classification of sonar targets using omkc, genetic algorithms and statistical moments

due to the complex physical properties of the detected targets using sonar systems, identification and classification of the actual targets is among the most difficult and complex issues of this field. considering the characteristics of the detected targets and unique capabilities of the intelligent methods in classification of their dataset, these methods seem to be the proper choice for the task. in recent years, neural networks and support vector machines are widely used in this field. linear methods cannot be applied on sonar datasets because of the existence of higher dimensions in input area, therefore, this paper aims to classify such datasets by a method called online multi kernel classification (omkc). this method uses a pool of predetermined kernels in which the selected kernels through a defined algorithm are combined with predetermined weights which are also updated simultaneously using another algorithm. since the sonar data is associated with higher dimensions and network complexity, this method has presented maximum classification accuracy of 97.05 percent. by reducing the size of input data using genetic algorithm (feature selection) and statistical moments (feature extraction), eliminating the existing redundancy is crucial; so that the classification accuracy of the algorithm is increased on average by 2% and execution time of the algorithm is declined by 0.1014 second at best.