Integration of connectionist methods and chaotic time-series analysis for the prediction of process data

✍ Scribed by R. Kozma; N. K. Kasabov; J. S. Kim; A. Cohen

• Publisher: John Wiley and Sons
• Year: 1998
• Tongue: English
• Weight: 354 KB
• Volume: 13
• Category: Article
• ISSN: 0884-8173
• DOI: 10.1002/(sici)1098-111x(199806)13:6<519::aid-int7>3.0.co;2-o

✦ Synopsis

A connectionist-based time-series analysis method is described that includes chaotic characterization, fractal analysis together with statistical data processing in an adaptive Ž . fuzzy neural network environment. The applied fuzzy neural network FuNN can utilize as well as generate knowledge during an iterative learning and adaptation procedure.

Ž . Two major aspects of the present work are 1 incorporating knowledge into the fuzzy neural network based on the nonlinear deterministic, chaotic analysis of the signals and Ž . 2 refining and updating the knowledge base by the FuNN using adaptive learning techniques. Examples include the standard gas-furnace benchmark data analysis and also an application to a case study of multivariate signal analysis as part of a project for establishing a plantwise monitoring and process control system.