PARTICLE FILTERING APPROACH TO STATE ESTIMATION IN BOOLEAN DYNAMICAL SYSTEMS

被引：0
|
作者：
Braga-Neto, Ulisses [1 ]
机构：
[1] Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX 77843 USA
来源：
2013 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP) | 2013年
关键词：
Boolean Dynamical Systems; Boolean Networks; Optimal State Estimation; Particle Filtering; Sequential Monte-Carlo Methods;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Exact optimal state estimation for discrete-time Boolean dynamical systems may become impractical computationally if system dimensionality is large. In this paper, we consider a particle filtering approach to address this problem. The methodology is illustrated through application to state tracking in high-dimensional Boolean network models. The results show that the particle filter can be very accurate under a moderate number of particles. The impact of resampling on performance is also investigated.
引用
收藏
页码：81 / 84
页数：4
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