Pattern recognition system by a matched spatial filter and neural networks for imperfect input characters

1993 ◽  
Author(s):  
Shun-ichi Kamemaru ◽  
Tsutomu Baba
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Recognition System ◽  
Spatial Filter ◽  
Pattern Recognition System

EMG Pattern Recognition System Based on Neural Networks

2012 ◽  
Author(s):  
Juan Carlos Gonzalez-Ibarra ◽  
Carlos Soubervielle-Montalvo ◽  
Omar Vital-Ochoa ◽  
Hector Gerardo Perez-Gonzalez
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Recognition System ◽  
Pattern Recognition System ◽  
Emg Pattern Recognition

Discrimination of Two-Mixture Fragrances Odor Using Artificial Odor Recognition System with Ensemble Backpropagation Neural Networks

2013 ◽  
Vol 303-306 ◽  
pp. 1514-1518
Author(s):  
Benyamin Kusumoputro ◽  
Li Na
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Quartz Resonator ◽  
Learning Mechanisms ◽  
Odor Recognition ◽  
Pattern Recognition System ◽  
Backpropagation Neural Networks ◽  
Ensemble Neural Networks

The human sensory test is often used to obtain the sensory quantities of odors, however, the fluctuation of results due to the experts condition can cause discrepancies among panelists. We have developed an artificial odor recognition system using a quartz resonator sensor and backpropagation neural networks as the pattern recognition system in order to eliminate the disadvantage of human panelist system. The backpropagation neural networks shows high recognition rate for single component odor, however, become very low when it is used to discriminate mixture fragrances odor. In this paper we have proposed an ensemble of backpropagation neural networks as the pattern recognition system, and by using the ensemble learning mechanisms, the recognition rate is significantly increased, especially when using ensemble neural networks with five components.

A test of a pattern recognition system for identification of spiders

1999 ◽  
Vol 89 (3) ◽  
pp. 217-224 ◽  
Author(s):  
M.T. Do ◽  
J.M. Harp ◽  
K.C. Norris
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Recognition System ◽  
Test System ◽  
Shape Information ◽  
Pattern Recognition System ◽  
Taxonomic Research ◽  
Automated Pattern Recognition ◽  
Artificial Neural

AbstractGrowing interest in biodiversity and conservation has increased the demand for accurate and consistent identification of arthropods. Unfortunately, professional taxonomists are already overburdened and underfunded and their numbers are not increasing with significant speed to meet the demand. In an effort to bridge the gap between professional taxonomists and non-specialists by making the results of taxonomic research more accessible, we present a partially automated pattern recognition system utilizing artificial neural networks (ANNs). Various artificial neural networks were trained to identify spider species using only digital images of female genitalia, from which key shape information had been extracted by wavelet transform. Three different sized networks were evaluated based on their ability to discriminate a test set of six species to either the genus or the species level. The species represented three genera of the wolf spiders (Araneae: Lycosidae). The largest network achieved the highest accuracy, identifying specimens to the correct genus 100% of the time and to the correct species an average of 81% of the time. In addition, the networks were most accurate when identifying specimens in a hierarchical system, first to genus and then to species. This test system was surprisingly accurate considering the small size of our training set.

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