Bioassay Using Daphnia Daphnia Trajectories

金沢大学大学院自然科学研究科Scedule:17-18 March 2003, Vemue: Kanazawa, Japan, Kanazawa Citymonde Hotel, Project Leader : Hayakawa, Kazuichi, Symposium Secretariat: XO kamata, Naoto, Edited by:Kamata, Naoto

Study of Intelligent Impedance Control Using a Fuzzy Neural Network

             This paper presents study of adaptive force control that takes into account object characteristics using a fuzzy neural network. This study applies fuzzy theory to position control and force control, similar to those actually implemented by industrial robots, to enable automatic establishment of optimum parameters for different environments and autonomous, flexible motion

Design of Fuzzy Neural Network Based Multi-Variables Controller for Manipulators

This paper proposes a robust multivariable control design by intelligent control that uses a fuzzy neural network by producing robustness capable of automatically controlling gain against a conventional, fixed PID control system. This structural feature of the proposed controller forms a nonlinear deviation compensator using fuzzy neural networks. Therefore, in multidimensionality the inverse dynamic model portion of the control law is referred to as a linearizing and decoupling control law. This method uses a control law where parameter response leads to critical damping and adaptive changes in gain according to time, making it possible to decouple mutual interference in each multivariable system

STUDY ON AN AUTONOMOUS INTELLIGENT CONTROL SYSTEM FOR AN ARM ROBOT WITH SPEECH RECOGNITION/SYNTHESIS THAT DOES NOT REQUIRE TEACHING

This paper describes research conducted on an advanced, autonomous intelligent-type arm robot that does not require teaching, and that can execute spoken work commands and work reports through speech synthesis. This intelligent arm robot has been developed with the ability to recognize and determine the shape, color, and position of an object, and subsequently grasp the object