Nonlinear systems identification using dynamic multi-time scale neural networks

Han, Xuan; Xie, Wenfang; Fu, Zhi-Jun; Luo, Weidong

doi:10.1016/j.neucom.2011.06.007

Cited by 50 publications

(19 citation statements)

References 17 publications

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“…In this paper, we will extend our prior result [16][18] of single-layer dynamic neural networks with multi-time scales to the multilayer case. To the best of our knowledge, the on-line update laws for the dynamic neural networks' hidden layers, output layers have been proposed.…”

Section: Introductionmentioning

confidence: 79%

“…So, the nonlinear system identification process turns out to be one of the central parts in constructing successful tracking controller. In our previous work [16] [18], the adaptive identification and control for dynamic systems with different time scales via multiple time scales neural networks have been established. However, they deal with the simple structure of dynamic neural networks including only single output layer.…”

Section: Introductionmentioning

confidence: 99%

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Indirect adaptive control of nonlinear system via dynamic multilayer neural networks with multi-time scales

Liu

Xie

2013

2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP)

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This paper deals with the adaptive nonlinear identification and trajectory tracking via dynamic multilayer neural network with different time-scales. By means of a Lyapunov-like analysis we determine stability conditions for the identification. Based on the identification results, we design a sliding mode controller for the nonlinear system to track the trajectory of a reference model. The main contributions of the paper are: First, we extend our prior results of single-layer dynamic neural networks with multi-time scales to the multilayer case. Second, the e-modification in standard use in adaptive control is introduced in the on-line update laws to guarantee bounded weights, bounded identification and tracking errors. Simulation results are presented confirming the validity of the above approach.

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Section: Introductionmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Indirect adaptive control of nonlinear system via dynamic multilayer neural networks with multi-time scales

Liu

Xie

2013

2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP)

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“…Theorem 1 is proved in [28] by defining the Lyapunov function as V 1,I (5) and using Theorem 1 in [32].…”

Section: A Identification Algorithmmentioning

confidence: 99%

Nonlinear Systems Identification and Control Via Dynamic Multitime Scales Neural Networks

Xie

Han

et al. 2013

IEEE Trans. Neural Netw. Learning Syst.

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This paper deals with the adaptive nonlinear identification and trajectory tracking via dynamic multilayer neural network (NN) with different timescales. Two NN identifiers are proposed for nonlinear systems identification via dynamic NNs with different timescales including both fast and slow phenomenon. The first NN identifier uses the output signals from the actual system for the system identification. In the second NN identifier, all the output signals from nonlinear system are replaced with the state variables of the NNs. The online identification algorithms for both NN identifier parameters are proposed using Lyapunov function and singularly perturbed techniques. With the identified NN models, two indirect adaptive NN controllers for the nonlinear systems containing slow and fast dynamic processes are developed. For both developed adaptive NN controllers, the trajectory errors are analyzed and the stability of the systems is proved. Simulation results show that the controller based on the second identifier has better performance than that of the first identifier.

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“…During this process, an appropriate identification algorithm largely determines the model precision. Although modern system identification methods based on common artificial intelligence theories such as the neural network algorithm, genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm have solved some identification problems [4,5] , drawbacks such as the small sample estimation, high-dimensional optimization problems, algorithm structure selection and so on still cannot be resolved which finally hinder their practical engineering application.…”

Section: Introductionmentioning

confidence: 99%

Prestressed Tension Hydraulic System Identification Using Linear Programming Support Vector Regression

Li¹,

Lü²,

Xu³

et al. 2017

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Nonlinear systems identification using dynamic multi-time scale neural networks

Cited by 50 publications

References 17 publications

Indirect adaptive control of nonlinear system via dynamic multilayer neural networks with multi-time scales

Indirect adaptive control of nonlinear system via dynamic multilayer neural networks with multi-time scales

Nonlinear Systems Identification and Control Via Dynamic Multitime Scales Neural Networks

Prestressed Tension Hydraulic System Identification Using Linear Programming Support Vector Regression

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