A discussion is presented of a buying- and selling-time prediction system for stocks on the Tokyo Stock Exchange and the analysis of internal representation. The system is based on modular neural networks. The authors developed a number of learning algorithms and prediction methods for the TOPIX (Tokyo Stock Exchange Prices Indexes) prediction system. The prediction system achieved accurate predictions, and the simulation on stocks trading showed an excellent profit

A mobile robot with behavior controlled by a neural network, and its learning method are presented. The robot has four wheels and travels with two motors; it has twelve sensors for detecting internal conditions and changes in environment. The sensor signals are fed into the input layer of the network, and the network outputs motor control commands. The network model is divided into two subnetworks connected to each other with a short-term memory to process time-dependent data. The robot can learn various habits by changing the patterns to be taught. One example, the habits to play a cops-and-robbers game, was taught. Through training, the robots learned habits such as capture and escape.>