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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >A new multiobjective genetic programming approach using compromise distance ranking for automated design of nonlinear system design
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A new multiobjective genetic programming approach using compromise distance ranking for automated design of nonlinear system design

机译：一种采用折衷距离排序的多目标遗传规划新方法，用于非线性系统设计的自动化设计

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摘要

This paper presents a newmultiobjective genetic programming (MOGP) approach, to realize an all-in-one automatic nonlinear system design (NSD). The nonlinear system design is here modeled as a multiobjective optimization problem (MOP) to solve parameter estimation, structure optimization and feature selection simultaneously. The novel MOGP method is then proposed to rank individuals according to the 'compromise distance' between them, which has the benefit of combining decision making for NSD with the optimization process to get the final compromise solution in a single process. The effectiveness of the proposed learning approach for nonlinear system design is verified through experiments on the classical nonlinear autoregressive with extra inputs (NARX) system by comparison with classical aggregating method and a Pareto-based method for MOP. Finally, experimental results demonstrate the proposed approach is available to explore the unknown structure of nonlinear systems as well as the features and parameters with high accuracy and efficiency.
机译：本文提出了一种新的多目标遗传规划（MOGP）方法，以实现多合一的自动非线性系统设计（NSD）。在此，将非线性系统设计建模为多目标优化问题（MOP），以同时解决参数估计，结构优化和特征选择的问题。然后提出了新颖的MOGP方法，根据个体之间的“折衷距离”对个体进行排名，这具有将NSD决策与优化过程相结合的优势，从而在单个过程中获得最终的折衷解决方案。通过与经典聚合方法和基于Pareto的MOP方法进行比较，通过对经典非线性自回归附加输入（NARX）系统进行实验，验证了所提出的学习方法在非线性系统设计中的有效性。最后，实验结果证明了该方法可用于探索非线性系统的未知结构以及具有高准确度和效率的特征和参数。

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