A Self-Organizing RBF Neural Network Based on Distance Concentration Immune Algorithm

Junfei Qiao; Fei Li; Cuili Yang; Wenjing Li; Ke Gu

doi:10.1109/JAS.2019.1911852

Volume 7 Issue 1

Jan. 2020

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2020 > 7(1): 276-291

Junfei Qiao, Fei Li, Cuili Yang, Wenjing Li and Ke Gu, "A Self-Organizing RBF Neural Network Based on Distance Concentration Immune Algorithm," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 276-291, Jan. 2020. doi: 10.1109/JAS.2019.1911852

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Junfei Qiao, Fei Li, Cuili Yang, Wenjing Li and Ke Gu, "A Self-Organizing RBF Neural Network Based on Distance Concentration Immune Algorithm," IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 276-291, Jan. 2020. doi: 10.1109/JAS.2019.1911852

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A Self-Organizing RBF Neural Network Based on Distance Concentration Immune Algorithm

doi: 10.1109/JAS.2019.1911852

Both of the authors are with the department of Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China and Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China

Funds: This work was supported by the National Natural Science Foundation of China (61890930-5, 61533002, 61603012), the Major Science and Technology Program for Water Pollution Control and Treatment of China (2018ZX07111005), the National Key Research and Development Project (2018YFC1900800-5), and Beijing Municipal Education Commission Foundation (KM201710005025)

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Author Bio:
Junfei Qiao (M’11) received the B.E. and M.E. degrees in control engineering from Liaoning Technical University, Fuxin, China, in 1992 and 1995, respectively, and the Ph.D. degree from Northeast University, Shenyang, China, in 1998. From 1998 to 2000, he was a Post-Doctoral Fellow with the School of Automatics, Tianjin University, Tianjin, China. He is currently a Vice-President and Professor with the Beijing University of Technology, Beijing, China. His current research interests include neural networks, intelligent systems, self-adaptive/learning systems, and process control systems. Dr. Qiao is a member of the IEEE Computational Intelligence Society. He is currently a reviewer for over 20 international journals, such as the IEEE Transactions on Fuzzy Systems, IEEE Transactions on Neural Networks and Learning Systems

Fei Li is currently working toward the Ph.D. degree at Beijing University of Technology, Beijing, China. Her research interests include in intelligent control, multi-objective optimization algorithm and analysis and design of neural networks

Cuili Yang received the B.E. degree from China University of Petroleum, Dongying, China, in 2008, the M.S. degree from Tianjin University, Tianjin, China in 2010, and the Ph.D. degree from City University of Hong Kong, Hong Kong, China, in 2014. She is currently a Lecturer at Beijing University of Technology. Her current research interests include computational intelligence, modeling and control for wastewater treatment process

Wenjing Li received her bachelor degree at Xi’an Jiaotong University in 2007, and Ph.D. degree at the Institute of Automation, Chinese Academy of Sciences in 2013. She is now an Associate Professor in Beijing University of Technology. Her research interests include cognitive neuroscience, pattern recognition, and intelligent system

Ke Gu received the B.S. and Ph.D. degrees in electronic engineering from Shanghai Jiao Tong University, Shanghai, China, in 2009 and 2015, respectively. He is currently a Professor with Beijing University of Technology, Beijing, China. His research interests include environmental perception, image processing, quality assessment, and machine learning. He received the Best Paper Award from the IEEE Transactions on Multimedia, the Best Student Paper Award at the IEEE International Conference on Multimedia and Expo in 2016, and the Excellent Ph.D. Thesis Award from the Chinese Institute of Electronics in 2016. He was the Leading Special Session Organizer in the VCIP 2016 and the ICIP 2017, and serves as a Guest Editor for the Digital Signal Processing Journal. He is currently an Associate Editor of the IEEE Access and the IET Image Processing. He is a Reviewer for 20 top SCI journals
Corresponding author: The authors are with Beijing University of Technology, Beijing 100083, China (e-mail: Junfeig@bjut.edu.cn; lifeiaedu@hotmait.com; clyang5@bjut.edu.cn; wenjing.li@bjut.edu.cn; guke.doctor@gmail.com)
Received Date: 2019-03-30
Revised Date: 2019-07-17
Accepted Date: 2019-09-26

Available Online: 2019-12-09

Abstract

Abstract

Radial basis function neural network (RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm (DCIA) is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly, the information processing strength (IPS) algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm (DCIA-SORBFNN) is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIA-SORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors.

