Self-organized network with a supervised training and its comparison with FALVQ in artificial odor recognition system

2000 ◽  
Author(s):  
Benyamin Kusumoputro ◽  
Linda Rostiviani ◽  
Ari Saptawijaya
Keyword(s):  
Recognition System ◽  
Odor Recognition ◽  
Supervised Training ◽  
Self Organized

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.

scholarly journals Structured Learning Based Turkish Sentiment Analysis

2019 ◽  
pp. 90-93
Author(s):  
Oguz Ulgen ◽  
Arif Selcuk Ogrenci
Keyword(s):  
Pattern Recognition ◽  
Sentiment Analysis ◽  
Recognition System ◽  
Structured Learning ◽  
Supervised Training ◽  
Training Methodology ◽  
The Social ◽  
Private And Public ◽  
And Training

Sentiment analysis is highly popular topic to identify people's opinions through the social media, forums and other websites. There are an abundance of opinions on internet and analysing those opinions would have many benefits for both private and public sectors. Research has evolved looking on tweets for mining opinions and for the classification of the tweets as positive, negative or neutral in its sentiment. In this research, Turkish tweets are used for sentiment extraction where a two layer neural network is used as the pattern recognition system. The supervised training of this system is based on structured learning. As a conclusion, structured learning seems to be helpful in pattern recognition to classify tweets and mining the opinions. However, it is evident that further research in data processing and training methodology is necessary to obtain reliable sentiment analysis results.

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