Neural Network Based Fuzzy Systems Design

2003 ◽  
pp. 111-141 ◽  
Author(s):  
Yaochu Jin
Keyword(s):  
Neural Network ◽  
Fuzzy Systems ◽  
Systems Design

Type-2 Fuzzy Interface for Artificial Neural Network

2010 ◽  
pp. 72-92 ◽  
Author(s):  
Priti Srinivas Sajja
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Systems ◽  
Selection Process ◽  
User Friendliness ◽  
Artificial Neural ◽  
Generalized Knowledge ◽  
Artificial Neural Network Ann ◽  
Self Learning

Artificial Neural Network (ANN) based systems are bio-inspired mechanisms for intelligent decision support with capabilities to learn generalized knowledge from the large amount of data and offers high degree of self-learning. However, the knowledge in such ANN system is stored in the generalized connection between neurons in implicit fashion, which does not help in providing proper explanation and reasoning to users of the system and results in low level of user friendliness. On the other hand, fuzzy systems are very user friendly, represent knowledge in highly readable form and provide friendly justification to users as knowledge is stored explicitly in the system. Type-2 fuzzy systems are one step ahead while computing with words in comparison to typical fuzzy systems. This chapter introduces a generic framework of type-2 fuzzy interface to an ANN system for course selection process. Resulting neuro-fuzzy system offers advantages of self-learning and implicit knowledge representation along with the utmost user friendliness and explicit justification.

scholarly journals General Parameter Radial Basis Function Neural Network Based Adaptive Fuzzy Systems

1998 ◽  
Vol 10 (5) ◽  
pp. 973-982 ◽  
Author(s):  
Daouren F. AKHMETOV ◽  
Yasuhiko DOTE ◽  
Yukinori SUZUKI ◽  
Sato SAGA
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Fuzzy Systems ◽  
Adaptive Fuzzy ◽  
Radial Basis ◽  
General Parameter

A Hierarchical Fuzzy-Neural Multi-Model Applied in Nonlinear Systems Identification and Control

2013 ◽  
Vol 347-350 ◽  
pp. 617-622
Author(s):  
Feng Ye ◽  
Wei Min Qi
Keyword(s):  
Neural Network ◽  
Control System ◽  
Recurrent Neural Network ◽  
Systems Design ◽  
Membership Functions ◽  
Neural Network Models ◽  
Trajectory Tracking Control ◽  
Fuzzy Neural ◽  
Systems Identification ◽  
And Control

The paper brings forward a hierarchical fuzzy-neural multi-model with recurrent neural procedural consequent par for systems identification, states estimation and adaptive control of complex nonlinear plants. The parameters and states of the local recurrent neural network models are used for a local direct and indirect adaptive trajectory tracking control systems design. The designed local control laws are coordinated by a fuzzy rule-based control system. The upper level defuzzyfication is performed by a recurrent neural network. The applicability of the proposed intelligent control system is confirmed by simulation examples and by a DC-motor identification and control experimental results. Two main cases of a reference and plant output fuzzyfication are considereda two membership functions without overlapping and a three membership functions with overlapping. In both cases a good convergent results are obtained.

New stability criteria for a class of systems arising in neural network control and fuzzy systems

2000 ◽  
Vol 214 (1) ◽  
pp. 1-8
Author(s):  
Y Fang ◽  
T G Kincaid ◽  
S Li
Keyword(s):  
Neural Network ◽  
Stability Criterion ◽  
Fuzzy Systems ◽  
Neural Control ◽  
Network Control ◽  
Neural Network Control ◽  
Stability Criteria ◽  
Simple Derivation ◽  
The Stability

In this paper, the stability of a class of systems arising from neural control and fuzzy systems is studied. A new unifying stability criterion is presented using a very simple derivation. This result generalizes some previous results; some easily testable conditions are obtained.

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