Title :
A learning scheme for bipartite recurrent networks and its performance
Author :
Kumazawa, Itsuo ; Fukuda, Mitsuyoshi
Author_Institution :
Dept. of Comput. Sci., Tokyo Inst. of Technol., Japan
Abstract :
A new learning scheme specialized to recurrent neural networks with the bipartite topology is proposed. The scheme is expected to have better convergence than the general Boltzmann machine learning. This improvement results from the restricted form of the network topology and an energy form devised to have a dominant global minimum. Compared to the recurrent backpropagation algorithm, the scheme is simple, more suitable for hardware realization and its probabilistic nature reduces the effect of spurious local minima. The performance of the scheme is partly demonstrated by simulations of associative memory and compared with the general Boltzmann machine learning
Keywords :
Boltzmann machines; backpropagation; content-addressable storage; learning (artificial intelligence); recurrent neural nets; uncertainty handling; Boltzmann machine learning; associative memory; bipartite recurrent networks; bipartite topology; convergence; dominant global minimum; energy form; hardware realization; learning scheme; network topology; probabilistic nature; recurrent backpropagation algorithm; recurrent neural networks; simulations; Associative memory; Backpropagation; Computer science; Machine learning; Network topology; Neural networks; Optical computing; Optical fiber networks; Optical interconnections; Optical network units;
Conference_Titel :
Artificial Neural Networks and Expert Systems, 1993. Proceedings., First New Zealand International Two-Stream Conference on
Conference_Location :
Dunedin
Print_ISBN :
0-8186-4260-2
DOI :
10.1109/ANNES.1993.323088
