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Evolutionary Multiobjective Neural Network Models Identification: Evolving Task-Optimised Models

机译：进化多目标神经网络模型识别：不断发展的任务优化模型

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In the system identification context, neural networks are black-box models, meaning that both their parameters and structure need to be determined from data. Their identification is often done iteratively in an ad-hoc fashion focusing the first aspect. Frequently the selection of inputs, model structure, and model order are underlooked subjects by practitioners, because the number of possibilities is commonly huge, thus leaving the designer at the hands of the curse of dimensionality. Moreover, the design criteria may include multiple conflicting objectives, which gives to the model identification problem a multiobjective combinatorial optimisation character. Evolutionary multiobjective optimisation algorithms are particularly well suited to address this problem because they can evolve optimised model structures that meet pre-specified design criteria in acceptable computing time. In this article the subject is reviewed, the authors present their approach to the problem in the context of identifying neural network models for time-series prediction and for classification purposes, and two application case studies are described, one in each of these fields.
机译：在系统识别环境中，神经网络是黑盒模型，这意味着它们的参数和结构都需要根据数据确定。通常以针对第一个方面的临时方式来反复进行其标识。通常，从业人员会忽略输入，模型结构和模型顺序的选择，因为可能性通常很大，从而使设计人员无法掌握维数的诅咒。此外，设计标准可能包括多个相互矛盾的目标，这给模型识别问题带来了多目标组合优化特征。演化多目标优化算法特别适合解决此问题，因为它们可以在可接受的计算时间内演化出满足预定设计标准的优化模型结构。在本文中，该主题进行了回顾，作者在识别用于时序预测和分类目的的神经网络模型的背景下提出了解决该问题的方法，并描述了两个应用案例研究，每个领域中一个。

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