Article

# Three-phase strategy for the OSD learning method in RBF neural networks

School of Engineering, Tarbiat Modares University, P.O. Box 14115-179, Tehran, Iran; School of Engineering, Islamic Azad University of Qazvin, Member of YRC, P.O. Box 14115-179, Tehran, Iran; School of Basic Sciences, Tarbiat Modares University, P.O. Box 14115-179, Tehran, Iran

Neurocomputing 01/2009; DOI: 10.1016/j.neucom.2008.05.011 Source: DBLP

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**ABSTRACT:**Due to environmental concerns and growing cost of fossil fuel, high levels of distributed generation (DG) units have been installed in power distribution systems. However, with the installation of DG units in a distribution system, many problems may arise such as increase and decrease of short circuit levels, false tripping of protective devices and protection blinding. This paper presents an automated and accurate fault location method for identifying the exact faulty line in the test distribution network with high penetration level of DG units by using the Radial Basis Function Neural Network with Optimum Steepest Descent (RBFNN-OSD) learning algorithm. In the proposed method, to determine the fault location, two RBFNN-OSD have been developed for various fault types. The first RBFNN-OSD is used for predicting the fault distance from the source and all DG units while the second RBFNN is used for identifying the exact faulty line. Several case studies have been simulated to verify the accuracy of the proposed method. Furthermore, the results of RBFNN-OSD and RBFNN with conventional steepest descent algorithm are also compared. The results show that the proposed RBFNN-OSD can accurately determine the location of faults in a test given distribution system with several DG units.Measurement. 01/2013; 46(9):253-67. - [Show abstract] [Hide abstract]

**ABSTRACT:**Radial basis function (RBF) neural networks have been broadly used for classification and interpolation regression. So the idea for trying to develop new learning algorithms for getting better performance of RBF neural networks is an interesting subject. This paper presents a new learning method for RBF neural networks. A novel Particle Swarm Optimization (PSO) has been applied in the proposed method to optimize the Optimum Steepest Decent (OSD) algorithm. The OSD algorithm could be used in applications where need real-time capabilities for retraining neural networks. To initialize the RBF units more accurately, the new approach based on PSO has been developed and compared with a Conventional PSO clustering algorithm. The obtained results have shown better and same network response in fewer train iterations which is essential for fast retraining of the network. The PSO–OSD and Three-phased OSD algorithms have been applied on five benchmark problems and the results have been compared. Finally, employing the proposed method in a real-time problem has shown interesting outcomes as have come out in this paper.Neurocomputing. 07/2013; 111:169–176. - [Show abstract] [Hide abstract]

**ABSTRACT:**In this paper a new methodology for training radial basis function (RBF) neural networks is introduced and examined. This novel approach, called Fuzzy-OSD, could be used in applications, which need real-time capabilities for retraining neural networks. The proposed method uses fuzzy clustering in order to improve the functionality of the Optimum Steepest Descent (OSD) learning algorithm. This improvement is due to initialization of RBF units more precisely using fuzzy C-Means clustering algorithm that results in producing better and the same network response in different retraining attempts. In addition, adjusting RBF units in the network with great accuracy will result in better performance in fewer train iterations, which is essential when fast retraining of the network is needed, especially in the real-time systems. We employed this new method in an online radar pulse classification system, which needs quick retraining of the network once new unseen emitters detected. Having compared result of applying the new algorithm and Three-Phase OSD method to benchmark problems from Proben1 database and also using them in our system, we achieved improvement in the results as presented in this paper.Applied Soft Computing. 09/2013; 13(9):3831-3838.

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