The Hopfield neural network for optimization problems often falls into local minima. To escape from the local minima, the neuron unit in the neural network is modified to become an oscillatory unit by adding a simple self-feedback circuit. By combining the oscillatory unit with an energy-value extraction circuit, an oscillatory neural network is constructed. The network can repeatedly extract solutions, and can simultaneously evaluate them. In this paper, the network is applied to four NP-complete problems to demonstrate its generality and efficiency. The network can solve each problem and can obtain better solutions than the original Hopfield neural network and simple algorithms.

This paper gives an overview of research activities on high performance databases in Japan. It focuses on parallel algorithms for relational databases and data mining, parallel approaches for object-oriented databases, and parallel disk systems. Studies surveyed in this paper are carried out mainly by database researchers in Japanese universities under the Grant-in-Aid for Scientific Research (1996-1998).