Fuzzy neural networks for estimation and fuzzy controller design: simulation study for a pulp batch digester

2000 ◽  
Vol 10 (1) ◽  
pp. 35-41 ◽  
Author(s):  
K. Belarbi ◽  
K. Bettou ◽  
A. Mezaache
Keyword(s):  
Neural Networks ◽  
Simulation Study ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Batch Digester

scholarly journals INTELLIGENT VEHICLE PARKING USING FUZZY-NEURAL NETWORKS

2013 ◽  
pp. 14-18
Author(s):  
Subhrajit Pradhan ◽  
Srikant Patnaik ◽  
Sukanta Kumar Tulo
Keyword(s):  
Neural Networks ◽  
Fuzzy Controller ◽  
Fuzzy Neural Networks ◽  
Intelligent Vehicle ◽  
Practical Implementation ◽  
Positioning Error ◽  
Neural Controller ◽  
Different Types ◽  
Fuzzy Neural ◽  
Refined Version

This paper analyzes the performance and practical implementation of fuzzy-neural networks for the autonomous motion of mobile robots. The designed fuzzy-neural controller is a refined version of a conventional fuzzy controller, and was trained to optimize a given cost function minimizing positioning error. It was found that the mobile robot with fuzzyneural controller presents good positioning and tracking performance for different types of desired trajectories. It was verified by computer simulation as well as experimentally using a laboratory-scale car-like robot model.

Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Network Structure ◽  
Fuzzy Systems ◽  
Fuzzy Neural Networks ◽  
Cnc Machining ◽  
Machining Process ◽  
Modeling Process ◽  
Fuzzy Neural ◽  
Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

Hotbox level detection of railway vehicle using fuzzy neural networks

2013 ◽  
Vol 33 (9) ◽  
pp. 2566-2569 ◽  
Author(s):  
Zhuanling CUI ◽  
Guoning LI ◽  
Sen LIN
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Railway Vehicle ◽  
Fuzzy Neural

scholarly journals Affine Memory Control for Synchronization of Delayed Fuzzy Neural Networks

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Wookyong Kwon ◽  
Yongsik Jin ◽  
Dongyeop Kang ◽  
Sangmoon Lee
Keyword(s):  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Memory Control
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