Nonlinear identification using a B-spline neural network and chaotic immune approaches

Coelho, Leandro dos Santos; Pessôa, Marcelo Wicthoff

doi:10.1016/j.ymssp.2009.01.013

Cited by 32 publications

(9 citation statements)

References 42 publications

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“…Additionally, the MSE obtained for the estimation phase is 0.0147 in this case, which is better than the one reported in [72]. With respect to R 2 , for both estimation and validation datasets our method presents better results than the ones reported in [73]. In FR simulation, other references did not provide this metric but our approach achieved a reasonable approximation as the R 2 is greater than 0.9.…”

Section: Methodsmentioning

confidence: 51%

“…Kim et al [71] yðt À 1Þ uðt À 4Þ 0.129 --yðt À 2Þ Du and Zhang [72] yðt À 4Þ uðt À 4Þ 0.06 --uðt À 5Þ Coelho and Pessôa [73] yðt Table 3 shows the values of MSE, R 2 coefficients in the estimation and validation phases as well as in free-run simulation, for the proposed method and other results found in the literature. A short description about the methods compared follows.…”

Section: Methodsmentioning

confidence: 84%

“…With respect to Takagi-Sugeno fuzzy models, in [70] they are applied for system identification, evolved by Evolutionary Algorithms in [71] and with a new encoding scheme in [72]. In [73], chaotic immune approaches are applied to train B-splines neural networks.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Cascaded evolutionary algorithm for nonlinear system identification based on correlation functions and radial basis functions neural networks

Ayala

Coelho

2016

Mechanical Systems and Signal Processing

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Section: Methodsmentioning

confidence: 51%

Section: Methodsmentioning

confidence: 84%

See 1 more Smart Citation

Cascaded evolutionary algorithm for nonlinear system identification based on correlation functions and radial basis functions neural networks

Ayala

Coelho

2016

Mechanical Systems and Signal Processing

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“…For its excellent local features, the B-spline function can be used as an activation function of neural network. The B-spline neural networks (BSNNs) can be divided into two groups: one group is based on the B-spline basis function [29,30,31,32] and the other group is based on the B-spline curve [33,34,35,36,37,38]. …”

Section: Introductionmentioning

confidence: 99%

“…If the positions of the control points are changed, the shapes of B-spline curves are also changed. In some studies, the weights are considered as control points [38]. Through the training of weights, the BSNN can then become adaptive.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive B-Spline Neural Network and Its Application in Terminal Sliding Mode Control for a Mobile Satcom Antenna Inertially Stabilized Platform

Zhang

Zhao

Guo

et al. 2017

Sensors

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The mobile satcom antenna (MSA) enables a moving vehicle to communicate with a geostationary Earth orbit satellite. To realize continuous communication, the MSA should be aligned with the satellite in both sight and polarization all the time. Because of coupling effects, unknown disturbances, sensor noises and unmodeled dynamics existing in the system, the control system should have a strong adaptability. The significant features of terminal sliding mode control method are robustness and finite time convergence, but the robustness is related to the large switching control gain which is determined by uncertain issues and can lead to chattering phenomena. Neural networks can reduce the chattering and approximate nonlinear issues. In this work, a novel B-spline curve-based B-spline neural network (BSNN) is developed. The improved BSNN has the capability of shape changing and self-adaption. In addition, the output of the proposed BSNN is applied to approximate the nonlinear function in the system. The results of simulations and experiments are also compared with those of PID method, non-singularity fast terminal sliding mode (NFTSM) control and radial basis function (RBF) neural network-based NFTSM. It is shown that the proposed method has the best performance, with reliable control precision.

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Modeling nonlinear systems using the tensor network B‐spline and the multi‐innovation identification theory

Wang

Tang

Deng

2022

Intl J Robust & Nonlinear

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The nonlinear autoregressive exogenous (NARX) model shows a strong expression capacity for nonlinear systems since these systems have limited information about their structures. However, it is difficult to model the NARX system with a relatively high dimension by using the popular polynomial NARX and the neural network efficiently. This article uses the tensor network B-spline (TNBS) to model the NARX system, whose representation of the multivariate B-spline weight tensor can alleviate the computation and store burden for processing high-dimensional systems. Furthermore, applying the multi-innovation identification theory and the hierarchical identification principle, the recursive algorithm by combining the l 2 -norm is proposed to the NARX system with Gaussian noise. Because of the local adjustability of the B-spline curve, the TNBS can fit nonlinear systems with strong nonlinearity by the meaning of setting a proper degree and knots number. Finally, a numerical experiment is given to demonstrate the effectiveness of the proposed algorithm.

show abstract

Nonlinear identification using a B-spline neural network and chaotic immune approaches

Cited by 32 publications

References 42 publications

Cascaded evolutionary algorithm for nonlinear system identification based on correlation functions and radial basis functions neural networks

Cascaded evolutionary algorithm for nonlinear system identification based on correlation functions and radial basis functions neural networks

An Adaptive B-Spline Neural Network and Its Application in Terminal Sliding Mode Control for a Mobile Satcom Antenna Inertially Stabilized Platform

Modeling nonlinear systems using the tensor network B‐spline and the multi‐innovation identification theory

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