DocumentCode :
718581
Title :
Application of genetic algorithm to configure artificial neural network for processing a vector multisensor array signal
Author :
Dykin, V.S. ; Musatov, V.Yu. ; Varezhnikov, A.S. ; Bolshakov, A.A. ; Sysoev, V.V.
Author_Institution :
Yuri Gagarin State Tech. Univ. of Saratov, Saratov, Russia
fYear :
2015
fDate :
21-23 May 2015
Firstpage :
1
Lastpage :
4
Abstract :
The possibility of applying genetic algorithms to configure a topology of artificial neural network which process a multisensor array vector signal for gas-analytical tasks has been considered. Such a configuration is implemented according to the criteria of increasing the percentage of correct recognition and reducing the computing cost by using a set of predefined possible values of the parameters of the neural network architecture. The optimized characteristics are the number of hidden layers of neurons in each layer and the amount of training sampling. The tests have been performed in the ©Matlab neural network operated under Levenberg-Marquardt back-propagation learning function. The obtained results confirm the efficiency of the genetic algorithm to optimize the topology of designed artificial neural network with advanced characteristics.
Keywords :
array signal processing; genetic algorithms; neural nets; Levenberg-Marquardt backpropagation learning function; configure artificial neural network; correct recognition; genetic algorithm application; multisensor array vector signal; training sampling; vector multisensor array signal processing; Artificial neural networks; Electronic noses; Neurons; Sensor arrays; Training; electronic nose; gas analysis; gas sensor; genetic algorithm; multisensor microarray; pattern recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control and Communications (SIBCON), 2015 International Siberian Conference on
Conference_Location :
Omsk
Print_ISBN :
978-1-4799-7102-2
Type :
conf
DOI :
10.1109/SIBCON.2015.7147049
Filename :
7147049
Link To Document :
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