- 刊物主题：Production; Manufacturing, Machines, Tools; Control, Robotics, Mechatronics;
- 出版者：Springer US
- ISSN：1572-8145
- 卷排序：28
文摘
In this paper, a multi-objective uniform-diversity genetic programming (MUGP) algorithm deployed for robust Pareto modeling and prediction of complex nonlinear processes using some input-output data table. The uncertainties included in measured data are considered to obtain more robust models. The considered benchmarks are an explosive cutting and forming processes, in which the nonlinear behavior between the input and output of processes are detected using MUGP. For both case studies, a multi-objective modeling and prediction procedure firstly performed using deterministic data. Secondly, the same identification procedure carried out using probabilistic uncertainty in the experimental input-output data. The objective functions considered are namely, training error, prediction error and number of tree nodes (complexity of models) in the deterministic approach. Accordingly, the mean and standard deviation of training error and prediction error are considered in robust Pareto modeling and prediction of such processes. In this way, Pareto front of such modeling and prediction is first obtained for both explosive cutting and forming processes with deterministic data. Such Pareto front is then obtained using experimental input-output-data having probabilistic uncertainty in input parameters through a Monte Carlo simulation (MCS) approach. In addition, it has been shown that for both cases, the trade-off models obtained from deterministic data have significant biases when tested on data with probabilistic uncertainty. Finally, the obtained results of such multi-objective robust model identification show promising results in terms of compensating uncertainty in the experimental input-output-data.