scholarly journals Adaptive Network Fuzzy Inference Systems for Classification in a Brain Computer Interface

10.5772/55989 ◽  
2013 ◽  
Author(s):  
Vahid Asadpour ◽  
Mohammd Reza ◽  
Reza Fazel-Rezai
Keyword(s):  
Fuzzy Inference ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Fuzzy Inference Systems ◽  
Adaptive Network ◽  
Inference Systems

Evaluate Fabric Wrinkle Grade Based on Subtractive Clustering Adaptive Network Fuzzy Inference Systems

2011 ◽  
Vol 332-334 ◽  
pp. 1505-1510
Author(s):  
Xiao Bo Yang
Keyword(s):  
Network Inference ◽  
Clustering Algorithm ◽  
Fuzzy Inference ◽  
Real Data ◽  
Subtractive Clustering ◽  
Fuzzy Inference Systems ◽  
Adaptive Network ◽  
Inference System ◽  
Fuzzy Neural ◽  
Inference Systems

In this paper, a new method of subtractive clustering adaptive network fuzzy inference systems is proposed to assess degree of wrinkle in the fabric. The clustering center can be gotten through subtractive clustering algorithm, which is the base to set up adaptive network inference systems. Firstly, subtractive clustering algorithm is used to confirm the structure of fuzzy neural network, then, fuzzy inference system is used to process pattern recognition. Finally, four kinds of fabric wrinkle feature parameters are used to verify the results on real fabric. The results show the applicability of the proposed method to real data.

scholarly journals Identification of ERD using Fuzzy Inference Systems for Brain-Computer Interface

2011 ◽  
Vol 6 (3) ◽  
pp. 403 ◽  
Author(s):  
Ioan Dzitac ◽  
Tiberiu Vesselényi ◽  
Radu Cătălin Ţarcă
Keyword(s):  
Fuzzy Inference ◽  
Brain Activity ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Eeg Signals ◽  
Inference System ◽  
Eeg Data ◽  
Recent Developments ◽  
Potential Activity ◽  
Inference Systems

A Brain-Computer Interface uses measurements of scalp electric potential (electroencephalography - EEG) reflecting brain activity, to communicate with external devices. Recent developments in electronics and computer sciences have enabled applications that may help users with disabilities and also to develop new types of Human Machine Interfaces. By producing modifications in their brain potential activity, the users can perform control of different devices. In order to perform actions, this EEG signals must be processed with proper algorithms. Our approach is based on a fuzzy inference system used to produce sharp control states from noisy EEG data.

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