Self Organizing Neural Network for Adaptive Prediction of Dynamic Systems

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The goal of this project is to design a novel self- organizing neural system for adaptive prediction of hourly municipal water consumptions subject to climatic disturbances, and to test the proposed design by using real time series from the City of Arlington, Texas. It will apply a multiple-model approach to neuroidentification. The adaptive prediction system is composed of three subnetworks: (1) A layer of least-squares-based adaptive predictor elements (APEs), each of which corresponds to a model in the set of a priori system's input-output models, for filtering measurements and input data and performing multistep-ahead predictions; (2) A middle layer of self-organizing adaptive neurons that perform model switching without statistical hypotheses testing; (3) An output layer of adaptive linear combiners (ALCs) for combination of the multiple predictions of the same leadtimes. The self-organizing adaptive predictor (SOAP) also employs various on-line forecast evaluation feedback signals and other non- time-series inputs for adapting the ALC weights as well as providing reinforcement signals to enhance model switching performance of the self organization subnetwork. The activities of this project consist of: 1) Development of the SOAP system, using synthetic time series and simulation tests on a computer workstation. Off-line training, on-line pattern learning, convergence rates and plasticity of the connection weights are the key issues during the simulation. 2) Analysis of the fine-tuned SOAP system, using real time series and operation data from the City of Arlington, Texas. This research has is intend to advance the fundamental state of knowledge in neuridentification. The restricted assumption in neuroengineering on independent noises is removed by implementing the ARMAX model in the APEs layer; another limitation with respect to the existing inability to predict beyond one step ahead is also alleviated by the use of system- based adaptive prediction algorithms in the APEs.