Cascaded VLSI neural network chips: Hardware learning for pattern recognition and classification
| Autor: | M. Tran, Tuan A. Duong, Taher Daud, Timothy X. Brown, Anilkumar P. Thakoor |
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| Rok vydání: | 1992 |
| Předmět: |
Self-organizing map
021103 operations research Neural gas Pixel Artificial neural network Time delay neural network business.industry Computer science 0211 other engineering and technologies Global Map Pattern recognition 02 engineering and technology Computer Graphics and Computer-Aided Design Software Modeling and Simulation Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
| Zdroj: | SIMULATION. 58:340-347 |
| ISSN: | 1741-3133 0037-5497 |
| DOI: | 10.1177/003754979205800507 |
| Popis: | Currently map data is stored as high- resolution digitized pixel data on CD-ROM storage devices. The copious amount of data generated from the global map data base overwhelms even high-density optical storage methods. In addition, the map user is concerned not with the high-resolution image of the map, but the actual features such as roads and rivers. By classifying the map-image pixels into separate features, the dimensional ity of the data is dramatically reduced, the map is significantly decluttered, and the data is in the form most suitable for further analysis. Because of the extensive volume of the data already stored and its on-demand nature, classification speed must exceed the CD-ROM read speed so that access rates are unaffected. This paper describes a neural network approach to pattern classification applied to map pixel data. Software simulations of a sophisticated neural network show that neural networks are indeed equivalent to optimal statistical pattern classifiers. Furthermore, a fully parallel neural network hardware implementatiort developed at JPL, surpasses the necessary processing speed, and provides high classification accuracy. Our software as well a hardware results are presented in this paper along with a brief backgrourtd in pattern classification and neural networks. |
| Databáze: | OpenAIRE |
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