Nonlinear System Identification and Control: Rop Scheme and Neural Networks

2018 ◽  
Author(s):  
Reddy B Raja Genal
Keyword(s):  
Neural Networks ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
And Control

NONLINEAR SYSTEM IDENTIFICATION AND CONTROL BASED ON MODULAR NEURAL NETWORKS

2011 ◽  
Vol 21 (04) ◽  
pp. 319-334 ◽  
Author(s):  
GHEORGHE PUSCASU ◽  
BOGDAN CODRES
Keyword(s):  
Neural Networks ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Nonlinear Controller ◽  
New Approach ◽  
Modular Neural Networks ◽  
Neural Controllers ◽  
And Control ◽  
Operating Points

A new approach for nonlinear system identification and control based on modular neural networks (MNN) is proposed in this paper. The computational complexity of neural identification can be greatly reduced if the whole system is decomposed into several subsystems. This is obtained using a partitioning algorithm. Each local nonlinear model is associated with a nonlinear controller. These are also implemented by neural networks. The switching between the neural controllers is done by a dynamical switcher, also implemented by neural networks, that tracks the different operating points. The proposed multiple modelling and control strategy has been successfully tested on simulated laboratory scale liquid-level system.

scholarly journals A Nonlinear System Identification Method Based on Adaptive Neural Network

2021 ◽  
Vol 28 (2) ◽  
pp. 111-123
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Generic Model ◽  
Control Engineering ◽  
The Neural Network ◽  
Adaptive Pso ◽  
And Control ◽  
Scientific Engineering

Nonlinear system identification (NSI) is of great significance to modern scientific engineering and control engineering. Despite their identification ability, the existing analysis methods for nonlinear systems have several limitations. The neural network (NN) can overcome some of these limitations in NSI, but fail to achieve desirable accuracy or training speed. This paper puts forward an NSI method based on adaptive NN, with the aim to further improve the convergence speed and accuracy of NN-based NSI. Specifically, a generic model-based nonlinear system identifier was constructed, which integrates the error feedback and correction of predictive control with the generic model theory. Next, the radial basis function (RBF) NN was optimized by adaptive particle swarm optimization (PSO), and used to build an NSI model. The effectiveness and speed of our model were verified through experiments. The research results provide a reference for applying the adaptive PSO-optimized RBFNN in other fields.

scholarly journals Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Lan Wang ◽  
Yu Cheng ◽  
Jinglu Hu ◽  
Jinling Liang ◽  
Abdullah M. Dobaie
Keyword(s):  
System Identification ◽  
Nonlinear System ◽  
Radial Basis Function Network ◽  
Nonlinear System Identification ◽  
Support Vector ◽  
Identification Procedure ◽  
Identification Methods ◽  
Nonlinear Parameter Identification ◽  
And Control ◽  
Function Network

Quasi-linear autoregressive with exogenous inputs (Quasi-ARX) models have received considerable attention for their usefulness in nonlinear system identification and control. In this paper, identification methods of quasi-ARX type models are reviewed and categorized in three main groups, and a two-step learning approach is proposed as an extension of the parameter-classified methods to identify the quasi-ARX radial basis function network (RBFN) model. Firstly, a clustering method is utilized to provide statistical properties of the dataset for determining the parameters nonlinear to the model, which are interpreted meaningfully in the sense of interpolation parameters of a local linear model. Secondly, support vector regression is used to estimate the parameters linear to the model; meanwhile, an explicit kernel mapping is given in terms of the nonlinear parameter identification procedure, in which the model is transformed from the nonlinear-in-nature to the linear-in-parameter. Numerical and real cases are carried out finally to demonstrate the effectiveness and generalization ability of the proposed method.

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