Since the simulation underwater acoustic signal is used in the semi-object simulation experiment of underwater weapons, it has great impression upon simulation fidelity. It is asked that whether simulation signals can...Since the simulation underwater acoustic signal is used in the semi-object simulation experiment of underwater weapons, it has great impression upon simulation fidelity. It is asked that whether simulation signals can replace the real signal effectually. Considering the randomness of signals, the interval estimation of feature parameters of simulation signals is made. By comparing the obtained confidence interval with the corresponding accept interval, the concept of similarity coefficient of simulation signals is given. By making a statistical analysis for similarity coefficient, the uniformity information of simulation signals is extracted, and the fuzzy number which expresses the fuzzy uniformity level of simu- lation signals is obtained. The analysis method on fuzzy uniformity of simulation underwater acoustic signals is presented. It is indi- cated by the application in simulation of target radiated-noises that the method is suitable and effectual for the simulation research on underwater acoustic signals, and the analysis result may provide support for decision-making relative to perfecting simulation sys- tems and applying simulation signals.展开更多
We solve numerically an eigenvalue elliptic partial differential equation(PDE)ranging from two to six dimensions using the generalized multiquadric(GMQ)radial basis functions(RBFs).Two discretization methods are em-pl...We solve numerically an eigenvalue elliptic partial differential equation(PDE)ranging from two to six dimensions using the generalized multiquadric(GMQ)radial basis functions(RBFs).Two discretization methods are em-ployed.The first method is similar to the classic mesh-based discretization method requiring n centers per dimension or a total ndpoints.The second method is based upon n randomly generated points in dℜrequiring far fewer data centers than the classic mesh method.Instead of having a crisp boundary,we form a“fuzzy”boundary.Using the analytic or numerical in-verse interior and boundary operators,we find the local and global minima and maxima to cull discretization points.We also find that the GMQ-RBF“flatness”can be controlled by increasing the GMQ exponential,β.We per-form a search to find the smallest root mean squared error(RMSE)by varying the exponent,the maximum,the minimum range of the GMQ shape parame-ter,and boundary influence,with the exponential having the most influence.Because the GMQ-RBFs are essentially nonlinear,it follows that the starting point of the simple search influences the end result.The optimal algorithm for high dimensional PDEs is still the subject of much research and may wait for the common place availability of massively parallel quantum computers for even higher dimensional PDEs and integral equations(IEs).展开更多
文摘Since the simulation underwater acoustic signal is used in the semi-object simulation experiment of underwater weapons, it has great impression upon simulation fidelity. It is asked that whether simulation signals can replace the real signal effectually. Considering the randomness of signals, the interval estimation of feature parameters of simulation signals is made. By comparing the obtained confidence interval with the corresponding accept interval, the concept of similarity coefficient of simulation signals is given. By making a statistical analysis for similarity coefficient, the uniformity information of simulation signals is extracted, and the fuzzy number which expresses the fuzzy uniformity level of simu- lation signals is obtained. The analysis method on fuzzy uniformity of simulation underwater acoustic signals is presented. It is indi- cated by the application in simulation of target radiated-noises that the method is suitable and effectual for the simulation research on underwater acoustic signals, and the analysis result may provide support for decision-making relative to perfecting simulation sys- tems and applying simulation signals.
文摘We solve numerically an eigenvalue elliptic partial differential equation(PDE)ranging from two to six dimensions using the generalized multiquadric(GMQ)radial basis functions(RBFs).Two discretization methods are em-ployed.The first method is similar to the classic mesh-based discretization method requiring n centers per dimension or a total ndpoints.The second method is based upon n randomly generated points in dℜrequiring far fewer data centers than the classic mesh method.Instead of having a crisp boundary,we form a“fuzzy”boundary.Using the analytic or numerical in-verse interior and boundary operators,we find the local and global minima and maxima to cull discretization points.We also find that the GMQ-RBF“flatness”can be controlled by increasing the GMQ exponential,β.We per-form a search to find the smallest root mean squared error(RMSE)by varying the exponent,the maximum,the minimum range of the GMQ shape parame-ter,and boundary influence,with the exponential having the most influence.Because the GMQ-RBFs are essentially nonlinear,it follows that the starting point of the simple search influences the end result.The optimal algorithm for high dimensional PDEs is still the subject of much research and may wait for the common place availability of massively parallel quantum computers for even higher dimensional PDEs and integral equations(IEs).
