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17 results on '"Liang Yanming"'

3. Using reservoir computing to solve FPK equations for stochastic dynamical systems under Gaussian or Non-Gaussian excitation.

Author

Liang, Yanming, Guo, Yongfeng, and Lin, Zifei

Subjects
*WHITE noise, *STOCHASTIC systems, *RANDOM noise theory, *REGULARIZATION parameter, *DYNAMICAL systems
Abstract

This paper presents a new approach that uses the Reservoir Computing Algorithm to solve Fokker-Planck-Kolmogorov (FPK) equation excited by both Gaussian white noise and non-Gaussian noise. Unlike typical numerical methods, this methodology does not necessitate spatial reconstruction or numerical supplementation. The novelty of this paper lies in the modifications made to the conventional Reservoir Computing algorithm. We altered the approach for calculating values of the input weight matrix and incorporated autoregressive techniques in the reservoir layer. In addition, we applied data normalization to the training data before training the algorithm to avoid a zero solution. The efficacy of this approach was verified through multiple arithmetic examples, showcasing its practicality and efficiency in solving FPK equations. Moreover, the Reservoir Computing-FPK algorithm is capable of solving high-dimensional and fractional-order FPK equations with a smaller training set than earlier algorithms. Finally, we analyzed how values of the input weight matrix and regularization parameter affected the performance of the algorithm. The findings suggest that the careful selection of hyperparameters can greatly improve the performance of the Reservoir Computing algorithm. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Power Plant Boiler Air Preheater Hot Spots Detection System Based on Least Square Support Vector Machines

Author

Han, Liu, Ding, Liu, Yu, Jin, Li, Qi, Liang, Yanming, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yin, Fu-Liang, editor, Wang, Jun, editor, and Guo, Chengan, editor

Published
2004
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8. A Non-intrusive Load Decomposition Method Based on Data Modeling

Author

Xiaojun Chang, Liang Yanming, Haiyang Zhao, Na Zhu, Liu Qian, and Chen Chunliang

Subjects
Support vector machine, Correlation coefficient, Artificial neural network, Mean squared error, Electrical load, Computer science, Control theory, Decomposition method (constraint satisfaction), AC power, Data modeling
Abstract

Most of the existing non-intrusive power load decomposition systems use the active power and reactive power of the electric load as the load characteristics. In the environment with a large load type and quantity, the decomposed effect is relatively poor, the accuracy is low, and the load characteristics are seriously lost. Aiming at this problem, this paper proposes a non-intrusive power load decomposition method using full current waveform as the characteristic of power load system. Firstly, in the data-driven framework, using the time series data of load voltage and current, the current model of each load is obtained by training RBF neural network based on the parameter optimization of support vector machine (SVM), and the load model library is formed. Then, genetic algorithm is used to find the combination of load model which is most similar to the actual current. The optimal combination of load model is the type and quantity of the actual load. In order to make the similarity evaluation more accurate, this paper establishes the weighted value of root mean square error, correlation coefficient and correlation entropy as the evaluated basis of similarity. The experimental results show that the load model library established by the decomposition method of this paper has better generalization ability and lower error, and the accuracy of decomposition load is 99.0%, which can effectively realize load decomposition.

Published
2021
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17. Flatness detection research based on the string vibration method

Author

Li Qi, Liang Yanming, and Jia Meng

Subjects
Artificial neural network, business.industry, Flatness (systems theory), Feature extraction, Fast Fourier transform, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, Vibrating string, Wavelet packet decomposition, Vibration, Wavelet, Artificial intelligence, business, ComputingMethodologies_COMPUTERGRAPHICS, Mathematics
Abstract

In order to better identify the flatness pattern, this paper proposes a non-contacted flatness detection called string vibration method. This method first use the principle of string vibration to design the detection scheme, and then use the wavelet packet decomposition to extract the effective shape characteristics, finally use the RBF neural network with strong classification ability to come true the shape pattern classification. By building shape detection platform, getting a lot of experimental data, the relationship between wavelet node vector and shape pattern was established. The plate-shaped pattern recognition rate reached 90%. The method improves the shortcomings of FFT in vibration signal feature extraction, the idea of string vibration method provides a new solution for the flatness detection.

Published
2015
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