Satoshi Sekine

Application of fuzzy neural network control to automatic train operation and tuning of its control rules

We have proposed two-degree-of-freedom fuzzy neural network control systems. It has a hierarchical structure of two sets of control knowledge, thus it is easy to extract and refine fuzzy rules before and after the operation has started, and the number of fuzzy rules is reduced. This paper shows an example application of automatic train operation and presents a rule tuning method.<<ETX>>

Application of fuzzy neural network control to automatic train operation

A transit system is subject to widely varying external conditions, such as weather, the time of day, etc. Different situations imply different control purposes and the varying conditions induce fluctuating dynamic characteristics. Therefore it is difficult to automatically control the vehicle satisfactorily. We apply a two-degree-of-freedom fuzzy neural network control system to automate the vehicle operation. The controllers function is to accurately stop the vehicle at a station. We investigate the controllers performance with acceleration error due to changing dynamic characteristics and with a control purpose transition from velocity control to position control.<<ETX>>

Modeling Methodology for Systems with Ambiguity and Uncertainty

Abstract This paper discusses a modeling methodology for systems that contain ambiguity or uncertainty. Models built by the methodology are expected to handle the ambiguity and adapt to the uncertainty. The methodology is simple and versatile so that it can be applied to the description of various system architectures; e.g. multi-layered perceptron, fuzzy neural networks and so forth. A description language and a design tool that employ the method are also introduced.