Neural Network Implementation of Chaotic Time Series Prediction

摘要

This thesis provides a description of how a neural network can be trained to learn the order inherent in chaotic time series data and then use that knowledge to predict future time series values. It examines the meaning of chaotic time series data, and explores in detail the Glass-Mackey nonlinear differential delay equation as a typical source of such data. An efficient weight update algorithm is derived, and its two-dimensional performance is examined graphically. A predictor network which incorporates this algorithm is constructed and used to predict chaotic data. The network was able to predict chaotic data. Prediction was more accurate for data having a low fractal dimension than for high-dimensional data. Lengthy computer run times were found essential for adequate network training. Keywords: Sine waves, Ada programming language. (kr)