Neurofuzzy agents and neurofuzzy laws for autonomous machine learning and control
Wen-Ran Zhang · Proceedings of International Conference on Neural Networks (ICNN'97) · 2002
Real world autonomous agents exhibit adaptive, incremental, exploratory, and sometimes explosive learning behaviors. Learning in neurofuzzy control, however, is often referred to as global training with a large set of random examples and with a very low learning rate. This type of controller does not show exploratory learning behaviors. An agent-oriented approach to neurofuzzy control is introduced and illustrated in folding-legged robot locomotion and gymnastics: necessary and sufficient conditions are established for agent-oriented neurofuzzy discovery; and a theory of coordinated multiagent neurofuzzy control is analytically formulated. The analytical features bridge a gap between linear control, neurofuzzy control, adaptive learning, and exploratory learning.