MACHINE LEARNING USING AN ADAPTIVE-NETWORK FOR PATTERN RECOGNITION

THE EVOLUTION OF HIGH ORDER LANGUAGES FOR ROBOTIC SYSTEMS HAS PROCEEDED TO A POINT WHERE THEIR UTILIZATION IN SOLVING APPLICATIONS PROBLEMS IS TERMED "ARTIFICIAL INTELLIGENCE". HOWEVER A KEY REMAINING TECHNOLOGY PROBLEM FOCUSES ON TECHNIQUES WHICH WILL ALLOW SOFTWARE SYSTEMS TO LEARN AS A FUNCTION OF THEIR UTILIZATION, AND EXPERIENCE. IN THIS SBIR WE PROPOSE RESEARCH TO ENHANCE A PROTOTYPE ADAPTIVE-NETWORK PATTERN RECOGNITION SYSTEM DESIGNED BY SYSTRAN UNDER AN AIR FORCE SUPPORTED RESEARCH PROGRAM. ADAPTIVE NETWORKS ARE AN ALTERNATIVE APPROACH TO AI WHICH MODEL BIOLOGICAL LEARNING SYSTEMS AND HAVE BEEN DEMONSTRATED TO LEARN FROM EXPERIENCE WITHOUT ADDITIONAL PROGRAMMING. THE TASKS SET FORTH IN THE STATEMENT OF WORK WILL ADVANCE A PATTERN RECOGNIZING ADAPTIVE NETWORK FOR FAST REALTIME PROCESSING.