A Proposal for Parameter-Free Surrogate Building Algorithm Using Artificial Neural Networks

S. Miriyala, K. Mitra
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Abstract

Surrogate models, capable of emulating the robust first principle based models, facilitate the online implementation of computationally expensive industrial process optimization. However, the heuristic estimation of parameters governing the surrogate building often renders them erroneous or under-trained. Current work aims at presenting a novel parameter free surrogate building approach, specifically focusing on Artificial Neural Networks. The proposed algorithm implements Sobol sampling plan and intelligently designs the configuration of network with simultaneous estimation of optimal transfer function and training sample size to prevent overfitting and enabling maximum prediction accuracy. A novel Sample Size Determination algorithm based on a potential concept of hypercube sampling technique adds to the speed of surrogate building algorithm, thereby assuring faster convergence. Surrogates models for a highly nonlinear industrial sintering process constructed using the novel algorithm resulted in 7 times faster optimization.
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一种基于人工神经网络的无参数代理构建算法
代理模型,能够模拟基于鲁棒第一性原理的模型,促进在线实现计算昂贵的工业过程优化。然而,对控制替代建筑的参数的启发式估计经常使它们出错或训练不足。目前的工作旨在提出一种新的无参数代理构建方法,特别关注人工神经网络。该算法实现Sobol采样计划,智能设计网络配置,同时估计最优传递函数和训练样本大小,防止过拟合,实现最大的预测精度。一种基于超立方体采样技术潜在概念的样本大小确定算法提高了代理构建算法的速度,从而保证了更快的收敛速度。利用该算法构建的工业烧结过程的模拟模型将优化速度提高了7倍。
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