Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks

Patra, J.C.; Kot, Alex C.

doi:10.1109/tsmcb.2002.1018769

Cited by 293 publications

(125 citation statements)

References 31 publications

Supporting

Mentioning

122

Contrasting

Unclassified

Order By: Relevance

“…Functional Link Neural Network is a class of HONNs created by Pao [7] and has been successfully used in many applications such as system identification [9][10][11][12][13][14], channel equalization [3], classification [15][16][17][18], pattern recognition [19,20] and prediction [21,22]. In this paper, we would discuss on the FLNN for the prediction task.…”

Section: Functional Link Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

Functional Link Neural Network – Artificial Bee Colony for Time Series Temperature Prediction

Hassim

Ghazali

2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Higher Order Neural Networks (HONNs) have emerged as an important tool for time series prediction and have been successfully applied in many engineering and scientific problems. One of the models in HONNs is a Functional Link Neural Network (FLNN) known to be conveniently used for function approximation and can be extended for pattern recognition with faster convergence rate and lesser computational load compared to ordinary feedforward network like the Multilayer Perceptron (MLP). In training the FLNN, the mostly used algorithm is the Backpropagation (BP) learning algorithm. However, one of the crucial problems with BP learning algorithm is that it can be easily gets trapped on local minima. This paper proposed an alternative learning scheme for the FLNN to be applied on temperature forecasting by using Artificial Bee Colony (ABC) optimization algorithm. The ABC adopted in this work is known to have good exploration and exploitation capabilities in searching optimal weight especially in numerical optimization problems. The result of the prediction made by FLNN-ABC is compared with the original FLNN architecture and toward the end we found that FLNN-ABC gives better result in predicting the next-day ahead prediction.

show abstract

Section: Functional Link Neural Networkmentioning

confidence: 99%

“…This model uses a tensor representation. Pao [7], Patra [10], Namatamee [24] has demonstrated that this architecture is very effective for classification task. …”

Section: Functional Link Neural Networkmentioning

confidence: 99%

Functional Link Neural Network – Artificial Bee Colony for Time Series Temperature Prediction

Hassim

Ghazali

2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

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%

“…A Chebyshev functional link artificial neural networks (CFLNN) has proposed by Patra et al [106] for non-linear dynamic system identification. This is obviously another improvement in this direction and also a source of inspiration to further validate this method in other application domain.…”

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

144

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

“…Simulation results showed that the performance identification using neural MIMO NARX model trained by back-propagation algorithm performed well. However, the drawback of the backpropagation algorithm applied in the studies [4], [5], [6] and [7] was that the convergence speed became slow, a large computation for learning and the cost function might lead to local minima.…”

Section: Introductionmentioning

confidence: 99%

Adaptive MIMO Neural Network Model Optimized by Differential Evolution Algorithm for Manipulator Kinematic System Identification

Ngoc¹,

Huy²

2014

2014 International Conference on Automatic Control Theory and Application

View full text Add to dashboard Cite

Abstract-In this paper, an adaptive MIMO neural network model is used for simultaneously modeling and identifying the forward kinematics of a 3-DOF robot manipulator. The nonlinear features of the robot manipulator kinematics system are modeled by an adaptive MIMO neural network model based on differential evolution algorithm. A differential evolution algorithm is used to optimally generate the appropriate neural weights so as to perfectly characterize the nonlinear features of the forward kinematics of a 3-DOF robot manipulator. This paper supports the performance of the proposed differential evolution algorithm in comparison with the conventional back-propagation algorithm. The results show that the proposed adaptive MIMO neural network model trained by the differential evolution algorithm for identifying the forward kinematics of a 3-DOF robot manipulator is successfully modeled and performed well.

show abstract

Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks

Cited by 293 publications

References 31 publications

Functional Link Neural Network – Artificial Bee Colony for Time Series Temperature Prediction

Functional Link Neural Network – Artificial Bee Colony for Time Series Temperature Prediction

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

Adaptive MIMO Neural Network Model Optimized by Differential Evolution Algorithm for Manipulator Kinematic System Identification

Contact Info

Product

Resources

About