Nonlinear Modeling And Control Of Parametric Sound Source Based On Artificial Neural Network

The parametric sound source is a new type sound system which utilizs the nonlinearpropagation effect of the sound in the air to generate an audible sound beam withdirectivity. To obtain high quality audible sound, this acoustic system needs to make fulluse of nonlinear propagation effect of the sound. And on the other hand, the sounddistortion produced by nonlinear characters should be restrained as far as possible. Sodeep understanding and rational utilizing nonlinear character is a key problerm needs tobe solved in the field of the parametric sound source. This dissertation has studiedtheoretically and experimentally nonlinear characters of the parametric sound source.And the main research works and achievements are summarized as follows:According to the basic nonlinear acoustic theory, the nonlinear characters of theparametric sound source are analyzed theoretically. The analysis results show that thecurrent theories are not enough to understand and explore the nonlinear characters of theparametric sound source. And the signal processing methods developed from the currenttheories remain to be improved on reducing the nonlinear distortion of sound.The artificial neural network(ANN) is proposed to bulid the model of the parametricacoustic system. The mapping relationship between input and output of this system canbe fitted accurately by the ANN even if the nonlinear principle and mechanism is notclear. This provides necessary conditions for system contol and performanceoptimization of the parametric sound source. After solving many basic problems ofANN modeling, the back propagation(BP) neural network model and radial basisfunction(RBF) neural network model are built. These models are demonstrated andcompared through simulations under sinusoidal signal excitation and random signalexcitation.The evaluation methods of ANN model are described. The sensitivity andgeneralization ability of ANN model of the parametric sound source are analyzed in thisdissertation. The activation function substitution method is proposed to calculate theglobal sensitivity of the parametric sound source model, and the theoretical formula isderived. After analyzing the factors influencing generalization ability of ANN model, many methods are used to improve the generalization ability of ANN model of theparametric sound source. And the effects of these methods are demonstrated bysimulations.This dissertation applies the basic idea and method of ANN inverse control to buildthe nonlinear control system of parametric acoustic system. The ANN direct inversecontrol system and PID compound inverse control system are designed. And the systemperformance improvements through the ANN inverse control are verified by simulation.The experiments for testing the self-demodulated signal have been done tounderstand the nonlinear and sound distortion of the parametric sound source. Andunder the same conditions as experiments, with the ANN model and the PID compoundANN inverse control model established in aforementioned work, the MATLABsimulations for analyzing the nonlinear and sound distortion are implemented.Comparisions between experimental result and emulational result confirm that the ANNmodel and the PID compound ANN inverse control model are effective. From thesecomparisions, the improvement direction and breakthrough points are clear in nextresearch work.