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Abstract

A hybrid fuzzy logic/neural network prediction system and method is disclosed for predicting response times to service requests to a service provider. Data from a historical database of records including customer requests and weather information are input to the hybrid system. The data is filtered to reject faulty data entries and data not necessarily useful for predicting response times to service requests such as customer comments are eliminated. A backpropagation neural network operating in a supervised learning mode is employed to decrease the effects of the inherent system nonlinearities. The prediction error from the neural network is trained to make predictions within a predetermined error limit. The neural network generates a prediction configuration; i.e. a set of neural network characteristics, for every record per geographical area, time frame, and month. A fuzzy logic classifier is used for further data reliability. A fuzzy logic classifier relying upon the Fuzzy Cell Space Predictor (FCSP) method is employed to improve predicted response times from year to year. The fuzzy logic classifier supervises the overall identification scheme and, for every record, computes a prediction configuration for its corresponding month in the preceding year. The fuzzy logic classifier then computes a prediction estimate for its neighboring months in the preceding year and computes the prediction estimate for the next time frame (i.e. morning and evening). The Center of Gravity method is used to smooth the different prediction estimates to obtain a final predicted response time.

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