Adaptation and interaction in dynamical systems: Modelling and rule discovery through evolving connectionist systems

The paper presents a methodology for adaptive modelling and discovery of dynamic relationship rules from continuous data streams. In dynamic processes, underlying rules may change over time and tracing these changes is a difficult task for computer modelling. Evolving fuzzy neural networks ( EFuNN) are used for this purpose here. EFuNNs belong to the group of evolving connectionist systems ( ECOS). These are information systems that learn from data in a supervised mode through on- line adaptive clustering and allow for rule extraction, each rule representing input- output relationship within a cluster of data. Extracted rules, after each consecutive chunk of data is entered into the system, are compared in order to discover new patterns of interaction between input and output variables. Thus the stability and plasticity of the investigated process are evaluated. The rules are also used for the prediction of future events. To illustrate the methodology, a mathematical example is used, along with two real case studies. The first case study is from Macroeconomics and the second one is from Bioinformatics. (C) 2005 Elsevier B. V. All rights reserved.