Development of System-Type Neural Network Architectures for Distributed Parameter Systems Using Algebraic Decomposition

提案编号：ECS-0501305提案标题：使用代数分解为分布参数系统开发系统型神经网络结构PI名称：Lee，光皮研究所：宾夕法尼亚州立大学智力优势：该项目将研究空间对称性的使用，以便训练神经网络分析分布在空间上的大型复杂系统。为了利用空间对称性，PI将使用代数分解来获得特定形式的经验数据的分析描述，称为半群形式，涉及系数向量和向量基集的乘积。此外，系数向量的每个分量和基本向量集合的每个分量可以被单独描述，从而允许每个分量被单独建模。将径向基函数神经网络和时滞递归神经网络相结合来实现这些原则。广泛的影响：该项目将使用监测锅炉炉膛内空间温度分布的挑战作为其初始试验台。对锅炉进行更准确的监测不仅在诊断和维护方面有用，而且在控制方面也是有用的；更有效的锅炉控制可以在一定程度上提高效率，大幅减少污染。还将考虑使用喷气发动机和柔性结构的试验台。锅炉、熔炉和喷气发动机是世界上NOx等主要污染物的主要来源。该项目还将推动宾夕法尼亚州立大学的跨学科教育。

Proposal Number: ECS-0501305Proposal Title: Development of System-Type Neural Network Architectures for Distributed Parameter Systems Using Algebraic Decomposition PI Name: Lee, KwangPI Institution: Penn State University Intellectual Merit: This project will investigate the use of symmetry properties over space in order to train neural networks to analyze large, complex systems distributed over space. To exploit spatial symmetry, the PI will use algebraic decomposition to obtain an analytic description of empirical data in a specific form, called the semigroup form, which involves the product of a coefficient vector and a basis set of vectors. Additionally, each component of the coefficient vector and each component of the basis set of vectors can be described individually, allowing each component to be modeled separately. A combination of RBF neural networks and time-lagged recurrent neural networks is used in implementing these principles.Broader Impacts: The project will use, as its initial testbed, the challenge of monitoring temperature distributions across space in a boiler furnace. More accurate monitoring of boilers may be useful not only in diagnostics and maintenance but also in control; more effective boiler control can improve efficiency somewhat and reduce pollution substantially. Testbeds involving jet engines and flexible structures will also be considered. Boilers, furnaces and jet engines account for a majority of major pollutants like NOx around the world. The project will also advance crossdisciplinary education at Penn State.