An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network

Leng, Gang; McGinnity, T.M.; Prasad, Girijesh

doi:10.1016/j.fss.2004.03.001

Cited by 248 publications

(127 citation statements)

References 41 publications

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“…GDFNN is based on ellipsoidal basis function (EBF) and is functionally equivalent to a Takagi-Sugeno-kang fuzzy system (Wu et al, 2001;Leng et al, 2005). Fig.…”

Section: Model Algorithmsmentioning

confidence: 99%

Dynamic Fuzzy Neural-Network Model for Estimating Heavy Metal Concentration in Rice Using Spectral Indices and Environmental Parameters

Liu¹,

Liu²

2011

Artificial Neural Networks - Application

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“…GDFNN is based on ellipsoidal basis function (EBF) and is functionally equivalent to a Takagi-Sugeno-kang fuzzy system (Wu et al, 2001;Leng et al, 2005). Fig.…”

Section: Model Algorithmsmentioning

confidence: 99%

Dynamic Fuzzy Neural-Network Model for Estimating Heavy Metal Concentration in Rice Using Spectral Indices and Environmental Parameters

Liu¹,

Liu²

2011

Artificial Neural Networks - Application

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“…As we can see from the resulting figures, the trained network obtains a high precision and the RMSE is also confined to a very small value. Comparative studies of the proposed algorithm with other published works, such as the OLS (Chen et al, 1991), GDFNN (Wu et al, 2001) and SOFNN (Leng et al, 2005) are shown in Table 4. APE trn % and APE chk % are APE of training data and testing data, respectively.…”

Section: Static Function Approximationmentioning

confidence: 99%

Development of Fuzzy Neural Networks: Current Framework and Trends

Liu¹,

Er²

2010

New Trends in Technologies: Control, Management, Computational Intelligence and Network Systems

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“…Depending on the Euclidean distance of the fuzzy rules to the newest datum, some EFNSs, i.e., dynamic evolving neuro-fuzzy inference system (DENFIS) [16], self-constructing neuro-fuzzy inference network [17] are developed to recruit the rules. In addition to the distance criterion, the learning error at each instant is applied as a criterion for evolving the system structure in some EFNSs [7], [8]. But these EFNSs are prone to outliers.…”

Section: Introductionmentioning

confidence: 99%

Correntropy-Based Evolving Fuzzy Neural System

Bao

Rong

Angelov

et al. 2018

IEEE Trans. Fuzzy Syst.

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Abstract-In this paper, a correntropy-based evolving fuzzy neural system (correntropy-EFNS) is proposed for approximation of nonlinear systems. Different from the commonly used meansquare error criterion, correntropy has a strong outliers rejection ability through capturing the higher moments of the error distribution. Considering the merits of correntropy, this paper brings contributions to build EFNS based on the correntropy concept to achieve a more stable evolution of the rule base and update of the rule parameters instead of the commonly used meansquare error criterion. The correntropy-EFNS (CEFNS) begins with an empty rule base and all rules are evolved online based on the correntropy criterion. The consequent part parameters are tuned based on the maximum correntropy criterion where the correntropy is used as the cost function so as to improve the nonGaussian noise rejection ability. The steady-state convergence performance of the CEFNS is studied through the calculation of the steady-state excess mean square error (EMSE) in two cases: i) Gaussian noise; and ii) non-Gaussian noise. Finally, the CEFNS is validated through a benchmark system identification problem, a Mackey-Glass time series prediction problem as well as five other real-world benchmark regression problems under both noise-free and noisy conditions. Compared with other evolving fuzzy neural systems, the simulation results show that the proposed CEFNS produces better approximation accuracy using the least number of rules and training time and also owns superior non-Gaussian noise handling capability.

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An approach for on-line extraction of fuzzy rules using a self-organising fuzzy neural network

Cited by 248 publications

References 41 publications

Dynamic Fuzzy Neural-Network Model for Estimating Heavy Metal Concentration in Rice Using Spectral Indices and Environmental Parameters

Dynamic Fuzzy Neural-Network Model for Estimating Heavy Metal Concentration in Rice Using Spectral Indices and Environmental Parameters

Development of Fuzzy Neural Networks: Current Framework and Trends

Correntropy-Based Evolving Fuzzy Neural System

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