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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Abdel-Aty-Zohdy, Hoda S. Al-Nsour, M. |
| Copyright Year | 1999 |
| Description | Author affiliation: Dept. of Electr. & Syst. Eng., Oakland Univ., Rochester, MI, USA (Abdel-Aty-Zohdy, H.S.) |
| Abstract | In a biological nose, the environment usually suggests a number of common odors. The classification process checks sensed information against existing knowledge. This similarity with Reinforcement Learning neural networks suggests challenging implementation problems. A VLSIC digital design and implementation of a Reinforcement Artificial Neural Network (RANN) for chemical classification, in an electronic nose is presented. The chip is designed to classify chemical gases among four possible volatile organic compounds. The system consists of four neurons and twelve synapses. A neuron has been implemented on a tiny chip, using 2.0 /spl mu/m n-well CMOS technology, at Orbit Semiconductors, through the MOSIS facilities. Simulation results demonstrated proper operation. Standalone experiments are satisfactory, with off-chip weight storage and weight update. Electronic nose system testing is under way. |
| Starting Page | 236 |
| Ending Page | 237 |
| File Size | 153928 |
| Page Count | 2 |
| File Format | |
| ISBN | 0769501044 |
| ISSN | 10661395 |
| DOI | 10.1109/GLSV.1999.757421 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1999-03-04 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Electronic noses Artificial neural networks Neurons CMOS technology Learning Chemical compounds Organic chemicals Gases Volatile organic compounds Chemical technology |
| Content Type | Text |
| Resource Type | Article |
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