Identification of distributed parameter systems: A neural net based approach

Centralized Distributed Parameter Bioprocess Identification and I-Term Control Using Recurrent Neural Network Model

“…The FFNN and the RNN have been applied for Distributed Parameter Systems (DPS) identification and control too. In (Deng & Li, 2003;Deng et al 2005;Gonzalez et al, 1998), an intelligent modeling approach is proposed for Distributed Parameter Systems (DPS). In ( Gonzalez et al, 1998), it is presented a new methodology for the identification of DPS, based on NN architectures, motivated by standard numerical discretization techniques used for the solution of Partial Differential Equations (PDE).…”

“…In (Deng & Li, 2003;Deng et al 2005;Gonzalez et al, 1998), an intelligent modeling approach is proposed for Distributed Parameter Systems (DPS). In ( Gonzalez et al, 1998), it is presented a new methodology for the identification of DPS, based on NN architectures, motivated by standard numerical discretization techniques used for the solution of Partial Differential Equations (PDE). In (Padhi et al, 2001), an attempt is made to use the philosophy of the NN adaptive-critic design to the optimal control of distributed parameter systems.…”

Distributed Parameter Bioprocess Plant Identification and I-Term Control Using Decentralized Fuzzy-Neural Multi-Models

“…Because of their approximation and learning capabilities, the ANNs have been widely employed to dynamic process modeling, identification, prediction and control, (Boskovic & Narendra, 1995;Haykin, 1999;Bulsari & Palosaari, 1993;Deng & Li, 2003;Deng et al, 2005;Gonzalez-Garcia et al, 1998;Padhi & Balakrishnan, 2003;Padhi et al, 2001;Ray,1989). Many applications have been done for identification and control of biotechnological plants too, (Padhi et al, 2001).…”

Robust nonlinear H∞ control of hyperbolic distributed parameter systems

“…Among other nonlinear empirical modeling methodologies, neural networks have found great acceptance in control engineering. A neural network approach for the identification of DPSs has been proposed by Gonzá les-García, Rico-Martínez, and Kevrekidis (1998). More recently, Padhi, Balakrishnan, and Randolph (2001) used two sets of neural networks to model a parabolic DPS and a discrete dynamic programming format for the synthesis of an optimal controller.…”