TY - GEN
T1 - Optimization of fuzzy-neural structure through genetic algorithms and its application in artificial odor recognition-system
AU - Putro, Benyamin Kusumo
AU - Irwanto, P.
AU - Jatmiko, Wisnu
N1 - Publisher Copyright:
© 2002 IEEE.
PY - 2002
Y1 - 2002
N2 - Fuzzy neural networks are gaining much research interest and have attracted considerable attention recently, due to diverse applications in such fields as pattern recognition, image processing and control. However, this type of neural system, similar to that of multilayer perceptrons, has a drawback due to its huge neural connections. In this article, we proposed a method for optimizing the structure of a fuzzy artificial neural network (FANN) through genetic algorithms. This genetic algorithm (GA) is used to optimize the number of weight connections in a neural network structure, by evolutionary calculation of the fitness function of those structures as individuals in a population. The developed optimized fuzzy neural net is then applied for pattern recognition in an odor recognition system. Experimental results show that the optimized neural system provides higher recognition capability compared with that of unoptimized neural systems. The recognition rate of the unoptimized neural structure is 70.4% and could be increased to 85.2% in the optimized neural system. It is also shown that the computational cost of the optimized neural system structure is also lower than for the unoptimized structure.
AB - Fuzzy neural networks are gaining much research interest and have attracted considerable attention recently, due to diverse applications in such fields as pattern recognition, image processing and control. However, this type of neural system, similar to that of multilayer perceptrons, has a drawback due to its huge neural connections. In this article, we proposed a method for optimizing the structure of a fuzzy artificial neural network (FANN) through genetic algorithms. This genetic algorithm (GA) is used to optimize the number of weight connections in a neural network structure, by evolutionary calculation of the fitness function of those structures as individuals in a population. The developed optimized fuzzy neural net is then applied for pattern recognition in an odor recognition system. Experimental results show that the optimized neural system provides higher recognition capability compared with that of unoptimized neural systems. The recognition rate of the unoptimized neural structure is 70.4% and could be increased to 85.2% in the optimized neural system. It is also shown that the computational cost of the optimized neural system structure is also lower than for the unoptimized structure.
KW - Artificial neural networks
KW - Fuzzy control
KW - Fuzzy neural networks
KW - Fuzzy systems
KW - Genetic algorithms
KW - Image processing
KW - Multilayer perceptrons
KW - Optimization methods
KW - Pattern recognition
KW - Process control
UR - http://www.scopus.com/inward/record.url?scp=31144441472&partnerID=8YFLogxK
U2 - 10.1109/APCCAS.2002.1115117
DO - 10.1109/APCCAS.2002.1115117
M3 - Conference contribution
AN - SCOPUS:31144441472
T3 - IEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS
SP - 47
EP - 51
BT - Proceedings - APCCAS 2002
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - Asia-Pacific Conference on Circuits and Systems, APCCAS 2002
Y2 - 28 October 2002 through 31 October 2002
ER -