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» On the Use of Evidence in Neural Networks
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IJCNN
2000
IEEE
15 years 6 months ago
Neural Networks for Novelty Detection in Airframe Strain Data
The structural health of airframes is often monitored by analysis of the frequency of occurrence matrix (FOOM) produced after each flight. Each cell in the matrix records a stres...
Simon J. Hickinbotham, James Austin
115
Voted
ICMCS
2000
IEEE
116views Multimedia» more  ICMCS 2000»
15 years 6 months ago
Non Linear Traffic Modeling of VBR MPEG-2 Video Sources
In this paper, a neural network scheme is presented for modeling VBR MPEG-2 video sources. In particular, three non linear autoregressive models (NAR) are proposed to model the ag...
Anastasios D. Doulamis, Nikolaos D. Doulamis, Stef...
ICASSP
2011
IEEE
14 years 5 months ago
Deep Belief Networks using discriminative features for phone recognition
Deep Belief Networks (DBNs) are multi-layer generative models. They can be trained to model windows of coefficients extracted from speech and they discover multiple layers of fea...
Abdel-rahman Mohamed, Tara N. Sainath, George Dahl...
WSCG
2001
137views more  WSCG 2001»
15 years 3 months ago
An Application of Combined Neural Networks to Remotely Sensed Images
Studies in the area of Pattern Recognition have indicated that in most cases a classifier performs differently from one pattern class to another. This observation gave birth to th...
Rafael Valle dos Santos, Marley B. R. Vellasco, Ra...
EUSFLAT
2009
245views Fuzzy Logic» more  EUSFLAT 2009»
14 years 11 months ago
Universal Approximation of a Class of Interval Type-2 Fuzzy Neural Networks Illustrated with the Case of Non-linear Identificati
Neural Networks (NN), Type-1 Fuzzy Logic Systems (T1FLS) and Interval Type-2 Fuzzy Logic Systems (IT2FLS) are universal approximators, they can approximate any non-linear function....
Juan R. Castro, Oscar Castillo, Patricia Melin, An...