Sequential RBF function estimator: memory regression network

The newal-network training algorithm can be divided into 2 categories: (I) Batch mode and (2) Sequential mode. In this paper, a novel online RBF network called "Memory Regression Network (MRN)" is proposed. Different from the previous approaches [2, 11], MRN involves two types of memories: Experience and Neuron, which handle short and long term memories respectively. By simulating human's learning behavior, a given function can be estimated without memorizing the whole training set. Two sets of function estimation experiments are examined in order to illustrate the performance of the proposed algorithm. The results show that MRN can effectively approximate the given function within a reasonable time and acceptable mean square error. © 2004 IEEE.published_or_final_versio

Image enlargement as an edge estimation

A robust image enlargement algorithm is presented in this paper. We formulate the image enlargement process as an edge information estimation process. In order to achieve a higher resolution, we first perform Pixel Duplication on the target image to form an initial high resolution image. Then the edge details of the enlarged image are estimated by using a novel neural network called "Agent Swarm Regression Network ASRN", which is trained by a set of low resolution (LR) / high resolution (HR) image patch pairs. Two benchmark images were used to verify the performance of the proposed algorithm. The results show that the enlarged images by the proposed algorithm are sharper than those by the conventional methods.published_or_final_versio