Multilayer feedforward potential function network

作者: Lee Sukhan , Kil

DOI: 10.1109/ICNN.1988.23844

关键词: Artificial neural networkPattern recognitionGaussianGradient descentFeed forwardFunction (mathematics)Artificial intelligenceComputer scienceSet (abstract data type)Domain (mathematical analysis)Pattern recognition (psychology)Algorithm

摘要: The authors present a multilayer feedforward network, called the Gaussian potential function network (GPFN), performing association or classification based on set of potentially fields synthesized over domain input space by number units (GPFUs). A GPFU as basic component GPFN is designed to generate form field. weighted summation generated suitable GPFUs provides an arbitrary shape field space. also detailed learning algorithm for GPFN. Learning consists determination minimally necessary and adjustment locations shapes individual defined well weights. control effective radius GPFUs, while parameter gradient descent procedure. >

参考文章(10)
D. E. Rumelhart, G. E. Hinton, R. J. Williams, Learning internal representations by error propagation Parallel distributed processing: explorations in the microstructure of cognition, vol. 1. ,vol. 1, pp. 318- 362 ,(1986)
Peter E. Hart, Richard O. Duda, Pattern classification and scene analysis A Wiley-Interscience Publication. ,(1973)
D. E. Rumelhart, Learning Internal Representations by Error Propagation, Parallel Distributed Processing Explorations in the Microstructure of Cognition. pp. 318- 362 ,(1986)
M. A. Aizerman, Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning Automation and Remote Control. ,vol. 25, pp. 821- 837 ,(1964)
James S. Albus, Mechanisms of planning and problem solving in the brain Mathematical Biosciences. ,vol. 45, pp. 247- 293 ,(1979) , 10.1016/0025-5564(79)90063-4
R. Lippmann, An introduction to computing with neural nets IEEE ASSP Magazine. ,vol. 4, pp. 4- 22 ,(1987) , 10.1109/MASSP.1987.1165576
Bart Kosko, Adaptive bidirectional associative memories Applied Optics. ,vol. 26, pp. 4947- 4960 ,(1987) , 10.1364/AO.26.004947
J. J. Hopfield, Neurons with graded response have collective computational properties like those of two-state neurons. Proceedings of the National Academy of Sciences of the United States of America. ,vol. 81, pp. 3088- 3092 ,(1984) , 10.1073/PNAS.81.10.3088
J. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities Proceedings of the National Academy of Sciences of the United States of America. ,vol. 79, pp. 2554- 2558 ,(1982) , 10.1073/PNAS.79.8.2554