SPRBF-ABLS: a novel attention-based broad learning systems with sparse polynomial-based radial basis function neural networks

Jing Wang1, Shubin Lyu1, C. L. Philip Chen2, Huimin Zhao1, Lin Chun1, Pingsheng Quan1
1Faculty of Computer Science, Guangdong polytechnic Normal University, Guangzhou, China
2Faculty of Science and Technology, University of Macau, Macau 99999, China

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Tài liệu tham khảo

Arthur, D., & Vassilvitskii, S. (2007). k-means++: the advantages of careful seeding. In SODA ’07.

Asuncion, A., & Newman, D. (2007). Uci machine learning repository.

Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997). Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7), 711–720.

Chen, C., & Liu, Z. (2018). Broad learning system: An effective and efficient incremental learning system without the need for deep architecture. IEEE Transactions on Neural Networks and Learning Systems, 29, 10–24.

Chen, C., Liu, Z., & Feng, S. (2019). Universal approximation capability of broad learning system and its structural variations. IEEE Transactions on Neural Networks and Learning Systems, 30, 1191–1204.

Elhefnawy, M., Ragab, A., & Ouali, M. S. (2021). Fault classification in the process industry using polygon generation and deep learning. Journal of Intelligent Manufacturing, 1–14.

Feng, S., & Chen, C. (2020). Fuzzy broad learning system: A novel neuro-fuzzy model for regression and classification. IEEE Transactions on Cybernetics, 50, 414–424.

Fu, Y., Cao, H., & Chen, X. (2021). Adaptive broad learning system for high-efficiency fault diagnosis of rotating machinery. IEEE Transactions on Instrumentation and Measurement, 70, 1–11.

Gong, X., Zhang, T., Chen, C. L. P., & Liu, Z. (2021). Research review for broad learning system: Algorithms, theory, and applications. IEEE transactions on cybernetics PP.

Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7132–7141).

Huang, G. B., Zhou, H., Ding, X., & Zhang, R. (2011). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2), 513–529.

Huang, S., Liu, Z., Jin, W., & Mu, Y. (2021). Broad learning system with manifold regularized sparse features for semi-supervised classification. Neurocomputing, 463, 133–143.

Janczak, A. (2004). Identification of nonlinear systems using neural networks and polynomial models: a block-oriented approach (Vol. 310). Springer.

Jin, J., Liu, Z., & Chen, C. (2018). Discriminative graph regularized broad learning system for image recognition. Science China Information Sciences, 61, 1–14.

Kim, E. H., Oh, S. K., & Pedrycz, W. (2017). Design of reinforced interval type-2 fuzzy c-means-based fuzzy classifier. IEEE Transactions on Fuzzy Systems, 26(5), 3054–3068.

Li, S., Xing, X., Fan, W., Cai, B., Fordson, P., & Xu, X. (2021). Spatiotemporal and frequential cascaded attention networks for speech emotion recognition. Neurocomputing, 448, 238–248.

Lin, J., Liu, Z., Chen, C., & Zhang, Y. (2020). Quaternion broad learning system: A novel multi-dimensional filter for estimation and elimination tremor in teleoperation. Neurocomputing, 380, 78–86.

Liu, Z., Chen, C., Feng, S., Feng, Q., & Zhang, T. (2021). Stacked broad learning system: From incremental flatted structure to deep model. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51, 209–222.

Mo, Y., Wu, Q., Li, X., & Huang, B. (2021). Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit. Journal of Intelligent Manufacturing, pp. 1–10.

Nikolaev, N. Y., & Iba, H. (2002). Genetic programming of polynomial models for financial forecasting. In Genetic Algorithms and Genetic Programming in Computational Finance (pp. 103–123). Springer.

Oh, S. K., Kim, W., Pedrycz, W., & Park, B. (2011). Polynomial-based radial basis function neural networks (p-rbf nns) realized with the aid of particle swarm optimization. Fuzzy Sets and Systems, 163, 54–77.

Oh, S. K., Kim, W. D., Pedrycz, W., & Joo, S. C. (2012). Design of k-means clustering-based polynomial radial basis function neural networks (prbf nns) realized with the aid of particle swarm optimization and differential evolution. Neurocomputing, 78(1), 121–132.

Oh, S. K., Yoo, S., & Pedrycz, W. (2013). Design of face recognition algorithm using pca -lda combined for hybrid data pre-processing and polynomial-based rbf neural networks : Design and its application. Expert Systems with Applications, 40, 1451–1466.

Ouyang, C. S., Kao, T. C., Cheng, Y. Y., Wu, C. H., Tsai, C. H., & Wu, M. W. (2016). An improved fuzzy extreme learning machine for classification and regression. In 2016 International Conference on Cybernetics, Robotics and Control (CRC), pp. 91–94. IEEE.

Quteishat, A., & Lim, C. P. (2008). A modified fuzzy min-max neural network with rule extraction and its application to fault detection and classification. Applied Soft Computing, 8(2), 985–995.

Samaria, F. S., & Harter, A. C. (1994). Parameterisation of a stochastic model for human face identification. In Proceedings of 1994 IEEE workshop on applications of computer vision, pp. 138–142. IEEE.

Tang, H., Dong, P., & Shi, Y. (2021). A construction of robust representations for small data sets using broad learning system. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51, 6074–6084.

Wong, S. Y., Yap, K. S., Yap, H. J., Tan, S. C., & Chang, S. W. (2015). On equivalence of fis and elm for interpretable rule-based knowledge representation. IEEE Transactions on Neural Networks and Learning Systems, 26, 1417–1430.

Yao, D., Liu, H., Yang, J., & Zhang, J. (2021). Implementation of a novel algorithm of wheelset and axle box concurrent fault identification based on an efficient neural network with the attention mechanism. Journal of Intelligent Manufacturing, 32(3), 729–743.

Zhu, L., Lian, C., Zeng, Z., & Su, Y. (2019). A broad learning system with ensemble and classification methods for multi-step-ahead wind speed prediction. Cognitive Computation, 12, 654–666.