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首页> 外文期刊>IEEE transactions on industrial informatics >Finite Gaussian Mixture Model Based Multimodeling for Nonlinear Distributed Parameter Systems
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Finite Gaussian Mixture Model Based Multimodeling for Nonlinear Distributed Parameter Systems

机译：基于有限高斯混合模型的非线性分布式参数系统的多模型

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摘要

Complex nonlinear distributed parameter systems (DPSs) widely exist in real industrial thermal processes. Modeling of such systems often leads to the following challenges: strong nonlinearities, time-varying dynamics, and large operating range with multiple working points. Therefore, traditional single spatiotemporal model will become ill suited. Motivated by the idea of multimodeling, integration of finite Gaussian mixture model (FGMM) and principle component regression (PCR) based multiple spatiotemporal modeling is proposed in this paper for complex nonlinear DPSs. The main idea of the proposed method can be summarized as the following three parts: FGMM-based operating space separation, Karhunen-Loeve based local spatiotemporal modeling, and PCR-based local spatiotemporal models integration. To evaluate the generalization bound of the proposed method, the Rademacher complexity is also developed here theoretically. Since multimodeling can reduce the nonlinear complexity, the proposed model has strong ability to track and handle the complex nonlinear dynamics. Simulations on a two-dimensional curing thermal process demonstrated the superior model performance of the proposed model.
机译：复杂的非线性分布式参数系统（DPS）广泛存在于实际工业热处理中。这些系统的建模通常导致以下挑战：强烈的非线性，时变动力学和具有多个工作点的大型操作范围。因此，传统的单一时尚模型将变得不满。通过多模思想的激励，在本文中提出了用于复杂的非线性DPS的有限高斯混合物模型（FGMM）和基于原理成分回归（PCR）的多重时滞的多次血流模拟。所提出的方法的主要思想可以总结为以下三个部分：基于FGMM的操作空间分离，基于karhunen-loeve的本地时空建模，基于PCR的本地时空模型集成。为了评估所提出的方法的泛化界定，在理论上也在这里在这里开发出变质的复杂性。由于多模块可以降低非线性复杂性，因此提出的模型具有很强的跟踪和处理复杂的非线性动力学的能力。二维固化热处理的模拟表明了所提出的模型的卓越模型性能。

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