Training RBF NN Using Sine-Cosine Algorithm for Sonar Target Classification

Radial basis function neural networks (RBF NNs) are one of the most useful tools in classification sonar targets. Despite many abilities RBF NNs, low accuracy classification, entrapment local minima, and slow convergence rate disadvantages these networks. In order to overcome issues, sine-cosine algorithm (SCA) has been used train NNs this work. To evaluate designed classifier, two benchmark underwater problems were used. Also, an experimental target was developed practically merits RBFbased classifier dealing with high-dimensional real world problems. have a comprehensive evaluation, is compared gradient descent (GD), gravitational search (GSA), genetic (GA), Kalman filter (KF) algorithms terms local minima, rate. The results show that proposed provides better performance than other classifiers as it classifies datasets 2.72% best on average.