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References(37)

References

[1]	J. Park and I. W. Sandberg, " Universal approximation using radial-basis-function network,” Neural Computation, vol. 3, no. 2, pp. 246–257, Jul. 1991. doi: 10.1162/neco.1991.3.2.246
[2]	Y. W. Wong, K. P. Seng, and L. M. Ang, " Radial basis function neural network with incremental learning for face recognition,” IEEE Trans. Systems,Man,and Cybernetics,Part B (Cybernetics), vol. 41, no. 4, pp. 940–949, Aug. 2011. doi: 10.1109/TSMCB.2010.2101591
[3]	H. G. Han, X. L. Wu, and J. F. Qiao, " Real-time model predictive control using a self-organizing neural network,” IEEE Trans. Neural Network and Learning Systems, vol. 24, no. 9, pp. 1425–1436, Sep. 2013. doi: 10.1109/TNNLS.2013.2261574
[4]	V. Tomenko, " Online dimensionality reduction using competitive learning and radial basis function network,” Neural Networks, vol. 24, no. 5, pp. 501–511, Jun. 2011. doi: 10.1016/j.neunet.2011.02.007
[5]	A. Alexandridis, E. Chondrodima, and H. Sarimveis, " Radial basis function network training using a nonsymmetric partition of the input space and particle swarm optimization,” IEEE Trans. Neural Networks and Learning Systems, vol. 24, no. 2, pp. 219–230, Feb. 2013. doi: 10.1109/TNNLS.2012.2227794
[6]	R. Zhang, Z. B. Xu, G. B. Huang, and D. Wang, " Global convergence of online BP training with dynamic learning rate,” IEEE Trans. Neural Networks and Learning Systems, vol. 23, no. 2, pp. 330–341, Jan. 2012. doi: 10.1109/TNNLS.2011.2178315
[7]	Z. B. Xu, R. Zhang, and W. F. Jing, " When does online BP training converge?” IEEE Trans. Neural Networks, vol. 20, no. 10, pp. 1529–1539, Aug. 2009. doi: 10.1109/TNN.2009.2025946
[8]	B. D. Chen, S. L. Zhao, P. P. Zhu, and J. C. Principe, " Quantized kernel recursive least squares algorithm,” IEEE Trans. Neural Networks and Learning Systems, vol. 24, no. 9, pp. 1484–1491, May 2013. doi: 10.1109/TNNLS.2013.2258936
[9]	M. S. Al-Batah, N. A. M. Isa, K. Z. Zamli, and K. A. Azizli, " Modified recursive least squares algorithm to train the hybrid multilayered perceptron (HMLP) network,” Applied Soft Computing, vol. 10, no. 1, pp. 236–244, Jan. 2010. doi: 10.1016/j.asoc.2009.06.018
[10]	J. Jiang and Y. M. Zhang, " A novel variable-length sliding window blockwise least-squares algorithm for on-line estimation of time-varying parameters,” Int. J. Adaptive Control and Signal Processing, vol. 18, no. 6, pp. 505–521, Aug. 2004.
[11]	J. X. Peng, K. Li, and G. W. Irwin, " A novel continuous forward algorithm for rbf neural modelling,” IEEE Trans. Automatic Control, vol. 52, no. 1, pp. 117–122, Jun. 2007. doi: 10.1109/TAC.2006.886541
[12]	J. F. Qiao and H. G. Han, " Identification and modeling of nonlinear dynamical systems using a novel self-organizing RBF-based approach,” Automatica, vol. 48, no. 8, pp. 1729–1734, Aug. 2012. doi: 10.1016/j.automatica.2012.05.034
[13]	G. B. Huang, P. Saratchandran, and N. Sundararajan, " An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks,” IEEE Trans. Systems,Man,and Cybernetics,Part B (Cybernetics), vol. 34, no. 6, pp. 2284–2292, Dec. 2004. doi: 10.1109/TSMCB.2004.834428
[14]	G. B. Huang, P. Saratchandran, and N. Sundararajan, " A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation,” IEEE Trans. Neural Network, vol. 16, no. 1, pp. 57–67, Jan. 2005. doi: 10.1109/TNN.2004.836241
[15]	N. Vuković and Z. Miljković, " A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation,” Neural Network, vol. 46, no. 1, pp. 210–226, Jan. 2013.
[16]	H. G. Han, Y. N. Guo, and J. F. Qiao, " Self-organization of a recurrent RBF neural network using an information-oriented algorithm,” Neurocomputing, vol. 225, pp. 80–91, Nov. 2016.
[17]	J. Lian, Y. Lee, S. D. Sudhoff, and S. H. Zak, " Self-organizing radial basis function network for real-time approximation of continuous-time dynamical systems,” IEEE Trans. Neural Networks, vol. 19, no. 3, pp. 460–474, Mar. 2008.
[18]	R. V. Babu, S. Suresh, and A. Perkis, " No-reference JPEG-image quality assessment using GAP-RBF,” Signal Processing, vol. 87, no. 6, pp. 1493–1503, Jun. 2007.
[19]	H. M. Feng, " Self-generation RBFNs using evolutional PSO learning,” Neurocomputing, vol. 70, no. 1, pp. 241–251, Dec. 2006.
