Forecasting Chaotic Series in Manufacturing Systems by Vector Support Machine Regression and Neural Networks
Keywords:chaos, forecast, neural networks, vector support machines, manufacturing systems
Currently, it is recognized that manufacturing systems are complex in their structureÂ and dynamics. Management, control and forecasting of such systems are very difficultÂ tasks due to complexity. Numerous variables and signals vary in time with differentÂ patterns so that decision makers must be able to predict the behavior of the system.
This is a necessary capability in order to keep the system under a safe operation.Â This also helps to prevent Â emergencies and the occurrence of critical events that mayÂ put in danger human beings and capital resources, such as expensive equipment andÂ valuable production. When dealing with chaotic systems, the management, control,Â and forecasting are very difficult tasks. In this article an application of neural networksÂ and vector support machines for the forecasting of the time varying average numberÂ of parts in a waiting line of a manufacturing system having a chaotic behavior, isÂ presented. The best results were obtained with least square support vector machinesÂ and for the neural networks case, the best forecasts, are those with models employingÂ the invariants characterizing the system’s dynamics.
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