STATISTICALLY ROBUST NEURAL NETWORK TIME SERIES PREDICTION

TIMESERIES PREDICTION TECHNIQUES ARE CENTRAL TO A NUMBER OF BROAD APPLICATION AREAS CRITICAL TO BOTH DEFENSE AND CIVILIAN INDUSTRY, INCLUDING WEATHER FORECASTING, ECONOMIC FORECAST AND A VARIETY OF SIGNAL PROCESSING AND CONTROL APPLICATIONS. MANY SPECIFIC APPLICATION PROBLEMS INVOLVE INTRINSIC NONLINEARITY WHICH THE WELL-ESTABLISHED LINEAR PREDICTION METHODS (EG., ARMA, ARIMA, FIR AND IIR FILTERS, KALMAN FILTERS, LMS FILTERS, ETC.) ARE UNABLE TO CAPTURE EFFECTIVELY. NONLINEAR PREDICTION MODELS USING NEURAL NETWORKS (SUCH AS MULTILAYER PERCEPTRONS AND RADIAL BASIS FUNCTIONS) OFFER THE POSSIBILITY OF SUBSTANTIALLY IMPROVED PERFORMANCE OVER CONVENTIONAL LINEAR MODELS IN PREDICTING THE BEHAVIOR OF NONLINEAR SYSTEMS. A NUMBER OF CRITICAL ISSUES MUST BE ADDRESSED IN THE DEVELOPMENT OF APPROPRIATE NONLINEAR PREDICTION MODELS, SUCH AS THE SELECTION OF VARIABLES, ARCHITECTURE SELECTION, ARCHITECTURE PRUNING, THE ESTIMATION OF PREDICTION RISK (GENERALIZATION PERFORMANCE). PHASE I OF THE PROJECT WILL FOCUS ON THE DEVELOPMENT OF STATISTICALLY ROBUST AND COMPUTATIONALLY EFFICIENT ALGORITHMS AND TECHNIQUES FOR CONSTRUCTING NEURAL NETWORK TIME SERIES PREDICTORS. WEATHER FORECASTING WILL SERVE AS A TEST PROBLEM FOR PHASE I. PHASE II OF THE PROJECT WILL USE THE ALGORITHMS TECHNIQUES DEVELOPED IN PHASE I TO SOLVE ONE OR MORE LARGE SCALE FORECASTING PROBLEMS OF DIRECT INTEREST TO DEFENSE OR COMMERCIAL CLIENTS. ANTICIPATED BENEFITS/POTENTIAL APPLICATIONS - DEVELOPING STATISTICALLY ROBUST METHODS FOR NONLINEAR NEURAL NETWORK TIME SERIES PREDICTION SYSTEMS WILL ALLOW NPS TO SOLVE A NUMBER OF PROBLEMS OF PRACTICAL INTEREST, INCLUDING SHORT-TERM, LOCALIZED FORECASTING OF WEATHER.