To synthesize dynamics with nonlinearities or distributed parameter structures in a simple manner for process controlling and diagnosis, we propose a knowledge-based modelling method. In this method, the structures of the process and the internal relations between the states are presented in a petri-net like data structure and the relations between the states are described with rules of mathematical procedure and linguistic logics (production rules or fuzzy inference rules). To illustrate this method, a model of a once-through type evaporator is presented. Complex characteristics can be described mainly with simple conservation laws.
A video signal transmission system of platform monitoring pictures up to running trains using infrared beam has been made practicable. Two light transmitters were placed wayside so that light receiver may catch the infrared beams sent out by the transmitters in sequence over required service area. The system is well proof against rain, snow and fog, and clear as well as stable pictures without ghost images are obtained.
A number of trajectory planning algorithms exist for calculating the joint positions, velocities, and torques which will drive a robotic manipulator along a given geometric path. This paper presents a learning method for optimal trajectory planning of robotic manipulators of which all joints are rotational. When the start and end points of end effector are given in the Cartesian coordinates, the Fourier coefficients representing each joint velocity and interval of motion which specify the optimal trajectory in joint coordinates are searched by using the method of steepest descent. In the searching process, a learning algorithm based on the idea of linear approximation and utilization of the information on the known optimal trajectories is introduced. As numerical examples, the trajectories of two-link manipulator are simulated and the learning effect is confirmed.
This paper deals with mathematical modeling of fish behavior in a water tank. The model presented in our earlier papers describes the behavior of each individual in a school. Then, it is applicable to the case of small school. In this paper, an aggregation model is presented on the basis of the following assumption : The schooling behavior of fish can be decomposed into two components. One is the motion of a representative of the school. The other is the variation of the school size. Since the model for the representative was presented previously, the present study is directed toward the model for the school size. An autoregressive model is presented for this purpose. The model order is determined by minimizing the AIC. The unknown parameters are estimated by applying the least squares algorithm. The validity of the autoregressive model is examined by a residual whiteness test and a simulation study.