A flexible rotor system has a coupled structure with a flexible rotor and a drive/effector. We have examined bifurcation phenomena of the system by an experiment and a simulation. As a result, it has been clarified that the jump phenomena occur in the rotating speed under mechanical resonance. This paper shows a sufficient condition for stabilization of an M-D-K-system which has continuously bounded time-varying coefficients, and proposes a control method which eliminates the jump phenomena in the rotating speed by using torque. The numerical discussion is also given.
In this paper, we discuss unsupervised learning for a temporally precise sequence. A network of leaky integrate-and-fire neurons is able to learn a fine spatio-temporal pattern, when the neurons are provided many excitatory random inputs. This unsupervised learning is achieved by selecting appropriate connections in the network. After learning, the trained network works as an associative memory with high temporal precision. Namely, it distinguishes the training sequence through filtering the disarranged sequence according to its correlation value with the training sequence.
A new design method for a traversal track formation in the linear-motor (LIM) driven transportation system has beeen developed. The object of the design is to minimize the LIM number assuring smoothness of vehicle speed control along the whole track. Conventionally, the design was made by human experts based on their experiments. Therefore, the quality of the design depends on their expert skill. To automate and standardize the design procedure, Cased Based Reasoning (CBR) technology was applied. In addition, Genetic Algorithms were used to update the CBR.
A programing language to generate GUI for industrial analyzers is proposed. We use a keyword “State” which corresponds to the State of an analyzer. This “State” represents the status of a program, and enables us to compose a program in the same structure as the specification or the manual of the analyzer.
This paper presents a novel approach for system identification of continuous-time stochastic state space models from random input-output continuous data. The approach is based on the introduction of the random distribution theory in describing the (higher) time derivatives of stochastic processes, and the input-output algebraic relationship is derived which is treated in the time-domain. The efficacy of the approach proposed is examined by comparing with other approaches employing the filters.