Data driven modeling based on dynamic parsimonious fuzzy neural network

Pratama, Mahardhika; Er, Meng Joo; Li, Xiang; Oentaryo, Richard J.; Lughofer, Edwin; Arifin, Imam

doi:10.1016/j.neucom.2012.11.013

Cited by 61 publications

(22 citation statements)

References 53 publications

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“…Among them, 72 parameters are the updating targets (i.e. learning parameters) and 228 parameters will be needed for the calculation of targets' update amount by equations (45) to (50) and (52) to (57), and the others are the initial values of the learning parameters. Since each parameter required 4 bytes, total 1352 bytes are required in the memory.…”

Section: Resultsmentioning

confidence: 99%

Recurrent Fuzzy Neural Cerebellar Model Articulation Network Fault-Tolerant Control of Six-Phase Permanent Magnet Synchronous Motor Position Servo Drive

Lin

Sun

Yang

et al. 2016

IEEE Trans. Fuzzy Syst.

103

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A recurrent fuzzy neural cerebellar model articulation network (RFNCMAN) fault-tolerant control of a six-phase permanent magnet synchronous motor (PMSM) position servo drive is proposed in this study. First, the fault detection and operating decision method of the six-phase PMSM position servo drive is developed. Then, an ideal computed torque controller is designed for the tracking of the rotor position reference command. In general, it is impossible to design an ideal computed control law owing to the uncertainties of the six-phase PMSM position servo drive, which are difficult to know in advance for practical applications. Therefore, the RFNCMAN, which combined the merits of a recurrent fuzzy cerebellar model articulation network (RFCMAN) and a recurrent fuzzy neural network (RFNN), is proposed to estimate a nonlinear equation included in the ideal computed control law with a robust compensator designed to compensate the minimum reconstructed error. Furthermore, the adaptive learning algorithm for the online training of the RFNCMAN is derived using the Lyapunov stability to guarantee the closed-loop stability. Finally, the proposed RFNCMAN fault-tolerant control system is implemented in a 32-bit floating-point DSP. The effectiveness of the six-phase PMSM position servo drive using the proposed intelligent fault-tolerant control system is verified by some experimental results. Index Terms-Recurrent fuzzy neural cerebellar model articulation network (RFNCMAN), fault-tolerant control, six-phase permanent magnet synchronous motor (PMSM), Lyapunov stability, Taylor series expansion.

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Section: Resultsmentioning

confidence: 99%

Recurrent Fuzzy Neural Cerebellar Model Articulation Network Fault-Tolerant Control of Six-Phase Permanent Magnet Synchronous Motor Position Servo Drive

Lin

Sun

Yang

et al. 2016

IEEE Trans. Fuzzy Syst.

103

View full text Add to dashboard Cite

show abstract

“…gClass is endowed with a generalized fuzzy rule [21], in which the multivariate Gaussian function, which possesses a non-diagonal covariance matrix, is utilized as the rule antecedent. This rule premise is an attractive option for covering real-world data distributions because it can evolve non-axis parallel ellipsoids and is capable of conferring more exact coverage of data distributions.…”

Section: Cognitive Component Of Gclassmentioning

confidence: 99%

An incremental meta-cognitive-based scaffolding fuzzy neural network

Pratama

Anavatti

et al. 2016

Neurocomputing

Self Cite

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“…It is a benchmark problem often used in many literatures (Kim and Kim 1997;Rojas et al 2002Rojas et al , 2008Ma et al 2007;Assaad et al 2008;Pratama et al 2013Pratama et al , 2014a and is characterized by being non-periodic, non-convergent, non-stationary and non-linear, these characteristics make the modeling phase difficult. The following differential equation governs the Mackey-Glass time series:…”

Section: Mackey-glass Time Seriesmentioning

confidence: 99%

“…Accuracy is one of the most important factors in selecting a forecasting method; hence, a lot of Forecasting time series can be either long or short term which are commonly referred to as multi-step-ahead and one-step-ahead forecasting, respectively. The complex and dynamic nature of time series makes the modeling process challenging and thus non-linear forecasting techniques have become handy in enhancing forecast accuracy (Karray 2004;Pratama et al 2013). A variation of architecture paradigms in artificial neural networks (ANN) has been proposed to improve the forecasting performance of non-linear chaotic time series.…”

Section: Introductionmentioning

confidence: 99%

Scaled UKF–NARX hybrid model for multi-step-ahead forecasting of chaotic time series data

Abdulkadir

Yong

2015

Soft Comput

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Accurate forecasting is critically important in many time series applications. In this paper, we consider forecasting chaotic problems by proposing a hybrid model composed of scaled unscented Kalman filter with reduced sigma points and non-linear autoregressive network with exogenous inputs, trained using a modified Bayesian regulation backpropagation algorithm. To corroborate developments of the proposed hybrid model, real-life chaotic and simulated time series which are both non-linear in nature are applied to validate the proposed hybrid model. Experiment results show that the proposed hybrid model outperforms other forecasting models reported in the literature in forecasting of chaotic time series.

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Data driven modeling based on dynamic parsimonious fuzzy neural network

Cited by 61 publications

References 53 publications

Recurrent Fuzzy Neural Cerebellar Model Articulation Network Fault-Tolerant Control of Six-Phase Permanent Magnet Synchronous Motor Position Servo Drive

Recurrent Fuzzy Neural Cerebellar Model Articulation Network Fault-Tolerant Control of Six-Phase Permanent Magnet Synchronous Motor Position Servo Drive

An incremental meta-cognitive-based scaffolding fuzzy neural network

Scaled UKF–NARX hybrid model for multi-step-ahead forecasting of chaotic time series data

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