Treffer: Predicting Chaotic Time Series Using a Fuzzy Neural Network

In this paper the authors present an alternative neurofuzzy architecture for application to chaotic time series prediction. The architecture employs an approximation to the fuzzy reasoning system to considerably reduce the dimensions of the network as compared to similar approaches. The application considered is the chaotic Mackey-Glass differential equation. Simulation results were obtained using the MATLAB neural network toolbox and these are compared with both traditional neural network implementations and other fuzzy reasoning approaches. The work not only demonstrates the advantage of the neurofuzzy approach but it also highlights the advantages of the architecture for hardware realisations. 1. Introduction Time series prediction is a very important practical application with a diverse range of applications including economic and business planning, inventory and production control, weather forecasting, signal processing and control [Box76]. As a result, there has been considerabl.