[20]	A. Alexandridis, E. Chondrodima, and H. Sarimveis, " Radial basis function network training using a nonsymmetric partition of the input space and particle swarm optimization,” IEEE Trans. Neural Network and Learning Systems, vol. 24, no. 2, pp. 219–230, Dec. 2012.
[21]	H. G. Han, W. Lu, Y. Hou, and J. F. Qiao, " An adaptive-PSO-based self-organizing RBF neural network,” IEEE Trans. Neural Network and Learning Systems, vol. 29, no. 1, pp. 104–117, Jan. 2018. doi: 10.1109/TNNLS.2016.2616413
[22]	K. E. Parsopoulos and M. N. Vrahatis, " Recent approaches to global optimization problems through particle swarm optimization,” Natural Computing, vol. 1, no. 2–3, pp. 235–306, Jun. 2002.
[23]	Y. Zhong, L. Zhang, B. Huang, and P. X. Li, " An unsupervised artificial immune classifier for multi/hyperspectral remote sensing imagery,” IEEE Trans. Geoscience and Remote Sensing, vol. 44, no. 2, pp. 420–431, Jan. 2006. doi: 10.1109/TGRS.2005.861548
[24]	M. J. Er, S. Wu, J. Lu, and H. L. Toh, " Face recognition with radial basis function (RBF) neural networks,” IEEE Trans. Neural Networks, vol. 13, no. 3, pp. 697–710, Feb. 2002. doi: 10.1109/TNN.2002.1000134
[25]	T. Liu, Y. C. Wang, Z. J. Wang, and J. Meng, " Distance concentration-based artificial immune algorithm,” J. China University of Mining and Technology, vol. 15, no. 2, pp. 81–85, Jun. 2005.
[26]	J. S. Chun, M. K. Kim, H. K. Jung, and S. K. Hong, " Shape optimization of electromagnetic devices using immune algorithm,” IEEE Trans. Magnetics, vol. 33, no. 2, pp. 1876–1879, Mar. 1876.
[27]	R. R. Zheng, Z. Y. Mao, and X. X. Luo, " Analysis and research of improved artificial immune algorithm,” Computer Engineering and Applications, vol. 39, no. 34, pp. 35–37, Apr. 2003.
[28]	Q. Z. Lin, J. Y. Chen, Z. H. Zhan, W. N. Chen, C. A. C. Coello, Y. L. Yin, C. M. Lin, and J. Zhang, " A hybrid evolutionary immune algorithm for multiobjective optimization problems,” IEEE Trans. Evolutionary Computation, vol. 20, no. 5, pp. 711–729, Oct. 2016.
[29]	G. Rudolph, " Convergence analysis of canonical genetic algorithms,” IEEE Trans. Neural Network, vol. 5, no. 1, pp. 96–101, 1994. doi: 10.1109/72.265964
[30]	M. Han, J. Fan, and J. Wang, " A dynamic feedforward neural network based on Gaussian particle swarm optimization and its application for predictive control,” IEEE Trans. Neural Networks, vol. 22, no. 9, pp. 1457–1468, Sep. 2011. doi: 10.1109/TNN.2011.2162341
[31]	H. G. Han, S. Zhang, and J. F. Qiao, " An adaptive growing and pruning algorithm for designing recurrent neural network,” Neurocomputing, vol. 242, pp. 51–62, Jun. 2017. doi: 10.1016/j.neucom.2017.02.038
[32]	T. T. Xie, H. Yu, J. Hewlett, P. Rozycki, and B. Wilamowshi, " Fast and efficient second-order method for training radial basis function network,” IEEE Trans. Neural Network and Learning Systems, vol. 23, no. 4, pp. 609–619, Apr. 2012. doi: 10.1109/TNNLS.2012.2185059
[33]	H. G. Han, W. D. Zhou, J. F. Qiao, and G. Feng, " A direct self-constructing neural controller design for a class of nonlinear systems,” IEEE Trans. Neural Network and Learning Systems, vol. 26, no. 6, pp. 1312–1322, Jun. 2015. doi: 10.1109/TNNLS.2015.2401395
[34]	A. Nickabadi, M. M. Ebadzadeh, and R. Safabakhsh, " A novel particle swarm optimization algorithm with adaptive inertia weight,” Applied Soft Computing, vol. 11, no. 4, pp. 3658–3670, Jun. 2011. doi: 10.1016/j.asoc.2011.01.037
[35]	M. Taherkhani and R. Safabakhsh, " A novel stability-based adaptive inertia weight for particle swarm optimization,” Applied Soft Computing, vol. 38, pp. 281–295, Jan. 2016. doi: 10.1016/j.asoc.2015.10.004
[36]	H. M. Feng, " Self-generation RBFNs using evolutional PSO learning,” Neurocomputing, vol. 70, no. 1–3, pp. 241–251, Dec. 2006. doi: 10.1016/j.neucom.2006.03.007
[37]	F. Li, C. L. Yang, and J. F. Qiao, " A novel RBF neural network design based on immune algorithm system”, in Proc. 36th Chinese Control Conf. (CCC), pp. 4598-4603, Jul. 2017.

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A Self-Organizing RBF Neural Network Based on Distance Concentration Immune Algorithm

doi: 10.1109/JAS.2019.1911852

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