Optimization of self-organizing polynomial neural networks

Expert Systems with Applications

Volumn 40, Issue 11, 2013, Pages 4528-4538

  Maric, Ivan ^a  

^a RUDER BO KOVI INSTITUTE   (Croatia)

Author keywords

GMDH; Levenberg Marquardt algorithm; Particle swarm optimization; Polynomial neural networks; Time series modeling

Indexed keywords

APPROXIMATION ACCURACY; GMDH; GROUP METHOD OF DATA HANDLING; LEVENBERG-MARQUARDT ALGORITHM; PARTIAL OPTIMIZATIONS; POLYNOMIAL NEURAL NETWORKS; SUPPORT VECTOR METHOD; TIME SERIES MODELING;

ALGORITHMS; DATA HANDLING; NEURAL NETWORKS; POLYNOMIALS;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 84876057277     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2013.01.060     Document Type: Article

References (20)

1. V. Ceperic, G. Gielen, and A. Baric Recurrent sparse support vector regression machines trained by active learning in the time-domain Expert Systems with Applications 39 2012 10933 10942
2. S.C. Chapra, and R.P. Canale Numerical methods for engineers 3rd ed. 1998 McGraw-Hill New York
3. R.C. Eberhart, and J. Kennedy A new optimizer using particles swarm theory Proceedings of the sixth international symposium on micro machine and human science, Nagoya, Japan 1995 IEEE Service Center Piscataway, NJ 39 43
4. S. Ferrari, and R.F. Stengel Smooth function approximation using neural 756 networks IEEE Transactions on Neural Networks 16 1 2005 24 38
5. G. Holzmann, and H. Hauser Echo state networks with filter neurons and a delay & sum readout Neural Networks 23 2010 244 256
6. ISO-20765-1. (2005). Natural gas-Calculation of thermodynamic properties-Part1: Gas phase properties for transmission and distribution applications, Ref. No. ISO-20765-1:2005.
7. ISO-5167-2. (2003). Measurement of fluid flow by means of pressure differential devices inserted in circular-cross section conduits running full-Part 2: Orifice plates, Ref. No. ISO-5167-2:2003(E).
8. A.G. Ivakhnenko Polynomial theory of complex systems IEEE Transactions on Systems Man, and Cybernetics SMC 1 4 1971 364 378
9. M. Iwasaki, H. Takei, and N. Matsui GMDH-based modeling and feedforward compensation for nonlinear friction in table drive systems IEEE Transactions on Industrial Electronics 50 6 2003 1172 1178
10. Jin, R., Chen, W., & Simpson, T. W. (2000). Comparative studies of metamodeling techniques under multiple modeling criteria. In AIAA-2000-4801, AIAA/USAF/ NASA/ISSMO 8th symposium on multidisciplinary analysis and optimization, Long Beach, CA.
11. K. Levenberg A method for the solution of certain problems in least squares Quarterly of Applied Mathematics 2 1944 164 168
12. I. Maric, and I. Ivek Compensation for Joule-Thomson effect in flow rate measurements by GMDH polynomial Flow Measurement and Instrumentation 21 1 2010 134 142
13. I. Maric, and I. Ivek Self-organizing polynomial networks for time-constrained applications IEEE Transactions on Industrial Electronics 58 5 2011 2019 2029
14. D. Marquardt An algorithm for least-squares estimation of nonlinear parameters SIAM Journal on Applied Mathematics 11 1963 431 441
15. N.Y. Nikolaev, and H. Iba Learning polynomial feedforward neural networks by genetic programming and backpropagation IEEE Transactions on Neural Networks 14 2 2003 337 350
16. J. Schmidhuber, D. Wierstra, M. Gagliolo, and F. Gomez Training recurrent networks by evolino Neural Computation 19 2007 757 779
17. Y.H. Shi, and R.C. Eberhart A modified particle swarm optimizer Proceedings of international conference on evolutionary computation 1998 IEEE WCCI Anchorage, Alaska 69 73
18. V. Vapnik, S.E. Golowich, and A.J. Smola Support vector method for function approximation, regression estimation and signal processing Advances in neural information processing systems Vol. 9 1997 Mit Press Cambridge 281 287
19. I.H. Witten, and F. Eibe Data mining: Practical machine learning tools and techniques with Java implementations 2005 Morgan Kaufmann Publishers 9780120884070
20. Y. Xue, L. Yang, and S. Haykin Decoupled echo state networks with lateral inhibition Neural Networks 20 2007 365 376