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SME MS900610
- A Bi-Directional Artificial Neural Network For 3d Motion Prediction
- standard by Society of Manufacturing Engineers, 06/01/1990
- Publisher: SME
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A BI-DIRECTIONAL NEURAL NETWORK MODEL FOR 3-D OBJECT MOTION PREDICTION IS PRESENTED. MOTION DETECTION AND PREDICTION REQUIRES VERY FAST COMPUTATION SPEED AND HIGH ACCURACY. ARTIFICIAL NEURAL NETWORK, A MASSIVELY PARALLEL AND DISTRIBUTED COMPUTATION ARCHITECTURE, IS VERY SUITABLE FOR THIS SPEED-CRITICAL REAL-TIME COMPUTER APPLICATION. DERIVED FROM SIMPLE NEURAL NETWORK MODELS, A BI-DIRECTIONAL DYNAMIC ASSOCIATIVE NEURAL NETWORK (BDANN) CAN PREDICT OBJECT MOTIONS ADEQUATELY FOR REAL-TIME APPLICATION. THE NETWORK APPLIES A RETROSPECTIVE AUTOREGRESSION MOVING AVERAGE (RARMA) COMPUTATION SCHEME FOR CONTINUAL UP-DATING OF NETWORK PARAMETERS. SIMULATION RESULTS SHOW THE EFFICIENCY AND ACCURACY OF THE APPROACH.