全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

广义约束神经元网络模型系统结构参数辨识(英)

, PP. 509-515

Keywords: GCNN,参数辨识,SISO,MINO,MIMO

Full-Text   Cite this paper   Add to My Lib

Abstract:

系统参数的辨识有助于帮助提高系统的透明性,从而增强系统的可控能力;如何提高系统参数的辨识能力是一个非常重要的课题,目前在单输入单输出(SISO)参数辨识上已经取得了一些成果.通过分析广义约束神经元网络模型,结合已有的一些理论,经过推理总结得到了m输入n输出(MINO)系统以及多输入多输出(MIMO)参数的辨识理论方法.经过实际验证,它为提高“黑盒”的透明度是可行的.该理论的提出,有助于提高广义约束神经元网络模型参数的辨识能力,进一步提高了神经网络“黑盒”系统的模型识别能力.

References

[1]  Basios V, Bonushkina A Y, Ivanov V V. A method for approximating onedimensional functions[J]. Comput Math Appl,1997,7/8:687693.
[2]  Catchpole E A, Morgan J T. Detecting parameter redundancy[J]. Biometrika,1997,84:187196.
[3]  Castiello C, Castellano G, Fanelli A M. Mindful: A framework for metainductive neurofuzzy learning[J]. Information Sciences,2008,178:32533274.
[4]  Castro J R, Castillo O, Melin P, et al. A hybrid learning algorithm for a class of interval type2 fuzzy neural networks[J]. Info Sci,2009,179(13):21752193.
[5]  Chen Y, Yang B, Dong J, et al. Timeseries forecasting using flexible neural tree model[J]. Information Sciences,2005,174:219235.
[6]  Cheney W, Light W. A Course of Approximation Theory[M]. New York:Brooks Publishing,2000.
[7]  Psichogios D, Ungar L H. A hybrid neural network: First principles approach to process modeling[J]. AIChE J,1992,38:14991511.
[8]  Scholkopf B, Simard P, Smola A, et al. Prior knowledge in support vector kernels[J]. Adv Neural Info Proc Sys,1998,10:640646.
[9]  Thompson M L, Kramer M A. Modeling chemical processes using prior knowledge and neural networks[J]. AIChE J,1994,40:13281340.
[10]  Thrun S. Explanation Based Neural Network Learning: A Lifelong Learning Approach[M]. Boston:Kluwer Academic Publisher,1996.
[11]  Tickle A, Andrews R, Golea M, et al. The truth will come to light: Directions and challenges in extracting the knowledge embedded within trained artificial neural networks[J]. IEEE Trans Neural Networks,1998,9:10571068.
[12]  Toussaint U V, Gori S, Dose V. Invariance priors for Bayesian feedforward neural networks[J]. Neural Networks,2006,19:15501557.
[13]  Towell G, Shavlik J. Knowledgebased artificial neural networks[J]. Art Intel,1994,70:119165.
[14]  Vapnik V. Statistical Learning Theory[M]. New York:John Wiley & Sons,1998.
[15]  Waibel A. Modular construction of timedelay neural networks for speech recognition[J]. Neural Comput,1989,1:3946.
[16]  Hu B G, Qu H B, Wang Y, et al. Generalized constraint neural network model: Associating partially known relationships for nonlinear regressions[R]. NLPR Technical Report,2007.
[17]  Hu B G, Qu H B, Wang Y, et al. A Generalizedconstraint neural network model: Associating partially known relationships for nonlinear regressions[J]. Information Sciences,2009,179:19291943.
[18]  Yang S H, Hu B G, Cournède P H. Structural identifiability of generalized constraints neural network models for nonlinear regression[J]. Neurocomputing,2008,72:392400.
[19]  Barnard E, Casasent D. Invariance and neural nets[J]. IEEE Trans Neural Networks,1991,2:498508.
[20]  CozzioBueler R A. The design of neural networks using a priori knowledge[D]. Switzerland:Swiss Federal Institute of Technology,1995.
[21]  Duda R O, Hart P E, Stork D. Pattern Classification[M]. 2nd ed. New York:John Willy & Sons,2001.
[22]  Fu L M. Rule generation from neural networks[J]. IEEE Trans Syst Man Cyber,1994,24:11141124.
[23]  Han F, Ling Q H, Huang D H. Modified constrained learning algorithms incorporating additional functional constraints into neural networks[J]. Information Sciences,2008,178:907919.
[24]  Haykin S. A Comprehensive Foundation[M]. New York:PrenticeHall,1999.
[25]  Huang D S. A constructive approach for finding arbitrary roots of polynomials by neural networks[J]. IEEE Trans Neural Networks,2004,15:477491.
[26]  Jang J S R. ANFIS: Adaptivenetworkbased fuzzy inference system[J]. IEEE Trans Syst Man Cyber,1993,23:665685.
[27]  Joerding W H, Meador J L. Encoding a priori information in feed forward networks[J]. Neural Networks,1991,4:847856.
[28]  Ljung L. Theory for the User[M]. New Jersey:PrenticeHall,1999.
[29]  Lampinen J, Vehtari A. Bayesian approach for neural networks C review and case studies[J]. Neural Networks,2001,14:257274.
[30]  Mitchell T M. Machine Learning[M]. New York:McGrawHill,1997.
[31]  Milanic S, Strmcnik S, Sel D, et al. Incorporating prior knowledge into artificial neural networks an industrial case study[J]. Neurocomputing,2004,62:131151.
[32]  Niyogi P, Girosi F, Poggio T. Incorporating prior information in machine learning by creating virtual examples[J]. Proc IEEE,1998,86(11):21962209.
[33]  Oh S K, Pedrycz W, Roh S B. Genetically optimized fuzzy polynomial neural networks with fuzzy setbased polynomial neurons[J]. Information Sciences,2006,176:34903519.
[34]  Oliveira R. Combining first principles modeling and artificial neural networks: A general framework[J]. Comput Chem Engine,2004,28:755766.
[35]  Omlin C W, Giles C L. Rule revision with recurrent neural networks[J]. IEEE Trans Know Data Engine,1996,8:183188.
[36]  Opitz D, Shavlik J. Connectionist theory refinement: Genetically searching the space of network topologies[J]. J Art Intel Research,1997,6:177209.
[37]  Poggio T, Girosi F. Networks for approximation and learning[J]. Proc IEEE,1990,78(9):14811497.
[38]  Wilson J A, Zorzetto L F M. A generalised approach to process state estimation using hybrid artificial neural network/mechanistic model[J]. Comput Chem Engine,1997,21:951963.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133