Time Series Analysis Using Unsupervised Construction of Hierarchical Classifiers

Recently we have proposed an algorithm of constructing hierarchical neural network classifiers (HNNC), that is based on a modification of error back-propagation. This algorithm combines supervised learning with selforganisation. Recursive use of the algorithm results in creation of compact and computationally effective selforganised structures of neural classifiers. The above algorithm was expanded for unsupervised analysis of dynamic objects, described by time series. It performs segmentation of the analysed time series into parts characterised by different types of dynamics. This paper presents the latest successful results of testing the algorithm of time series analysis on pseudo-chaotic maps.

• Type: Conference
• Year: 2001
• Where: FLAIRS
• Authors: S. A. Dolenko, Yu. V. Orlov, I. G. Persiantsev, Ju. S. Shugai, Anthony G. Pipe
• Keywords: Algorithm, Artificial Intelligence, FLAIRS 2001, Neural Network Classifiers, Time Series