Basios V, Bonushkina A Y, Ivanov V V. A method for approximating onedimensional functions[J]. Comput Math Appl,1997,7/8:687693.
[2]
Catchpole E A, Morgan J T. Detecting parameter redundancy[J]. Biometrika,1997,84:187196.
[3]
Castiello C, Castellano G, Fanelli A M. Mindful: A framework for metainductive neurofuzzy learning[J]. Information Sciences,2008,178:32533274.
[4]
Castro J R, Castillo O, Melin P, et al. A hybrid learning algorithm for a class of interval type2 fuzzy neural networks[J]. Info Sci,2009,179(13):21752193.
[5]
Chen Y, Yang B, Dong J, et al. Timeseries forecasting using flexible neural tree model[J]. Information Sciences,2005,174:219235.
[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,1992,38:14991511.
[8]
Scholkopf B, Simard P, Smola A, et al. Prior knowledge in support vector kernels[J]. Adv Neural Info Proc Sys,1998,10:640646.
[9]
Thompson M L, Kramer M A. Modeling chemical processes using prior knowledge and neural networks[J]. AIChE J,1994,40:13281340.
[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,1998,9:10571068.
[12]
Toussaint U V, Gori S, Dose V. Invariance priors for Bayesian feedforward neural networks[J]. Neural Networks,2006,19:15501557.
[13]
Towell G, Shavlik J. Knowledgebased artificial neural networks[J]. Art Intel,1994,70:119165.
[14]
Vapnik V. Statistical Learning Theory[M]. New York:John Wiley & Sons,1998.
[15]
Waibel A. Modular construction of timedelay neural networks for speech recognition[J]. Neural Comput,1989,1:3946.
[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 Generalizedconstraint neural network model: Associating partially known relationships for nonlinear regressions[J]. Information Sciences,2009,179:19291943.
[18]
Yang S H, Hu B G, Cournède P H. Structural identifiability of generalized constraints neural network models for nonlinear regression[J]. Neurocomputing,2008,72:392400.
[19]
Barnard E, Casasent D. Invariance and neural nets[J]. IEEE Trans Neural Networks,1991,2:498508.
[20]
CozzioBueler 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,1994,24:11141124.
[23]
Han F, Ling Q H, Huang D H. Modified constrained learning algorithms incorporating additional functional constraints into neural networks[J]. Information Sciences,2008,178:907919.
[24]
Haykin S. A Comprehensive Foundation[M]. New York:PrenticeHall,1999.
[25]
Huang D S. A constructive approach for finding arbitrary roots of polynomials by neural networks[J]. IEEE Trans Neural Networks,2004,15:477491.
[26]
Jang J S R. ANFIS: Adaptivenetworkbased fuzzy inference system[J]. IEEE Trans Syst Man Cyber,1993,23:665685.
[27]
Joerding W H, Meador J L. Encoding a priori information in feed forward networks[J]. Neural Networks,1991,4:847856.
[28]
Ljung L. Theory for the User[M]. New Jersey:PrenticeHall,1999.
[29]
Lampinen J, Vehtari A. Bayesian approach for neural networks C review and case studies[J]. Neural Networks,2001,14:257274.
[30]
Mitchell T M. Machine Learning[M]. New York:McGrawHill,1997.
[31]
Milanic S, Strmcnik S, Sel D, et al. Incorporating prior knowledge into artificial neural networks an industrial case study[J]. Neurocomputing,2004,62:131151.
[32]
Niyogi P, Girosi F, Poggio T. Incorporating prior information in machine learning by creating virtual examples[J]. Proc IEEE,1998,86(11):21962209.
[33]
Oh S K, Pedrycz W, Roh S B. Genetically optimized fuzzy polynomial neural networks with fuzzy setbased polynomial neurons[J]. Information Sciences,2006,176:34903519.
[34]
Oliveira R. Combining first principles modeling and artificial neural networks: A general framework[J]. Comput Chem Engine,2004,28:755766.
[35]
Omlin C W, Giles C L. Rule revision with recurrent neural networks[J]. IEEE Trans Know Data Engine,1996,8:183188.
[36]
Opitz D, Shavlik J. Connectionist theory refinement: Genetically searching the space of network topologies[J]. J Art Intel Research,1997,6:177209.
[37]
Poggio T, Girosi F. Networks for approximation and learning[J]. Proc IEEE,1990,78(9):14811497.
[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,1997,21:951963.