[1]乔俊飞,安茹,韩红桂.基于相对贡献指标的自组织RBF神经网络的设计[J].智能系统学报,2018,13(2):159-167.[doi:10.11992/tis.201608009]
 QIAO Junfei,AN Ru,HAN Honggui.Design of self-organizing RBF neural network based on relative contribution index[J].CAAI Transactions on Intelligent Systems,2018,13(2):159-167.[doi:10.11992/tis.201608009]
点击复制

基于相对贡献指标的自组织RBF神经网络的设计

参考文献/References:
[1] QIAO J F, ZHANG Z Z, BO Y C. An online self-adaptive modular neural network for time-varying systems[J]. Neurocomputing, 2014, 125: 7-16.
[2] 韩红桂, 李淼, 乔俊飞. 基于模型输出敏感度分析的动态神经网络结构设计[J]. 电子学报, 2010, 38(3): 731-736.
HAN Honggui, LI Miao, Qiao Junfei. Design of dynamic neural network based on the sensitivity analysis of model output[J]. Acta electronica sinica, 2010, 38(3): 731-736.
[3] PLATT J. A resource-allocating network for function interpolation[J]. Neural computation, 1991, 3(2): 213-225.
[4] LU Y G, SUNDARARAJAN N, SARATCHANDRAN P. A sequential learning scheme for function approximation using minimal radial basis function neural networks[J]. Neural computation, 1997, 9(2): 461-478.
[5] PANCHAPAKESAN C, PALANISWAMI M, RALPH D, et al. Effects of moving the center’s in an RBF network[J]. IEEE transactions on neural networks, 2002, 13(6): 1299-1307.
[6] HUANG G B, SARATCHANDRAN P, SUNDARARAJAN N. A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation[J]. IEEE transactions on neural networks, 2005, 16(1): 57-67.
[7] GONZALEZ J, ROJAS I, ORTEGA J, et al. Multi-objective evolutionary optimization of the size, shape, and position parameters of radial basis function networks for function approximation[J]. IEEE transactions on neural networks, 2003, 14(6): 1478-1495.
[8] FENG H M. Self-generation RBFNs using evolutional PSO learning[J]. Neurocomputing, 2006, 70(1): 241-251.
[9] HAO C, YU G, XIA H. Online modeling with tunable RBF network[J]. IEEE transactions on cybernetics, 2013, 43(3): 935-947.
[10] LIAN J M, LEE Y G, SCOTT D, SUDHOFF, et al. Self-organizing radial basis function network for real-time approximation of continuous-time dynamical systems[J]. IEEE transactions on neural networks, 2008, 19(3): 460-474.
[11] YU H, REINER P D, XIE T T, et al. An incremental design of radial basis function networks[J]. IEEe transactions on neural networks and learning systems, 2014, 25(10): 1793-1803.
[12] CONSTANTINOPOULO C, LⅡKAS A. An incremental training method for the probabilistic RBF network[J]. IEEE transactions on neural networks, 2006, 17(4): 966-974.
[13] CHEN S, HANZO L, TAN S. Symmetric complex-valued RBF receiver for multiple-antenna-aided wireless systems[J]. IEEe transactions on neural networks, 2008, 19(9): 1659-1665.
[14] 王晓丽, 黄蕾, 杨鹏, 等. 动态RBF神经网络在浮选过程模型失配中的应用[J]. 化工学报, 2016, 3(67): 897-902.
WANG Xiaoli, HUANG Lei, YANG Peng, et al. Dynamic RBF neural networks for model mismatch problem and its application in flotation process[J]. CIESC journal, 2016: 3(67): 897-902.
[15] WILAMOWSKI B M, YU H. Improved computation for Levenberg-Marquardt training[J]. IEEE transactions on neural networks, 2010, 21(6): 930-937.
[16] MA C, JIANG L. Some research on Levenberg-Marquardt method for the nonlinear equations[J]. Applied mathematics and computation, 2007, 184(2): 1032-1040.
