Evolving fuzzy neural networks for on-line knowledge discovery

Abstract

Fuzzy neural networks are connectionist systems that facilitate learning from data, reasoning over fuzzy rules, rule insertion, rule extraction, and rule adaptation. The concept evolving fuzzy neural networks (EFuNNs), with respective algorithms for learning, aggregation, rule insertion, rule extraction, is further developed here and applied for on-line knowledge discovery on both prediction and classification tasks. EFuNNs operate in an on-line mode and learn incrementally through locally tuned elements. They grow as data arrive, and regularly shrink through pruning of nodes, or through node aggregation. The aggregation procedure is functionally equivalent to knowledge abstraction. The features of EFuNNs are illustrated on two real-world application problems---one from macroeconomics and another from Bioinformatics. EFuNNs are suitable for fast learning of on-line incoming data (e.g., financial and economic time series, biological process control), adaptive learning of speech and video data, incremental learning and knowledge discovery from growing databases (e.g. in Bioinformatics), on-line tracing of processes over time, life-long learning. The paper includes also a short review of the most common types of rules used in the knowledge-based neural networks for knowledge discovery and data mining.