| Summary: | 碩士 === 國立政治大學 === 資訊管理學系 === 88 === This paper uses some experimental designs to detect if the Neural Networks system after learning the chaotic data is a chaotic system. That is verified via testing four characteristics in chaotic data, inclusive of boundedness, determinism, aperiodicity and sensitive dependence on initial conditions. Further, this paper uses the result above to predict the learned chaotic model. The purpose is to probe into if the Neural Networks system after learning the chaotic data is a chaotic system and is used to predict, how good the short-term and the long-term predictions will be? And, compare with if the Neural Networks system after learning the chaotic data is not a chaotic system and is used to predict, how large the error will be? We present the Neural Network systems after learning the chaotic data never can rebuild the learned chaotic model. But, sometimes the Neural Network system would mimic as a chaotic system. So, if we take Neural Network system to predict something with chaotic phenomena, it is possible to use one chaotic system to predict another chaotic system. According to the property of sensitive dependence on initial conditions, it should make large errors. However, from the experiments we design, we find whether the Neural Network system after learning is a chaotic system or not, it has no influence on its predicting effect. This hint is applied to use ANN to predict in financial markets or other areas with chaotic phenomenon.
|
|---|
