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Title:
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COLLABORATIVE AGENT LEARNING USING HYBRID NEUROCOMPUTING |
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Author(s):
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Saulat Farooque , Lakhmi Jain , Ajith Abraham |
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ISBN:
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972-99353-6-X |
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Editors:
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Nuno Guimarães and Pedro Isaías |
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Year:
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2005 |
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Edition:
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1 |
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Keywords:
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Multi-agents, hybrid neurocomputing, collaborative learning, security. |
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Type:
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Full Paper |
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First Page:
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377 |
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Last Page:
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384 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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This paper investigates the use of a hybrid neurocomputing approach to detect and then recognise images. The first technique creates and trains intelligent agents capable of detecting face images by using a Generalised Regression Neural Network (GRNN). The second technique further refines the search by recognising images from the detected data set using a feed forward backpropagation neural network. These two agents make up the Detection Agent and the Recognition Agent in an agent architecture that collaborates with each other to detect and then recognise certain images. The overall agent architecture will operate as an Automatic Target Recognition (ATR) system. The architecture of ATR system is presented in this paper and it is shown how the Detection and Recognition Agents (DRA) fit into the overall system. Experiments and results using the DRA are also presented. |
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