基于混合微分进化算法的生化过程动态参数估计(英文)

Determination of the optimal model parameters for biochemical systems is a time consuming iterative process. In this study, a novel hybrid differential evolution (DE) algorithm based on the differential evolution technique and a local search strategy is developed for solving kinetic parameter estimation problems. By combining the merits of DE with Gauss-Newton method, the proposed hybrid approach employs a DE algorithm for identifying promising regions of the solution space followed by use of Gauss-Newton method to determine the optimum in the identified regions. Some well-known benchmark estimation problems are utilized to test the efficiency and the robustness of the proposed algorithm compared to other methods in literature. The comparison indicates that the present hybrid algorithm outperforms other estimation techniques in terms of the global searching ability and the convergence speed. Additionally, the estimation of kinetic model parameters for a feed batch fermentor is carried out to test the applicability of the proposed algorithm. The result suggests that the method can be used to estimate suitable values of model parameters for a complex mathematical model.     

一种改进的卡尔曼滤波压缩感知信道估计算法*

精确、高速的信道估计是通信系统处理信号的基础。针对卡尔曼滤波压缩感知在信道估计时伪测量过程计算效率较低的问题,提出了一种高性能的卡尔曼滤波压缩感知信道估计算法。对伪测量过程的近似 范数约束框架进行了进一步优化,引入高斯核函数对雅克比赋权矩阵的列向量进行优化,使算法对稀疏信号支撑集的重构速度较大程度提升。同时,引入微分熵确立了收敛指标,降低了算法的运行时间。仿真表明,在同等条件下,本文算法相对于原有算法,估计精度和收敛速度均有较大程度提高,在低信噪比和不同稀疏度下都具有较好的鲁棒性和实用性。     

球形译码算法在信号空间分集系统中的应用研究

针对信号空间分集系统中最大似然检测算法复杂度高的问题,选用球形译码算法作为信号空间分集系统接收机检测算法,可以使接收机在接近最大似然算法检测性能的同时,降低接收机检测复杂度。为进一步降低检测复杂度,通过采用最小均方误差算法减小噪声对接收信号的干扰,来降低因搜索噪声点而产生的复杂度,并利用衰减因子k加快搜索半径收缩来达到进一步降低球形译码算法检测复杂度的目的。仿真结果表明,在信号空间分集系统中引入球形译码算法可以降低接收机检测复杂度,并且改进后的球形译码算法检测时间在低信噪比情况下约为传统球形译码算法的12%—33%。因此,球形译码算法可以有效地降低信号空间分集系统接收机检测复杂度。