Application of neural network and genetic algorithm in identification of a model of a variable mass underwater vehicle. (1st March 2015) - Shafiei, M.H.; Binazadeh, T.
Abstract: This paper is one of the few works in identification of a model of a Variable Mass Underwater Vehicles (VMUVs) with six degrees of freedom. Since, the mass of fuel changes during the operation, the mass and the center of mass of these vehicles also change and therefore the VMUV has nonlinear-time varying model. In order to obtain a model of the VMUV, an identification procedure is done by using an especial neural network. In this network, which is called the Volterra neural network, the basis functions are Volterra polynomials. Also, genetic algorithm (GA) is used with neural network to structure selection of these nonlinear polynomials. Computer simulations, Monte Carlo and cross-correlation analyses are done by considering admissible levels of noises in the underwater vehicles instrumentations (rate gyros) and the results show the efficiency of the presented approach. Highlights: We model a Variable Mass Underwater Vehicle (VMUV) with six degrees of freedom. An especial neural network is used to model the VMUV in identification procedure. In this network, the basis functions are Volterra polynomials. Genetic Algorithm (GA) is used for structure selection of Volterra polynomials. Monte Carlo and cross-correlation analyses are done by considering noises in the sensors.