Comparison of hybrid neural systems of KSOM-BP learning in artificial odor recognition system

2002 ◽  
Author(s):  
B. Kusumoputro ◽  
A. Saptawijaya ◽  
A. Murni
Keyword(s):  
Recognition System ◽  
Neural Systems ◽  
Odor Recognition

Recognizing Odor Mixtures Using Optimized Fuzzy Neural Network Through Genetic Algorithms

2005 ◽  
Vol 9 (3) ◽  
pp. 290-296 ◽  
Author(s):  
Benyamin Kusumoputro ◽  
◽  
Teguh P. Arsyad
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Multilayer Perceptron ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Recognition System ◽  
Back Propagation Neural Network ◽  
Odor Recognition ◽  
Fuzzy Neural ◽  
Odor Mixtures

Recognizing odor mixtures is rather difficult in artificial odor recognition system, especially when the number of sensors is limited. Classification is further hampered if the number of unlearned odor mixtures classes is increased. We developed a fuzzy-neuro multilayer perceptron as a pattern classifier and compared its recognition with that of the Probabilistic Neural Network and Back-propagation Neural Network. To enhance the recognition capability of the system, we then optimized fuzzy-neuro multilayer perceptron topology by deleting its weak weight connections using Genetic Algorithms. Experimental results show that the optimized fuzzy-neuro multilayer perceptron has the highest recognition in 18 classes of two-mixture odors with almost 98.2% when using hardware with 16 sensors, compared to 83.3% when using 8 sensors.

scholarly journals Fuzzy Clustering Neural Networks for Real-Time Odor Recognition System

2007 ◽  
Vol 2007 ◽  
pp. 1-6 ◽  
Author(s):  
Bekir Karlık ◽  
Kemal Yüksek
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Fuzzy Clustering ◽  
Input Data ◽  
Recognition System ◽  
Neural System ◽  
The Other ◽  
Odor Recognition ◽  
Recognition Probability

The aim of this study is to develop a novel fuzzy clustering neural network (FCNN) algorithm as pattern classifiers for real-time odor recognition system. In this type of FCNN, the input neurons activations are derived through fuzzy c mean clustering of the input data, so that the neural system could deal with the statistics of the measurement error directly. Then the performance of FCNN network is compared with the other network which is well-known algorithm, named multilayer perceptron (MLP), for the same odor recognition system. Experimental results show that both FCNN and MLP provided high recognition probability in determining various learn categories of odors, however, the FCNN neural system has better ability to recognize odors more than the MLP network.

HYBRID NEURAL SYSTEMS FOR PATTERN RECOGNITION IN ARTIFICIAL NOSES

2005 ◽  
Vol 15 (01n02) ◽  
pp. 137-149 ◽  
Author(s):  
CLEBER ZANCHETTIN ◽  
TERESA B. LUDERMIR
Keyword(s):  
Pattern Recognition ◽  
Intelligent Systems ◽  
Recognition System ◽  
Neural Systems ◽  
Wavelet Filter ◽  
Hybrid Intelligent Systems ◽  
Artificial Nose ◽  
Hybrid Approaches ◽  
Polymer Sensors ◽  
Odor Signals

This work examines the use of Hybrid Intelligent Systems in the pattern recognition system of an artificial nose. The connectionist approaches Multi-Layer Perceptron and Time Delay Neural Networks, and the hybrid approaches Feature-Weighted Detector and Evolving Neural Fuzzy Networks were investigated. A Wavelet Filter is evaluated as a preprocessing method for odor signals. The signals generated by an artificial nose were composed by an array of conducting polymer sensors and exposed to two different odor databases.

scholarly journals A Multi Biometric IRIS Recognition System based on a Profound Learning Method

2019 ◽  
Vol 8 (3S) ◽  
pp. 637-643
Keyword(s):  
Iris Recognition ◽  
Recognition System ◽  
Neural Systems ◽  
Learning Method ◽  
Multimodal Biometrics ◽  
Combination Strategy ◽  
Edge Channel ◽  
Palm Print

Biometrics is the estimation of natural qualities which are one of a kind to a person for recognizing and confirming the person. The estimations incorporate fingerprints, retinal outputs, iris checks, voice designs, facial qualities, palm prints, and so forth.., Biometric frameworks have been especially effective in distinguishing an obscure individual via looking through a database of attributes and by confirming the case of a person by contrasting his/her trademark with that put away in a database. To expand the heartiness of the framework and to make it more secure, different attributes of a similar individual are utilized. This is alluded to as multimodal biometrics. In this paper we talked about a portion of the multimodal biometric frameworks. Here a bi-modular biometric acknowledgment framework in light of iris, palm-print. Wavelet and curve let change and Gabor-edge channel are utilized to extricate includes in various weighing machine moreover introductions starting iris as well as palm print, finer points taking out in addition to arrangement is utilized in favour of coordinating. diverse combination calculations together with achieve based, positionbased plus choice depend on techniques are utilized to-join the consequences of two constituents. We additionally recommend another rank-based combination calculation Bio Maximum Inverse Rank (BMIR) which is vigorous as for varieties in scores and furthermore awful positioning from a module. IITD iris databases and CASIA datasets for palm print and unique mark are utilized in this investigation. The examinations demonstrate the adequacy of our combination strategy, profound learning, neural systems and our Bi-modular biometric acknowledgment framework in contrast with existing multi-modular acknowledgment frameworks.

scholarly journals Design of Multi-Biometrics for Fake Fingerprint Detection through Body Odor

2019 ◽  
Vol 8 (4) ◽  
pp. 9646-9650
Keyword(s):  
Real Time ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Body Odor ◽  
Biometric System ◽  
Biometric Recognition ◽  
Odor Recognition ◽  
Biometric Systems ◽  
History Of ◽  
Fingerprint Detection

This work proposes the modernistic multibiometric recognition system for detecting artificial fingerprints and new biometric recognition system to use it in some real-time scenarios. In the recent studies of multi-biometrics, the usage of fingerprint and body odor recognition system stays untouched. This proposed design of a multi-biometric system includes a body odor recognition system along with a fingerprint recognition system that will improve the results in terms of accuracy. The reason behind proposing this model is to detect artificial fingerprints by differentiating the odor of human skin from other materials that are employed in the preparation of artificial fingerprints. This multi-biometric system can be used in forensic labs to identify criminals and to improve the standards of security in authentication of an individual. This multi-biometric system will completely eradicate the use of fake fingerprints and this proposed work will make a remarkable place in real-time applications and the history of multi-biometric systems.

scholarly journals Construction of Taste/Odor Recognition System Using Optimized Sensory Device.

1997 ◽  
Vol 117 (7) ◽  
pp. 371-376
Author(s):  
Hiroshi Osada ◽  
Hiroshi Yoshida ◽  
Yutaka Omamiuda ◽  
Yoshifumi Ajishi ◽  
Kyoshiro Seki ◽  
...  
Keyword(s):  
Recognition System ◽  
Odor Recognition
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