A Novel Exact Fixed-node Quantum Monte Carlo Algorithm

In this paper we proposed a novel exact fixed-node quantum Monte Carlo (EFNQMC)algorithm,which is a self-optimizing and self-improving procedure,In contrast to the previous EFNQMC method,the trial function is optimized synchronistically in the diffusion procedure,but not before the beginning of EFNQMC computation.In order to optimize the trial function,the improved steepest descent technique is used,in which the step size is automatically adjustable.The procedure is quasi-Newton and converges super linearly.We also use a novel trial function,which has correct electron-electron and electron-nucleus cusp conditions.The novel EFNQMC algorithm and the novel trial function are employed to calculate the energies of 1^1A1 state of CH2,^1Ag state of C8 and the ground-states of H2,LiH,Li2,H2O,respectively.The test results show that both the novel algorithm and the trial function proposed in the present paper are very excellent.     

热风炉燃烧过程智能优化控制方法的研究

为了实现在不同工况条件下通过调整空燃比使热风炉燃烧系统保持在能耗最低、效率最高下运行,根据高炉生产的特点和蓄热式热风炉燃烧方式,设计了以模糊BP神经网络为智能优化算法的自寻优控制策略,实现了PID控制器参数实时、动态、精确的调整,实现了动态PID参数的准确调整,保证了煤气流量和空气流量的合理比值。经仿真结果表明,该系统不但有效地解决了传统热风炉的燃烧控制系统非线性、建模难和强耦合性的问题,而且使热风炉处于安全、节能的燃烧状态。     

自优化剩余函数量子Monte Carlo方法

提出剩余函数量子Monte Carlo的一个新算法，这是一个自优化和自改善的过程.与以前的算法相比，本算法中的试探函数的优化是在剩余函数方法中同步进行的，而不是在变分Monte Carlo计算之前.为了优化试探函数，使用一种改进了的速降法，这是一个步长能够自动调节，超线性收敛的优化技术.在这个算法中,还使用了一种新的相关函数，它满足电子与电子以及电子与核奇点条件.此方法已被用于计算H2、LiH、Li2、H2O分子的基态以及CH2的X 3B1态、1 1A1态和2 1A1态的能量值.     

基于自寻最优模糊控制的自动频率跟踪

文章概述了在功率超中自动频率跟踪的基本原理，在分析自寻最优控制的方法和模糊控制方法的基础上，提出了一种自寻最优模糊控制方法，利用它来实现自动频跟踪，在超声吸引器上取得良好的应用效果。     

On nearly self-optimizing strategies for a discrete-time uniformly ergodic adaptive model

We control a discrete-time uniformly ergodic system, which depends on an unknown parameter ⁰ A, a compact set. Our purpose is to minimize the long-run average-cost functional. We estimate the unknown parameter using the biased maximum likelihood estimator and apply the control which is almost optimal for the value of estimation. This way we construct strategies such that the value of the cost functional can be arbitrarily close to the optimal value obtained for ⁰.