Title :
Analysis and application of self-organizing sensory mapping
Author :
Lo, Z.-P. ; Fujita, M. ; Bavarian, B.
Author_Institution :
Dept. of Electr. & Comput. Eng., California Univ., Irvine, CA, USA
Abstract :
The authors present a mathematical analysis of self-organizing sensory mapping which was first proposed by Kohonen. It is shown that using the sensory mapping learning rule is equivalent to minimizing an energy function of the network outlined. The underlying work of Kohonen and the topology preserving networks are reviewed, along with the algorithm for implementing the network. The concept of the energy of a network is defined and a detailed analysis of the mapping algorithm is outlined
Keywords :
learning systems; network topology; neural nets; self-adjusting systems; Kohonen; energy function minimisation; learning systems; neural nets; self-organizing sensory mapping; sensory mapping learning rule; topology preserving networks; Application software; Convergence; Data mining; Feature extraction; Mathematical analysis; Nervous system; Network topology; Neurons; Signal mapping; Vector quantization;
Conference_Titel :
Systems, Man, and Cybernetics, 1991. 'Decision Aiding for Complex Systems, Conference Proceedings., 1991 IEEE International Conference on
Conference_Location :
Charlottesville, VA
Print_ISBN :
0-7803-0233-8
DOI :
10.1109/ICSMC.1991.169918
