Stability and Adaptation of Neural Networks

Abstract : Focused on unsupervised learning and adaptive fuzzy systems. Explored the differential competitive learning (DCL) law. We successfully benchmarked DCL against supervised competitive learning for phoneme recognition and centroid estimation. Proved structural stability for general competitive learning laws. Developed product-space clustering to develop adaptive fuzzy systems, which grow structured fuzzy rules from training data without supervision. Successfully benchmarked adaptive fuzzy systems against neural-network truck-and-trailer systems and Kalman-filter control systems for realtime target tracking.