Neural Network Sliding Mode based Current Decoupled Control for Induction Motor Drive

Peng, Kang; Jin, Zhao

doi:10.3923/itj.2010.1440.1448

Cited by 13 publications

(4 citation statements)

References 5 publications

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“…Radial Basis Function Neural Network algorithm has been proven to be useful and beneficial in many industrial applications [21]. RBFNN is different from other networks, possessing several distinctive features due to its universal approximation ability, more compact topology and faster learning speed [22][23]. RBFNN typically has three layers: an input layer, a hidden layer, and an output layer.…”

Section: Radial Basis Function Neural Network (Rbfnn)mentioning

confidence: 99%

Radial basis function neural network for 2 satisfiability programming

Alzaeemi

Mansor

Kasihmuddin

et al. 2020

IJEECS

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<span>Radial Basis Function Neural Network (RBFNN) is very prominent in data processing. However, improving this technique is vital for the NN training process. This paper presents an integrated 2 Satisfiability in radial basis function neural network (RBFNN-2SAT). There are two different types of training in RBFNN, namely no-training technique and half-training technique. The performance of the solutions via Genetic Algorithm (GA) training was investigated by comparing the Radial Basis Function Neural Network No-Training Technique (RBFNN- 2SATNT) and Radial Basis Function Neural Network Half-Training Technique (RBFNN- 2SATHT). The comparison of both techniques was examined on 2 Satisfiability problem by using a C# software that was developed for this experiment. The performance of the RBFNN-2SATNT and RBFNN-2SATHT in performing 2SAT is discussed in terms of root mean squared error (RMSE), sum squared error (SSE), mean absolute percentage error (MAPE), mean absolute error (MAE), number of the hidden neurons and CPU time. Results obtained from a computer simulation showed that RBFNN-2SATHT outperformed RBFNN-2SATNT.</span>

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Section: Radial Basis Function Neural Network (Rbfnn)mentioning

confidence: 99%

Radial basis function neural network for 2 satisfiability programming

Alzaeemi

Mansor

Kasihmuddin

et al. 2020

IJEECS

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“…The similarity measure between fuzzy time series is need on pattern recognition for fuzzy time series forecasting model. In this study we represent a fuzzy predictive Solaimani, 2009;Dastorani et al, 2010) method for global prediction which learns an input output mapping (Ismail et al, 2010;Anton et al, 2009;Saleh et al, 2009;Al-Suhaibani et al, 2010;Effendi et al, 2010;Eslamian et al, 2009;Kang and Jin, 2010;Podeh et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Application of Neuro-Fuzzy Techniques for Solar Radiation

Rahoma¹,

Hassan²

2011

Journal of Computer Science

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Problem statement:The prediction is very useful in solar energy applications because it permits to estimate solar data for locations where measurements are not available. The developed artificial intelligence models predict the solar radiation time series more effectively compared to the conventional procedures based on the clearness index. Approach: The forecasting ability of some models could be further enhanced with the use of additional meteorological parameters. After having simulated many different structures of neural networks and trained using measurements as training data, the best structures were selected in order to evaluate their performance in relation with the performance of a neuro-fuzzy system. As the alternative system, ANFIS neuro-fuzzy system was considered, because it combines fuzzy logic and neural network techniques that are used in order to gain more efficiency. ANFIS is trained with the same data. Results: The comparison and the evaluation of both of the systems were done according to their predictions, using several error metrics. Fuzzy model was trained using data of daily solar radiation recorded on a horizontal surface in National Research Institute of Astronomy and Geophysics, Helwan, Egypt (NARIG) at ten years (1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000). Conclusion: The predicting conclusion indicated that the TS fuzzy model gave a good accuracy of approximately 96% and a root mean square error lower than 6%.

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“…RBFNN is different from the other networks, possessing several distinctive features. Owing to its universal approximation ability, more compact topology and faster learning speed, it has attracted much attention, and it has been widely applied in many science and engineering fields [3][4][5][6]. RBFNNs typically have three layers: an input layer, a hidden layer with a non-linear activation function and a linear output layer.…”

Section: Introductionmentioning

confidence: 99%