Fuzzification using Approximation with Fuzzy Controllers to Extract Structure and Motion Environments in Dynamic Humanoid Robotic System from the Opted and Forced Uncertainty by Quantization in Nonmonotone Neural Networks.

ASHOK KUMAR, RAMADOSS (2017) Fuzzification using Approximation with Fuzzy Controllers to Extract Structure and Motion Environments in Dynamic Humanoid Robotic System from the Opted and Forced Uncertainty by Quantization in Nonmonotone Neural Networks. In: Fifth International Conference on Advances in Computing, Communication and Information Technology - CCIT 2017, 02-03 September, 2017, Zurich, Switzerland.

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Abstract

Fuzzy controllers are special expert systems .Each employs a knowledge base,expressed in terms of relevant fuzzy inference rules, and an appropriate inference engine to solve this control problem which vary substantially according to the nature of the control problems in this research the control problem is a complex task in this robotics research which require a multitude of coordinated actions such a s maintaining a prescribed state of single variable ,which are capable of utilizing kn owledge elici ted from human operators extended to dynamic robotic system with nonmonotone neural networks.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Fuzzification, Fuzzy controllers, Expert System, Fuzzy sets, Quantization, Nonmonotone, Dynamic Robotic system
Depositing User: Mr. John Steve
Date Deposited: 11 Mar 2019 08:30
Last Modified: 11 Mar 2019 08:30
URI: http://publications.theired.org/id/eprint/381

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