RETRACTED: On-line system identification of complex systems using Chebyshev neural networks

Purwar, Shubhi; Kar, Indra Narayan; Jha, A. N.

doi:10.1016/j.asoc.2005.08.001

Cited by 104 publications

(41 citation statements)

References 15 publications

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“…Since the first two terms in Equation (30) are satisfying the condition in Equation (9), (10), next four terms are satisfying the LMI in Equation (11). Therefore, we conclude that the system in Equation (4) is stable with control law Equation (5) and LMI in Equation (11).…”

Section: Theoremmentioning

confidence: 69%

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Robust Sliding Mode Control for Nonlinear Discrete-Time Delayed Systems Based on Neural Network

Goyal¹,

Deolia²,

Sharma³

2015

ICA

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This paper presents a robust sliding mode controller for a class of unknown nonlinear discretetime systems in the presence of fixed time delay. A neural-network approximation and the Lyapunov-Krasovskii functional theory into the sliding-mode technique is used and a neural-network based sliding mode control scheme is proposed. Because of the novality of Chebyshev Neural Networks (CNNs), that it requires much less computation time as compare to multi layer neural network (MLNN), is preferred to approximate the unknown system functions. By means of linear matrix inequalities, a sufficient condition is derived to ensure the asymptotic stability such that the sliding mode dynamics is restricted to the defined sliding surface. The proposed sliding mode control technique guarantees the system state trajectory to the designed sliding surface. Finally, simulation results illustrate the main characteristics and performance of the proposed approach.

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Section: Theoremmentioning

confidence: 69%

“…Based on the approximation property of CNN [27]- [30], there exist ideal weights w , so that the function ( ) g x to be approximated can be represented as…”

Section: Cnn Structurementioning

confidence: 99%

Robust Sliding Mode Control for Nonlinear Discrete-Time Delayed Systems Based on Neural Network

Goyal¹,

Deolia²,

Sharma³

2015

ICA

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show abstract

“…Chandramathi et al [31] utilized fuzzy approach to estimate cell loss probability and Support Vector Machine (SVM) method was used to video streams classification by Awada et al [33]. Especially, other online hybrid approaches were used to system identification [35],network traffic classification [29,36] and data mining of data streams [32,34],which promote the online network traffic classification and controlling research greatly.…”

Section: Related Research Literaturementioning

confidence: 99%

Online hybrid traffic classifier for Peer-to-Peer systems based on network processors

Chen

Yang

Chen

et al. 2009

Applied Soft Computing

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“…Purwar et al [34] have proposed a Chebyshev functional link neural network for system identification of unknown dynamic nonlinearly discrete-time systems. Weng et al [35] have proposed a reduced decision feedback Chebyshev functional link artificial neural networks (RDF-CFLANN) for channel equalization.…”

Section: Functional Link Neural Networkmentioning

confidence: 99%

A Novel Learning Scheme for Chebyshev Functional Link Neural Networks

Dehuri

2011

Advances in Artificial Neural Systems

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A hybrid learning scheme (ePSO-BP) to train Chebyshev Functional Link Neural Network (CFLNN) for classification is presented. The proposed method is referred as hybrid CFLNN (HCFLNN). The HCFLNN is a type of feed-forward neural networks which have the ability to transform the nonlinear input space into higher dimensional-space where linear separability is possible. Moreover, the proposed HCFLNN combines the best attribute of particle swarm optimization (PSO), back propagation learning (BP learning), and functional link neural networks (FLNNs). The proposed method eliminates the need of hidden layer by expanding the input patterns using Chebyshev orthogonal polynomials. We have shown its effectiveness of classifying the unknown pattern using the publicly available datasets obtained from UCI repository. The computational results are then compared with functional link neural network (FLNN) with a generic basis functions, PSO-based FLNN, and EFLN. From the comparative study, we observed that the performance of the HCFLNN outperforms FLNN, PSO-based FLNN, and EFLN in terms of classification accuracy.

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RETRACTED: On-line system identification of complex systems using Chebyshev neural networks

Cited by 104 publications

References 15 publications

Robust Sliding Mode Control for Nonlinear Discrete-Time Delayed Systems Based on Neural Network

Robust Sliding Mode Control for Nonlinear Discrete-Time Delayed Systems Based on Neural Network

Online hybrid traffic classifier for Peer-to-Peer systems based on network processors

A Novel Learning Scheme for Chebyshev Functional Link Neural Networks

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