CIS Publication Spotlight [Publication Spotlight]
Digital Object Identifier: 10.1109/ TNNLS.2013.2295813 “This paper proposes an offline algorithm for incrementally constructing and training radial basis function (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 algor ithm designs very compact RBF networks compared with the other exiting 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.” A New Learning Algorithm for a Fully Connected Neuro-Fuzzy Inference System, by C.L.P. Chen, J. Wang, C.H. Wang, and L. Chen, IEEE Transactions on Neural Networks and Learning Systems, Vol. 25, No. 10, October 2014, pp. 1741–1757.