Advantages of Radial Basis Function Networks for Dynamic System Design

作者：Yu Hao^*; Xie Tiantian; Paszczynski Stanislaw; Wilamowski Bogdan M

来源：IEEE Transactions on Industrial Electronics, 2011, 58(12): 5438-5450.

DOI：10.1109/TIE.2011.2164773

• Adaptive control
• fuzzy inference systems
• neural networks
• online learning
• radial basis RBF) networks

Radial basis RBF) networks have advantages of easy design, good generalization, strong tolerance to input noise, and online learning ability. The properties of RBF networks make it very suitable to design flexible control systems. This paper presents a review on different approaches of designing and training RBF networks. The recently developed algorithm is introduced for designing compact RBF networks and performing efficient training process. At last, several problems are applied to test the main properties of RBF networks, including their generalization ability, tolerance to input noise, and online learning ability. RBF networks are also compared with traditional neural networks and fuzzy inference systems.

• 出版日期2011-12