In this paper we derive a subspace-based state-space identification method for continuous-time systems. By using δ-operator we transform the continuous-time system to a discrete-time δ-operator state-space model which converges to the original continuous-time model as the sampling period goes to zero. Then we obtain the estimates of system matrices by applying some well-known subspace identification methods such as MOESP to the discrete-time δ-operator state-space model. We give two numerical examples to show the effectiveness of the proposed method. A beneficial comparison with the other methods such as q-, ω-and λ-operator methods is included.

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This paper is concerned with the boolean quadratic optimization problem. We formulate and analyze a class of non-convex relaxation problems which includes the relaxation problem with complex variables and the SDP relaxation problem as special cases. The effects of expanding the parameter space of decision variables to a space of hypercomplex number are investigated. It is shown that for any instance of problem data the relaxation problem in complex variable is the strongest non-convex relaxation among the relaxations (in our formulation) under the condition of having “monotonically decreasing path” which connects any two feasible solutions of the original problem.

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In previous studies of human information processing, the visual separate system and the auditory separate system have been frequently studied. However, human related characteristic between visual and auditory systems has not been investigated substantially. If the mechanisms of human visual and auditory parallel information processing are elucidated, the finding will contribute to improving technical systems. In our previous studies, human related characteristic between visual search and speech perception is studied. The results showed the mutual effect between visual information processing and auditory information processing in the visual and the auditory parallel information processing. However, to clarify the cause of many car accidents lies in the use of car-phone on driving, it is necessary to use the same kind of visual and auditory stimuli in psychological experiments. In this paper, characteristic of reaction time in parallel information processing of human visual and auditory system is measured by using the same kind of stimuli. The experimental results show the factors that influence the visual and the auditory reaction time are the difficulty of the visual and the auditory stimuli and the timing of the visual and the auditory inputs. The experimental results also suggest that one of the causes of car accidents (to use car-phone while driving) is that the human visual information processing is affected by the auditory information.

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In this paper, we derive the PDS feedback control law for a two-link flexible arm based on a distributed parameter model. The PDS controller consists of the PD feedback and a strain feedback (S). As the PDS control law is a static direct sensor output feedback, it is simple, robust and easy to implement. On the basis of the distributed parameter model, we prove the asymptotic stability of the closed-loop system for the PDS feedback control law by using the Lyapunov method and the Invariance Principle. Experimental results are shown.

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Genetic algorithms (GAs) are the adaptation methods broadly applicable to many classes of problems. Adaptation to changing environments is one of the important classes of such problems. Continuous search for the solutions by the GA is the fundamental mechanism for adaptation, and therefore to avoid convergence by maintaining the diversity is an intrinsic requirement for successful search. The authors have proposed to utilize the thermodynamical genetic algorithms (TDGA), a genetic algorithm which maintains the diversity of the population by evaluating its entropy, for the problem of adaptation to changing environments. However, if the environmental change has a recurrent nature, a memory-based approach, i.e., to memorize the results of past adaptation and to retrieve them as candidates for the solution, will be a smart strategy. In the present paper, the authors combine the memory-based approach with TDGA as an adaptation algorithm to changing environments. The adaptation ability of the proposed method is verified by computer simulations taking recurrently varying knapsack problems as examples.

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