Many industrial processes belong to distributed parameter systems (DPS) that have strong
spatial–temporal dynamics. Modeling of DPS is difficult but essential to simulation, control …

The purpose of this volume is to provide a brief review of the previous work on model
reduction and identifi cation of distributed parameter systems (DPS), and develop new …

The least-squares support vector machine (LS-SVM) has been successfully used to model
nonlinear time dynamics; however, it does not have the capability to handle space …

In this paper the basic properties of the singular value decomposition (SVD) for integral
equation models of distributed parameter systems (DPS) are presented in the context of …

Accurate solutions of distributed parameter systems may be represented as the sum of an
infinite series. Control design however, requires low-order models primarily due to …

Global principal component analysis (PCA) has been successfully introduced for modeling
distributed parameter systems (DPSs). In spite of the merits, this method is not feasible due …

This paper presents a deep learning-based model reduction method for distributed
parameter systems (DPSs). The proposed method includes three phases. In phase I …

In this work the radial basis function neural network architecture is used to model the
dynamics of Distributed Parameter Systems (DPSs). Two pure data driving schemes which …

Hyperbolic distributed parameter systems (DPS) represent a large number of industrial
processes with spatially nonuniform operating variable profiles. Research has been …

The selection of an appropriate descriptive system and modeling framework to capture
system dynamics and support process control applications is a fundamental problem in the …