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System identification advances and case studies



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Hinamoto, T., Realizations of a State-Space Model from Two-


This chapter investigates the application of perceptron neural networks in modeling traffic sources in packet based computer communication networks. It is motivated by recent measurement studies that indicate the presence of significant statistical features in packet traffic belong to the fractal nature of the processes rather than their stochastic nature. The chapter first provides an illustration of the statistical features of the measured traffic over the Internet. It then outlines a learning scheme based on back propagation algorithm for a class of perceptron neural networks that can be used to capture several of the fractal properties observed in actual data. The most important conclusion of this chapter is that, despite the existence of numerical difficulties, neural networks may allow building of accurate models to predict the behavior of packet traffic sources.


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system identification advances and case studies
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