Parallel sub-neural network system for hand vein pattern recognition

Partial discharge patterns are an important tool for diagnosis of HV insulation systems. Skilled humans can identify the possible insulation defects in various representations of partial discharge (PD) data. One of the most widely used representation is phase resolved PD (PRPD) patterns. This paper describes a method for the automated recognition of PRPD patterns using a novel composite neural network system for the actual classification task. This paper elucidates the possible methods of extracting relevant features from the PRPD data in a knowledge based way i.e. according to physical properties of PD gained from PD modeling. This allows the novel complex neural network (NN) system for classification. The efficacy of composite neural network developed using original probabilistic neural network is examined. This innovative methodology of giving inputs to the composite neural network compares favorably with the traditional network architecture used previously for PD pattern recognition.

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Compact holographic optical neural network system for real‐time pattern recognition

Optical Engineering ◽

10.1117/1.600792 ◽

1996 ◽

Vol 35 (8) ◽

pp. 2122

Author(s):

Taiwei Lu

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Real Time ◽

Network System ◽

Time Pattern ◽

Neural Network System ◽

Optical Neural Network

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Spectral pattern recognition of controlled substances in street samples using artificial neural network system

10.1117/12.883408 ◽

2011 ◽

Cited By ~ 3

Author(s):

Larisa Poryvkina ◽

Valeri Aleksejev ◽

Sergey M. Babichenko ◽

Tatjana Ivkina

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Artificial Neural Network ◽

Network System ◽

Spectral Pattern ◽

Controlled Substances ◽

Neural Network System ◽

Artificial Neural

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USE OF GRADATED PATTERNS IN AN ASSOCIATIVE NEURAL MEMORY FOR INVARIANT PATTERN RECOGNITION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001494000310 ◽

1994 ◽

Vol 08 (02) ◽

pp. 595-607

Author(s):

KAZUKUNI KOBARA ◽

TAIHO KANAOKA ◽

YOSHIHIKO HAMAMOTO ◽

SHINGO TOMITA ◽

KOUKICHI MUNECHIKA

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Back Propagation ◽

Point Of View ◽

Network System ◽

Back Propagation Algorithm ◽

Error Back Propagation ◽

Neural Network System ◽

Feedback Error ◽

Error Back Propagation Algorithm

Distortion invariant pattern recognition is an interesting problem from the biological and technological point of view. However, it has not yet been solved by neural networks in satisfactory way. This paper investigates an associative neural network system to improve the recalling accuracy for distortion patterns. On a perception type of neural network with feedback, error back-propagation algorithm and energy function are used for a learning process and a recalling process, respectively. By using gradated patterns as learning and unknown patterns, it is shown that the recalling accuracy becomes higher than using original pattern themselves.

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