scholarly journals Self-Learning Visual Servoing of Robot Manipulator Using Explanation-Based Fuzzy Neural Networks and Q-Learning

2014 ◽  
Vol 78 (1) ◽  
pp. 83-104 ◽  
Author(s):  
Mehdi Sadeghzadeh ◽  
David Calvert ◽  
Hussein A. Abdullah
Keyword(s):  
Neural Networks ◽  
Visual Servoing ◽  
Robot Manipulator ◽  
Fuzzy Neural Networks ◽  
Q Learning ◽  
Fuzzy Neural ◽  
Self Learning

Diagnose Expert System of Engine Based on Fuzzy Neural Network

2012 ◽  
Vol 588-589 ◽  
pp. 1472-1475
Author(s):  
Miao Tian
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Expert System ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural Networks ◽  
Learning Ability ◽  
Fuzzy Neural ◽  
Enhance Efficiency ◽  
Self Learning

Engine has a high chance of failure, it usually accounts for about 40% of vehicle failures. Study expert system of engine fault diagnosises that it can locate fault timely and accurately, and enhance efficiency. However, the traditional expert system has shortcomings so as inefficient inference and poor self-learning capability. The fuzzy logic and traditional neural networks are combined to form fuzzy neural networks, they are established a model of fuzzy neural network (FNN) of fault diagnosis, and that the model is applied to engine fault diagnosis, complementary advantages, to effectively enhance efficiency of inference and self-learning ability, its performance is higher than the traditional BP network.

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