[17] XIE T T, YU H, HEWLETT J, et al. Fast and efficient second-order method for training radial basis function networks[J]. IEEE transactions on neural networks and learning systems, 2012, 23(4): 609-619.
[18] QIAO J F, HAN H G. Identification and modeling of nonlinear dynamical systems using a novel self-organizing RBF-based approach[J]. Automatica, 2012, 48(8): 1729-1734.
[19] CHEN C, WANG F Y. A self-organizing neuro-fuzzy network based on first order effect sensitivity analysis[J]. Neurocomputing, 2013, 118: 21-32.
[20] HAN H G, CHEN Q L, QIAO J F. An efficient self-organizing RBF neural network for water quality prediction[J]. Neural networks the official journal of the international neural network society, 2011, 24(7): 717-25.
[21] HUANG G B, SARATCHANDRAN P, SUNDARARAJAN N. An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks[J]. IEEE transactions on systems, man, and cybernetics, Part B: (Cybernetics), 2004, 34(6): 2284-2292.
[22] CHO K B, WANG B Y. Radial basis function based adaptive fuzzy systems and their applications to system identification and prediction[J]. Fuzzy sets and systems, 1996, 83(3): 325-339.
[23] EBADZADEH M M, SALIMI-BADR A. CFNN: correlated fuzzy neural network[J]. Neurocomputing, 2015, 148: 430-444.
[24] An R, Li W J, Han H G, et al. An improved Levenberg-Marquardt algorithm with adaptive learning rate for RBF neural network[C]//IEEE Control Conference, [s.l.], 2016: 3630-3635.
相似文献/References:
[1]章? 钱,李士勇.一种新型自适应RBF神经网络滑模制导律[J].智能系统学报,2009,4(4):339.
 ZHANG Qian,LI Shi-yong.A new adaptive RBFNN sliding mode guidance law for intercepting maneuvering targets[J].CAAI Transactions on Intelligent Systems,2009,4():339.
[2]张秀玲,陈丽杰,逄宗朋,等.RBF神经网络的板形预测控制[J].智能系统学报,2010,5(1):70.
 ZHANG Xiu-ling,CHEN Li-jie,PANG Zong-peng,et al.A predictive system for process control of flatness in rolling mills using a radial basis function network[J].CAAI Transactions on Intelligent Systems,2010,5():70.
[3]陈亮,何为,韩力群.RBF神经网络的行车路径代价函数建模[J].智能系统学报,2011,6(5):424.
 CHEN Liang,HE Wei,HAN Liqun.Radial basis function neural network modeling of the traffic path cost function[J].CAAI Transactions on Intelligent Systems,2011,6():424.
[4]陈亮,何为,韩力群.城市交通最优路径算法[J].智能系统学报,2012,7(2):167.
 CHEN Liang,HE Wei,HAN Liqun.Study on an urban transportation optimal path algorithm[J].CAAI Transactions on Intelligent Systems,2012,7():167.
[5]蒙西,乔俊飞,李文静.基于快速密度聚类的RBF神经网络设计[J].智能系统学报,2018,13(3):331.[doi:10.11992/tis.201702014]
 MENG Xi,QIAO Junfei,LI Wenjing.Construction of RBF neural networks via fast density clustering[J].CAAI Transactions on Intelligent Systems,2018,13():331.[doi:10.11992/tis.201702014]

备注/Memo

收稿日期:2016-08-29。
基金项目:国家自然科学基金重点项目(61533002,61225016);北京市教育委员会科研计划项目(km201410005002);高等学校博士学科点基金项目(20131103110016).
作者简介:乔俊飞,男,1968年生,教授,博士生导师。主要研究方向为智能信息处理、智能优化控制。获教育部科技进步奖一等奖和北京市科学技术奖三等奖各1项,获得授权国家发明专利12项。发表学术论文近70篇,被SCI检索15篇;安茹,女,1990年生,硕士研究生,主要研究方向为智能控制理论及应用、非线性系统建模;韩红桂,男,1983年生,教授,博士生导师,主要研究方向为污水处理过程建模、优化与控制。获得授权国家发明专利13项,授权实用新型专利3项,授权软件著作权11项;研究成果获教育部科学技术进步奖一等奖等。近5年来发表学术论文35篇,撰写专著1部。
通讯作者:乔俊飞.E-mail:anru@emails.bjut.edu.cn.

更新日期/Last Update: 1900-01-01
Copyright © 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134 邮箱:tis@vip.sina.com