An improved radial basis function neural network for object image retrieval

An improved radial basis function neural network for object image retrieval
دوره: 
۱۶۸
شماره صفحه (از - تا): 
۲۲۱-۲۳۳
چکیده

Radial Basis Function Neural Networks (RBFNNs) have been widely used for classification and function approximation tasks.‎ Hence, it is worthy to try improving and developing new learning algorithms for RBFNNs in order to get better results.‎ This paper presents a new learning method for RBFNNs.‎ An improved algorithm for center adjustment of RBFNNs and a novel algorithm for width determination have been proposed to optimize the efficiency of the Optimum Steepest Decent (OSD) algorithm.‎ To initialize the radial basis function units more accurately, a modified approach based on Particle Swarm Optimization (PSO) is presented.‎ The obtained results show fast convergence speed, better and same network response in fewer train data which states the generalization power of the improved neural network.‎ The Improved PSO–OSD and Three-phased PSO–OSD algorithms have been tested on five benchmark problems and the results have been compared.‎ Finally, using the improved radial basis function neural network we propose a new method for object image retrieval.‎ The images to be retrieved are object images that can be divided into foreground and background.‎ Experimental results show that the proposed method is really promising and achieves high performance.‎

استناد: 

Montazer, Gholam Ali, and Davar Giveki.‎ 2015.‎ An improved radial basis function neural network for object image retrieval.‎ Neurocomputing ۱۶۸: ۲۲۱-۲۳۳.

مقاله ادواری علمی
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