采用带预过滤的人工蜂群算法的最优传感器布置
Optimal Sensor Placement for Output Estimation Using an Artificial Bee Colony Algorithm with Pre-filter
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中文总结 AI 辅助
针对三维热弹性系统的传感器布置优化问题，采用带Gramian预过滤的新型二元人工蜂群（NBABC）算法降低计算复杂度，该方法高效且收敛快速，可解决最大化卡尔曼滤波器估计性能的NP难问题。
中文摘要 AI 辅助
最大化卡尔曼滤波器估计性能的传感器布置是一个NP难优化问题，其可行集随候选位置和传感器数量呈组合式增长。本文针对建模为离散时间线性随机模型的三维热弹性系统，研究该传感器布置问题，采用带基于Gramian预过滤的新型二元人工蜂群（NBABC）算法以降低计算复杂度。结果表明所提方法高效且收敛快速。
英文摘要
Sensor placement for maximizing the estimation performance of the Kalman filter is an NP-hard optimization problem. Furthermore, its feasible set grows combinatorially with the candidate locations and the number of sensors. In this paper, we study this sensor placement problem for a 3D thermoelastic system modelled as a discrete-time linear stochastic model. We use the Novel Binary Artificial Bee Colony (NBABC) algorithm with a Gramian-based pre-filter to reduce the computational complexity. Our results show the efficiency and the fast convergence of the proposed approach.
