Neutral networks, which are landscapes containing neighboring points of equal fitness, have attracted much research interest in recent years. In this paper, we conduct a series of computer simulations to investigate the effect of an error threshold on the moving speed of a population as well as a variable mutation rate strategy against ruggedness. Two kinds of GA are adopted. One is the simple GA where the mutation rate is constant, and the other is the operon-GA whose effective mutation rate is changing at each locus independently according to the history of the genetic search. The results demonstrate that the moving speed of a population is correlated with the selection pressure as well as the mutation rate. The variable mutation rate strategy is beneficial in the cases of the simplest test function and complex test functions. This tendency becomes clearer with the increase of ruggedness in the test functions.
This paper is concerned with an approach to design of output-feedback gain-scheduling controllers via solving LMIs, based on descriptor representation of linear parameter varying (LPV) systems. Algebraic constraints of the descriptor form can reduce a state space LPV model with complex functions of scheduling parameters to a descriptor LPV model with simplified functions of the parameters. By change-of-variables technique, LMI conditions are derived for design of output feedback gain-scheduling controllers for descriptor LPV systems. A numerical example is provided to illustrate the proposed method.
In recent years, an aim of conveyance systems is to provide suitably for various demands and to treat a wide variety of products, therefore autonomous decentralization is needed in conveyance system. Because conventional conveyance systems are designed for efficiency and it takes much time and resource to change specification or cope with breakdowns. Many research works on autonomous distributed conveyance systems have been proposed so far. However, it is difficult to use conventional methods in dynamic environment with environmental change. Therefore, this paper proposes a strategy determination method by statistical technique. In this method, each robot estimates an environmental state with queueing theory in order to decide strategy autonomously. Moreover, each robot recognizes environmental change and expire the stored environmental state in order to decide a new strategy suitable for a new environment.
In this paper, a new sensorless Interior Permanent Magnet Synchronous Motor (IPMSM) drive method with Extended Kalman Filtering (EKF) estimation of speed and rotor position is proposed. This method is on the basis of the sensorless Surface Permanent Magnet Synchronous Motor (SPMSM) drive  that can be applied to motors with no salient pole and is developed so as to be able to be applied to motors with salient pole such as IPMSM. The feature of the method is that rotor position estimation error does not occur even if motor current or motor voltage changes. Therefore, the method can be applied with arbitrary motor voltage waveform and, consequently, the method is suitable for sensorless IPMSM drive. Numerical simulations are illustrated to verify the effect of the method.