We have been developing exoskeletal robots in order to support the motion of physically weak persons such as elderly persons or handicapped persons. In our previous research, the 2-DOF exoskeletal robots for shoulder motion support and its control method have been developed since the shoulder motion is especially important for people to take care of themselves in everyday life. In this paper, we propose intelligent interface, which realizes the fuzzy-neuro controller adjustment in accordance with the human subject's arm posture, in order to effectively control the 2-DOF exoskeletal robot for shoulder motion support. The intelligent interface is realized by applying a neural network. The effectiveness of the proposed intelligent interface of the exoskeletal robot has been evaluated by experiment.

The exoskeletal robots are expected to support the daily activities of physically weak persons such as elderly persons or handicapped persons in these days. In our previous research, the exoskeletal robots for elbow or shoulder motion support have been developed since the elbow and shoulder motion is especially important for people to perform tasks in everyday life. In this paper, we present a mechanism of the moving center of rotation of the shoulder joint of the exoskeletal robot for human shoulder motion support in order to fit the center of rotation of the robot shoulder joint to that of the physiological human shoulder joint during the shoulder motion. The effectiveness of the proposed mechanism of the robot has been evaluated by experiment.

This paper deals with statistical mechanical characteristics of fuzzy clustering regularized with the fuzzy entropy, especially a "phase transition" phenomena. We derive the critical temperature at which the phase transition occurs in the fuzzy clustering. Then, by combining the cluster divisions by phase transitions with an adequate division termination condition, we propose a hierarchical fuzzy clustering method which can determine a number of clusters. Numerical experiment shows that the proposed method works properly.

This paper is dealing with the fuzzy clustering method which combines the deterministic annealing approach and the fuzzy entropy. Regularizing fuzzy c-Means with the fuzzy entropy, we obtain the Fermi-Dirac type distribution function as the membership function. Then we examine the meanings of Fermi-Dirac function and the fuzzy entropy from the statistical mechanical point of view, and show that the mechanism of this fuzzy clustering is no other than the Fermi-Dirac statistics. Also we propose the algorithm which performs deterministic annealing of the membership function. Numerical experiment demonstrates that the proposed method can cluster a given set of data points properly.