title: A probabilistic wavelet system for stochastic and incomplete data-based modeling.
abstract: A probabilistic wavelet system (PWS) is proposed to model the unknown dynamic system with stochastic and incomplete data. When compared with the traditional wavelet system, the PWS uses a novel three-domain wavelet function to make a balance among the probability, time, and frequency domains, which achieves a robust modeling performance with poor data information. The definition, transformation, multiple-resolution analysis, and implementation of the PWS are presented to construct the whole theoretical framework. Simulation studies show that the performance of the proposed PWS is superior to the traditional one in a stochastic and incomplete data environment.
authors: Department of Automation, Guangdong University of Technology, Guangzhou, China. lz@gdut.edu.cn
authors: Zhi
authors: Z
authors: Liu
authors: Han-Xiong
authors: HX
authors: Li
authors: Yun
authors: Y
authors: Zhang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D018401:Sample Size; D013269:Stochastic Processes