An Incremental Design of Radial Basis Function Networks

作者：Yu Hao^*; Reiner Philip D; Xie Tiantian; Bartczak Tomasz; Wilamowski Bogdan M

来源：IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(10): 1793-1803.

DOI：10.1109/TNNLS.2013.2295813

• Error correction (ErrCor)
• incremental design
• Levenberg-Marquardt (LM) algorithm
• radial basis RBF) networks

This paper proposes an offline algorithm for incrementally constructing and training radial basis RBF) networks. In each iteration of the error correction (ErrCor) algorithm, one RBF unit is added to fit and then eliminate the highest peak (or lowest valley) in the error surface. This process is repeated until a desired error level is reached. Experimental results on real world data sets show that the ErrCor algorithm designs very compact RBF networks compared with the other investigated algorithms. Several benchmark tests such as the duplicate patterns test and the two spiral problem were applied to show the robustness of the ErrCor algorithm. The proposed ErrCor algorithm generates very compact networks. This compactness leads to greatly reduced computation times of trained networks.

• 出版日期2014-10