To solve the problem of Volterra frequency‐domain kernels (VFKs) of nonlinear systems, which can be difficult to identify, we propose a novel non‐parametric identification method based on multitone excitation. First, we have studied the output properties of VFKs of nonlinear systems excited by the multitone signal, and derived a formula for identifying VFKs. Second, to improve the efficiency of the non‐parametric identification method, we suggest an increase in the number of tones for multitone excitation to simultaneously identify multi‐point VFKs with one excitation. We also propose an algorithm for searching the frequency base of multitone excitation. Finally, we use the interpolation method to separate every order output of VFK and extract its output frequency components, then use the derived formula to calculate the VFKs. The theoretical analysis and simulation results indicate that the non‐parametric method has a high precision and convenience of operation, improving the conventional methods, which have the defects of being unable to precisely identify VFKs and identification results are limited to three‐order VFK. 相似文献
A special unilateral NMR sensor has been designed for investigations of thin samples with a thickness of less than 1 mm and of surface effects of polymers. For use with the bar‐magnet NMR‐MOUSE®, the so‐called “crazy coil” is introduced with a low penetration depth. It is a flat meander coil etched on a printed circuit board with wiggles in the conductors. The design of the new coil and FEM simulations of the B1 field are presented. Different applications are discussed by means of illustrative examples. They are the detection of surface damage in rubber samples, the swelling and drying of a latex membrane exposed to cyclohexane vapor mimicking a chemical sensor, and the drying of a thin sprayed adhesive layer.
Abstract. Several models have been proposed in recent years for analysing spatial data and also, to some extent, spatio‐temporal data. One of the important problems, namely the choice of an appropriate model for describing real data sets, remains unsolved. Here we consider the analysis of spatio‐temporal processes from which observations over space and time are available. We propose statistical tests for discriminating between space–time autoregressive processes and multivariate autoregressive processes. The sampling properties of the proposed tests are considered. We illustrate the methods with a real example. We use the above tests to find the best model to describe spatio‐temporal variations of hourly carbon monoxide measurements at four locations in London in January 2004. 相似文献