Adaptive, evolving, hybrid connectionist systems for image pattern recognition

Abstract

The chapter presents a new methodology for building adaptive, incremental learning systems for image pattern classification. The systems are based on dynamically evolving fuzzy neural networks that are neural architectures to realise connectionist learning, fuzzy logic inference, and case-based reasoning. The methodology and the architecture are applied on two sets of real data—one of satellite image data, and the other of fruit image data. The proposed method and architecture encourage fast learning, life-long learning and on-line learning when the system operates in a changing environment of image data.