Kazuo Asakawa

Stock market prediction system with modular neural networks

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

Mobile robot control by a structured hierarchical neural network

A mobile robot whose behavior is controlled by a structured hierarchical neural network and its learning algorithm is presented. The robot has four wheels and moves about freely with two motors. Twelve sensors are used to monitor internal conditions and environmental changes. These sensor signals are presented to the input layer of the network, and the output is used as motor control signals. The network model is divided into two subnetworks connected to each other by short-term memory units used to process time-dependent data. A robot can be taught behaviors by changing the patterns presented to it. For example, a group of robots were taught to play a cops-and-robbers game. Through training, the robots learned behaviors such as capture and escape.<<ETX>>

A prototype of neuro-fuzzy cooperation system

The authors are developing a prototype of a neuro-fuzzy cooperation system that has the precision and learning ability of a neural network and is easy to understand like a fuzzy model. To help convert between neural and fuzzy systems, this system has a neural network with a structure corresponding to that of a fuzzy model. Knowledge acquired from experts was converted from a fuzzy system to a neural network. The neural network was applied to a target system and learned from data obtained during operation to enhance the accuracy of the model. Converting the neural network back into a fuzzy model helps explain the inner representation of the neural network. The model of the target system will be constructed as basic rules and will be improved step by step using a repetition of the fuzzy-neuro and neuro-fuzzy conversion.<<ETX>>

Neural networks in Japan

Applications of neural networks made their debut in Japan in 1988. Fujitsu used a neural network for robot control [21], and NEC used one to recognize printed characters [4]. Since then, neural network have been used extensively in Japan, and by 1990, they had been applied to commercial products and industrial systems

Initializing multilayer neural networks with fuzzy logic

The authors have developed a neuro-fuzzy system that initializes a structured neural network with a fuzzy logic system that is based on expert knowledge. The neural network gains precision through adaptive learning, and is then converted back into a set of fuzzy rules for ease of understanding. The authors discuss a bond rating application that uses this process. The system produces bond ratings that closely match those of human experts, and has higher precision and better generalization than a simple three-layer neural network. The system also makes it easier to understand the neural systems reasoning by translating it into the fuzzy inference format.<<ETX>>

Mobile robot control by neural networks using self-supervised learning

A reinforcement learning algorithm based on supervised learning is described. It uses associative search to discover and learn actions that make the system perform a desired task. One problem with associative search is that the systems actions are often inconsistent. In the searching process, the systems actions are always decided stochastically, so the system cannot perform learned actions more than once, even if they have been determined to be suitable actions for the desired task. To solve this problem, a neural network that can predict an evaluation of an action and control the influence of the stochastic element is used. Results from computer simulations using the algorithms to control a mobile robot are described. >

Inverse modeling of dynamical system-network architecture with identification network and adaptation network

The authors describe a neural network architecture enabling inverse modeling of a nonlinear dynamical system. It consists of two neural networks, a system identification network and an adaptation network. The effectiveness of the proposed network architecture is examined by applying it to a digital mobile communication adaptive equalizer. In digital mobile communication, the problem of multipath fading caused by vehicular movement becomes a nonlinear dynamical system. The proposed network architecture is able to obtain an inverse model of such transmission channels and attain equalization of signal distortions. The performance of the proposed adaptive equalizer was evaluated by computer simulation. The bit error rate was found to decrease by one-third compared to that without an equalizer.<<ETX>>

Behaviour control for a mobile robot by a structured neural network

We have been researching ways to use neurocomputers that have highly parallel data processing and learning functions for robot control. In this paper, a structured network model for robot control and its learning algorithm are presented. There are three requirements for the robots: (1) The robot must be easy to control but the neural network must be sophisticated enough to handle multiple sensor input. (2) The robot must be able to learn easily. (3) The robot must be able to adjust its own actions. We have developed a new mobile mechanism, created a network model, and increased the network learning speed. Sensor signals from the robot are input to the neural network. The network outputs a certain reaction pattern in response to the sensor input. Then the reaction is refined to an ideal one using training patterns. A robot can change its reaction pattern by changing the training pattern. We created two robots with different action patterns: one chases other robots and the other runs away from other robots....

Progress of ubiquitous information services and keeping their security by biometrics authentication

With progress of both fixed and mobile networks, various ubiquitous information services have been brought up to apply in actual business scene. In fact, it becomes possible to receive and send information in various access points not only in offices but also in cars through sophisticated ubiquitous terminals such as PDA, cellular phone, mobile PC and so on. Moreover, the spread of RFID-tag application to household commodities will accelerate the popularization of ubiquitous services. Every ubiquitous terminal will have RFID reader/writer on board in near future. By using the ubiquitous information service environment, variety of services such as e-government, e-bank, e-commerce, Intelligent Transport Systems service that usually require customized services for each customer has already been provided. However, high-tech crimes such as skimming and phishing are increasing day by day inversely increment of its convenience. Such high-tech crimes were happened in worldwide actually. It is impossible to keep security by 4 or more digits PIN code any longer. Biometrics authentication is promising to protect against high-tech crimes in the present situation. I will give an overview of current ubiquitous information services, focusing on some of actual services in keeping security by biometrics authentication.

Robot assembly of precision parts using tactile sensors

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