Titlebook: Aspects of Soft Computing, Intelligent Robotics and Control; János Fodor,Janusz Kacprzyk Book 2009 Springer-Verlag Berlin Heidelberg 2009

阻塞

https://doi.org/10.1007/978-3-642-58252-3ave proposed a novel method named Passive Dynamic Autonomous Control (PDAC) that realized not only a bipedal walk but also a quadruped walk. In this paper, the PDAC is also applied to a brachiation motion on the irregular ladder; then the proposed controller is validated by the experiment.

客觀

PDAC-Based Brachiating Control of the Multi-Locomotion Robotave proposed a novel method named Passive Dynamic Autonomous Control (PDAC) that realized not only a bipedal walk but also a quadruped walk. In this paper, the PDAC is also applied to a brachiation motion on the irregular ladder; then the proposed controller is validated by the experiment.

nautical

1860-949X and tolerance for imprecision and uncertainty in traditionally intractable problems, has become very effective and popular especially because of the synergy derived from its components. The integration of constituent technologies provides complementary methods that allow developing flexible computin

感染

Wolfgang Kilger,Jochen R. Pampel,Kurt Vikasas a generalization of the classical convolution is given, with many important special cases. Further applications of pseudo-additive measures, pseudo-integrals and pseudo-convolutions are given in the theory of fuzzy numbers, information theory, system theory and control theory, and probabilistic metric spaces.

deadlock

hypotension

鋼盔

A Simple Method for Generating Smooth Robot Arm Motionrmulated in the .and employs a . base function in the phase space. Hence the name: Harmonic Motion Generator (HMG). Performance capabilities of HMG are illustrated, including its combination with force-moment sensor based active compliance control and with task space (Cartesian space) position servo control.

共同生活

Neural Networks or Fuzzy Systemsthis way neural network loses its generalization property. Performances of both fuzzy systems and neural networks are compared leading to the conclusion that neural networks can produce much more accurate nonlinear mapping and they are simple to implement. At the end of the presentation several concepts of neuro-fuzzy systems are compared.