The novel aggregation function-based neuron models in complex domain

Soft Computing

Volumn 14, Issue 10, 2010, Pages 1069-1081

  Tripathi, Bipin Kumar ^a   Kalra, Prem K ^a,b  

^a INDIAN INSTITUTE OF TECHNOLOGY KANPUR   (India)

^b INDIAN INSTITUTE OF TECHNOLOGY JODHPUR   (India)

Author keywords

2 D transformation; Complex back propagation; Complex multilayer perceptron

Indexed keywords

AGGREGATION FUNCTIONS; ARTIFICIAL NEURONS; COMPLEX DOMAINS; COMPUTATIONAL POWER; FASTER CONVERGENCE; FEED-FORWARD; FUNCTIONAL MAPPING; GENERALIZATION ABILITY; GENERALIZATION CAPABILITY; GLOBAL APPROXIMATION; GROUPING PROCESS; INPUT SIGNAL; INPUT-OUTPUT MAPPING; LOCAL APPROXIMATION; MATHEMATICAL REPRESENTATIONS; MULTI LAYER PERCEPTRON; NEURON MODEL; NON-LINEAR; NON-LINEARITY; PREDICTION ACCURACY; RADIAL BASIS; RBF NETWORK;

BACKPROPAGATION; ELECTRIC LOADS; MAPPING; MULTILAYER NEURAL NETWORKS; NEURONS; RADIAL BASIS FUNCTION NETWORKS;

MULTILAYERS;

EID: 77954084070     PISSN: 14327643     EISSN: 14337479     Source Type: Journal    
DOI: 10.1007/s00500-009-0502-5     Document Type: Article

References (21)

1. Aizenberg I, Moraga C (2007) Multilayer feedforward neural network based on multi-valued neurons (MLMVN) and a back-propagation learning algorithm. Soft Comput 11(2): 169-183.
2. Aizenberg NN, Ivaskiv YL, Pospelov DA (1971) About one generalization of the threshold function Doklady Akademii Nauk SSSR (The Reports of the Academy of Sciences of the USSR) 196(6): 1287-1290 (in Russian).
3. Aizenberg I, Aizenberg N, Vandewalle J (2000) Multi-valued and universal binary neurons: theory, learning, applications. Kluwer, Boston.
4. Blake CL, Merz CJ (1998) UCI repository of machine learning database. http://www.ics.uci.edu/mealrn/MLRepository.html, Department of Information and Computer Science, University of California.
5. Brown JW, Churchill RV (2003) Complex variables and applications, VIIth edn. Mc Graw-Hill, New York.
6. Chaturvedi DK, Satsangi PS, Kalra PK (1999) New neuron models for simulating rotating electrical machines and load forecasting problems. Electr Power Syst Res 52: 123-131.
7. Chen X, Li S (2005) A modified error function for the complex-value back-propagation neural network. Neural Inf Process 8(1).
8. Foggel BD (1991) An information criterion for optimal neural network selection. IEEE Trans Neural Netw 2: 490-497.
9. Georgiou GM, Koutsougeras C (1992) Complex domain back-propagation. IEEE Trans Circuits Syst II 39(5).
10. Gorman RP, Sejnowski TJ (1988) Analysis of hidden units in a layered network trained to classify sonar targets. Neural Netw 1: 75-89.
11. Gupta MM, Homma N (2003) Static and dynamic neural networks, from fundamentals to advanced theory. Wiley, New York.
12. Hirose A (2006) Complex-valued neural networks. Springer, New York.
13. Homma N, Gupta MM (2002) A general second-order neural unit. Bull Coll Med Sci Tohoku Univ 11(1): 1-6.
14. Jianping D, Sundararajan N, Saratchandran P (2002) Communication channel equalization using complex-valued minimal radial basis function neural networks. IEEE Trans Neural Netw 13(3): 687-696.
15. Koch C (1999) Biophysics of computation: information processing in single neurons. Oxford University Press, Oxford.
16. Lee CC, Chung PC, Tsai JR, Chang CI (1999) Robust radial basis function neural network. IEEE Trans Syst Man Cybern B Cybern 29(6).
17. Leung H, Haykin S (1991) The complex backpropagation algorithm. IEEE Trans Signal Process 39(9).
18. Mel BW (1995) Information processing in dendritic tree. Neural Comput 6: 1013-1085.
19. Nitta T (1997) An extension of the back-propagation algorithm to complex numbers. Neural Netw 10(8): 1391-1415.
20. Nitta T (2000) An analysis of the fundamental structure of complex-valued neurons. Neural Process Lett 12: 239-246.
21. Piazza F, Benvenuto N (1992) On the complex back-propagation algorithm. IEEE Trans Signal Process 40(4): 967-969.