Title :
Successive learning in chaotic neural network
Author :
Osana, Yuko ; Hagiwara, Masafumi
Author_Institution :
Keio Univ., Yokohama, Japan
Abstract :
In this paper, we propose a successive learning method in a chaotic neural network using a continuous pattern input. It can distinguish an unknown pattern from the stored known patterns and learn the unknown pattern successively. In the proposed model, it makes use of the difference in the response to the input pattern in order to distinguish an unknown pattern from the stored known patterns. When an input pattern is regarded as an unknown pattern, the pattern is memorized. Furthermore, it can estimate and learn a correct pattern from a noisy unknown pattern or an incomplete unknown pattern by considering the temporal summation of the continuous pattern input. In addition, similarity to the physiological facts in the olfactory bulb of a rabbit found by Freeman (1991) is observed in the behavior of the proposed model. A series of computer simulations shows the effectiveness of the proposed model
Keywords :
chaos; learning (artificial intelligence); neural nets; noise; pattern recognition; chaotic neural network; computer simulations; continuous pattern input; incomplete unknown pattern; noisy unknown pattern; olfactory bulb; rabbit; successive learning; temporal summation; Associative memory; Biological neural networks; Chaos; Humans; Intelligent networks; Learning systems; Neural networks; Neurons; Olfactory; Rabbits;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.686000