A channel equalizer using reduced decision feedback Chebyshev functional link artificial neural networks

Weng, Wan-De; Yang, Che-Shih; Lin, Rui-Chang

doi:10.1016/j.ins.2007.01.006

Cited by 48 publications

(19 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Functional Link Neural Networkmentioning

confidence: 99%

A Novel Learning Scheme for Chebyshev Functional Link Neural Networks

Dehuri

2011

Advances in Artificial Neural Systems

View full text Add to dashboard Cite

show abstract

“…v rf = Ω f − v adf ). Then the fast-loop control law (10) can be designed as (12) where M ad is the control adjustment in the presence of uncertainties. M r denotes a robust control (RC) item used for offsetting the error between Ω f and v adf .…”

Section: The Pffln-based Adaptive Ngpcmentioning

confidence: 99%

“…It has been applied to pattern recognition, image processing and channel equalization [8][9][10] etc, whereas little investigation has been reported about the FLN-based controller design. From the view point of hardware implementation, an FLN has simpler structure and higher computation speed than an MLP [10]. Furthermore, no other parameters but weights need training for the FLN.…”

Section: Introductionmentioning

confidence: 99%

Adaptive functional link network control of near-space vehicles with dynamical uncertainties

Du¹,

Wu²,

Jiang³

et al. 2010

J. of Syst. Eng. Electron.

View full text Add to dashboard Cite

The control law design for a near-space hypersonic vehicle (NHV) is highly challenging due to its inherent nonlinearity, plant uncertainties and sensitivity to disturbances. This paper presents a novel functional link network (FLN) control method for an NHV with dynamical thrust and parameter uncertainties. The approach devises a new partially-feedback-functional-link-network (PFFLN) adaptive law and combines it with the nonlinear generalized predictive control (NGPC) algorithm. The PFFLN is employed for approximating uncertainties in flight. Its weights are online tuned based on Lyapunov stability theorem for the first time. The learning process does not need any offline training phase. Additionally, a robust controller with an adaptive gain is designed to offset the approximation error. Finally, simulation results show a satisfactory performance for the NHV attitude tracking, and also illustrate the controller's robustness.

show abstract

“…Combining the characteristics of the FLNN and Chebyshev orthogonal polynomial resulted in a new FLNN named Chebyshev FLNN (CFLNN) [105,106,124]. The basic principles of this method is same as previously discussed model, but the basis function considered here are Chebyshev polynomials.…”

Section: Functional Link Neural Network With Chebyshev Polynomialmentioning

confidence: 99%

“…Purwar et al [104] has proposed a Chebyshev functional link neural network (SyiFLNN) for system identification of unknown dynamic non-linear discrete time systems. Weng et al [105] has proposed a reduced decision feed-back Chebyshev functional link artificial neural networks (RDF-CFLNN) for channel equalization.…”

Section: Functional Link Neural Network: a Road Mapmentioning

confidence: 99%

A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN

Dehuri

Cho

2009

Neural Comput & Applic

143

View full text Add to dashboard Cite

Functional link neural network (FLNN) is a class of higher order neural networks (HONs) and have gained extensive popularity in recent years. FLNN have been successfully used in many applications such as system identification, channel equalization, short-term electricload forecasting, and some of the tasks of data mining. The goals of this paper are to: (1) provide readers who are novice to this area with a basis of understanding FLNN and a comprehensive survey, while offering specialists an updated picture of the depth and breadth of the theory and applications; (2) present a new hybrid learning scheme for Chebyshev functional link neural network (CFLNN); and (3) suggest possible remedies and guidelines for practical applications in data mining. We then validate the proposed learning scheme for CFLNN in classification by an extensive simulation study. Comprehensive performance comparisons with a number of existing methods are presented.

show abstract

A channel equalizer using reduced decision feedback Chebyshev functional link artificial neural networks

Cited by 48 publications

References 10 publications

A Novel Learning Scheme for Chebyshev Functional Link Neural Networks

A Novel Learning Scheme for Chebyshev Functional Link Neural Networks

Adaptive functional link network control of near-space vehicles with dynamical uncertainties

A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN

Contact Info

Product

Resources

About