Learning with generalized-mean neuron model

Neurocomputing

Volumn 69, Issue 16-18, 2006, Pages 2026-2032

  Yadav, R N ^a   Kumar, Nimit ^b   Kalra, Prem K ^c   John, Joseph ^c  

^a MAULANA AZAD NATIONAL INSTITUTE OF TECHNOLOGY   (India)

^b IBM RESEARCH INDIA   (India)

^c INDIAN INSTITUTE OF TECHNOLOGY KANPUR   (India)

Author keywords

Classification; Function approximation; Generalized mean neuron; Multilayer perceptrons

Indexed keywords

APPROXIMATION THEORY; COMPUTATIONAL METHODS; MATHEMATICAL MODELS; TIME SERIES ANALYSIS;

FUNCTION APPROXIMATION; GENERALIZED MEAN NEURON; NEURON MODEL; TIME SERIES PREDICTION PROBLEMS;

MULTILAYER NEURAL NETWORKS;

ACCURACY; ANALYTICAL ERROR; ARTICLE; ARTIFICIAL NEURAL NETWORK; DIABETES MELLITUS; HUMAN; INFORMATION PROCESSING; INTERMETHOD COMPARISON; INTERNET; LEUKEMIA; MATHEMATICAL MODEL; MAXIMUM LIKELIHOOD METHOD; PERFORMANCE; PREDICTION; PRIORITY JOURNAL; QUALITY CONTROL; TIME SERIES ANALYSIS;

EID: 33748563993     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2005.10.006     Document Type: Article

References (18)

1. Akaike H. A new look at the statistical model identification. IEEE Trans. Appl. Comput. AC-19 (1974) 716-723
2. M. Basu, T.K. Ho, Learning behavior of single neuron classifiers on linearly separable or nonseparable inputs, in: IEEE IJCNN'99, 1999, pp. 1259-1264.
3. Box G.E.P., Jenkins G.M., and Reinse G.C. Time Series Analysis: Forecasting and Control (1994), Prentice-Hall, Englewood Cliffs, NJ
4. Fogel D.B. An information criterion for optimal neural network selection. IEEE Trans. Neural Networks 2 (1991) 490-497
5. M. Guler, E. Sahin, A new higher-order binary-input neural unit: learning and generalizing effectively via using minimal number of monomials, in: Proceedings of Third Turkish Symposium on Artificial Intelligence and Neural Networks, 1994, pp. 51-60.
6. Iyoda E.M., Nobuhara H., and Hirota K. A solution for the N-bit parity problem using a single translated multiplicative neuron. Neural Process. Lett. 18 (2003) 233-238
7. R. Labib, New single neuron structure for solving nonlinear problems, in: IEEE IJCNN'99, 1999, pp. 617-620.
8. Mackey M., and Glass L. Oscillation and chaos in physiological control systems. Science 197 (1997) 287-289
9. McCulloch W.S., and Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5 (1943) 115-133
10. Murata N., Yoshizawa S., and Amari S. Network information criterion-determining the number of hidden units for and artificial neural networks model. IEEE Trans. Neural Networks 5 (1994) 865-872
11. Piegat A. Fuzzy Modeling and Control (2001), Physica-Verlag, Heidelberg, NY
12. Plate T.A. Randomly connected Sigma-Pi Neurons can form associator networks. NETCNS: Network: Comput. Neural Syst. 11 (2000) 321-332
13. Proakis J.G. Digital Communications (2001), McGraw Hill International, Singapore
14. Ripley B.D. Neural networks and related methods of classification. J. R. Stat. Soc. Ser. B 56 (1994) 409-456
15. Ripley B.D. Pattern Recognition and Neural Networks (1996), Cambridge University Press, Cambridge, UK
16. Schmitt M. On the complexity of computing and learning with multiplicative neural networks. Neural Comput. 14 (2001) 241-301
17. Schreiner K. Neuron function: the mystery persists. IEEE Intell. Syst. 16 (2001) 4-7
18. Zhang C.N., Zhao M., and Wang M. Logic operations based on single neuron rational model. IEEE Trans. Neural Networks 11 (2000) 739-747