A nonlinear system identification approach based on Fuzzy Wavelet Neural Network

Linhares, Leandro L. S.; Junior, José Medeiros de Araújo; Araújo, Fábio Meneghetti Ugulino de; Yoneyama, Takashi

doi:10.3233/ifs-141293

Cited by 5 publications

(9 citation statements)

References 31 publications

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“…Despite the advantages of correntropy over MSE, little effort has been reported towards the application of correntropy to identify nonlinear systems using neural networks [7,8]. The results presented in this work demonstrate that the FWNN architecture proposed in [29] is less sensitive to the presence of outliers and noise when it is trained using the MCC. In addition, this work also investigates the influence of the kernel size on the performance of the MCC in BP algorithm.…”

Section: Introductionmentioning

confidence: 83%

“…Therefore, the local models of such FWNN are solely represented by a set of wavelet functions, which differs from [5,6,24]. The results presented in [29] demonstrate that the modified FWNN structure maintains the generalization capability and also other important features presented by traditional FWNNs, despite the reduction in the complexity of these structures.…”

Section: Introductionmentioning

confidence: 93%

“…The aforementioned proposed structure is similar to the ones presented by Yilmaz and Oysal [5], Abiyev and Kaynak [6], and Lu [24]. However, the FWNN presented in [29] uses only wavelets in the consequent fuzzy rules. The wavelets in each node of the FWNN consequent layer are weighted by the activation signals of the fuzzy rules.…”

Section: Introductionmentioning

confidence: 95%

“…In [29], Linhares et al evaluate an alternative FWNN structure to identify the nonlinear dynamics of a multisection liquid tank. The aforementioned proposed structure is similar to the ones presented by Yilmaz and Oysal [5], Abiyev and Kaynak [6], and Lu [24].…”

Section: Introductionmentioning

confidence: 99%

“…In this work, the reliability of the FWNN recently proposed in [29] is evaluated when different percentages of outliers and noise contaminate the experimental data used to identify a nonlinear system. The aforementioned neural network is used to identify the dynamic relationship between the input and output of a multisection liquid tank.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification

Linhares

Fontes

Martins

et al. 2015

Mathematical Problems in Engineering

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Recent researches have demonstrated that the Fuzzy Wavelet Neural Networks (FWNNs) are an efficient tool to identify nonlinear systems. In these structures, features related to fuzzy logic, wavelet functions, and neural networks are combined in an architecture similar to the Adaptive Neurofuzzy Inference Systems (ANFIS). In practical applications, the experimental data set used in the identification task often contains unknown noise and outliers, which decrease the FWNN model reliability. In order to reduce the negative effects of these erroneous measurements, this work proposes the direct use of a similarity measure based on information theory in the FWNN learning procedure. The Mean Squared Error (MSE) cost function is replaced by the Maximum Correntropy Criterion (MCC) in the traditional error backpropagation (BP) algorithm. The input-output maps of a real nonlinear system studied in this work are identified from an experimental data set corrupted by different outliers rates and additive white Gaussian noise. The results demonstrate the advantages of the proposed cost function using the MCC as compared to the MSE. This work also investigates the influence of the kernel size on the performance of the MCC in the BP algorithm, since it is the only free parameter of correntropy.

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Section: Introductionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification

Linhares

Fontes

Martins

et al. 2015

Mathematical Problems in Engineering

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Source Diagnosis Method for Radiated EMI Noise Based on Wavelet and ICA Strategy

Zhang

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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In this paper, the independent component analysis (ICA) algorithm, combined with wavelet algorithm, are introduced to diagnose the radiation noise source, also to segregate various radiation noise components. The nonlinear time domain signals of supeposed rradiation noises are measured using multi-channel high-speed digital oscilloscope. Double frequency domain analysis of radiation noise are conducted with the proposed method. Furtherly, time-frequency joint graph of excessive radiation noise are accessed, according to overclock points. The radiation noise component is determined by employing signal separation algorithm. The effective diagnosis of common frequency radiation noise is thus realized, which contributes to reducing the radiated noise electronic device emitted.

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A Polak-Ribière-Polyak Conjugate Gradient-Based Neuro-Fuzzy Network and its Convergence

et al. 2018

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A nonlinear system identification approach based on Fuzzy Wavelet Neural Network

Cited by 5 publications

References 31 publications

Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification

Fuzzy Wavelet Neural Network Using a Correntropy Criterion for Nonlinear System Identification

Source Diagnosis Method for Radiated EMI Noise Based on Wavelet and ICA Strategy

A Polak-Ribière-Polyak Conjugate Gradient-Based Neuro-Fuzzy Network and its Convergence

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