Title :
Associative memories for chemical sensing
Author :
Reznik, A.M. ; Shirshov, Yu.M. ; Snopok, B.A. ; Nowicki, D.W. ; Dekhtyarenko, A.K. ; Kruglenko, I.V.
Abstract :
We consider application of neural associative memories to chemical image recognition. Chemical image recognition is identification of substance using chemical sensors´ data. The primary advantage of associative memories as compared with feed-forward neural networks is high-speed learning. We have made experiments on odour recognition using hetero-associative and modular auto-associative memories. We have also tested backpropagation NNs with one hidden layer. Associative memories displayed recognition quality not worse than backpropagation networks.
Keywords :
content-addressable storage; gas sensors; intelligent sensors; iterative methods; learning (artificial intelligence); pattern recognition; QCM-based arrays; artificial nose; backpropagation networks; chemical image recognition; chemical sensing; fast learning; feedforward neural networks; hetero-associative memories; high-speed learning; maximum classification quality; modular auto-associative memories; neural associative memories; neural software package; odour recognition; single iteration; substance identification; Associative memory; Chemical sensors; Feedforward neural networks; Image recognition; Kinetic theory; Neural networks; Physics; Resonance; Sensor arrays; Sensor phenomena and characterization;
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
DOI :
10.1109/ICONIP.2002.1201972
