算法

We calculate the differential characteristic set of determining equations for source evolution equations (include several linear and nonlinear differential system of equations).

用算法′计算了几个微分方程对称的确定方程组(包括线性微分方程组以及非线性微分方程组)的微分特征列集，并在计算量上对算法′与算法进行了比较。

This paper introduces the homotopy algorithm into back-propagation neural network and indicates that the convergence speed can be improved and the defects of converging to local extremum can be eliminated.

同伦算法是解决非线性优化问题的新理论，将同伦算法引入误差反向传播神经网络，提高了网络的训练速度，消除了常规BP网络算法收敛于局部极值的缺点。

We make an introduction to Maximum Intensity Projection algorithm and its improved version, Local Maximum Intensity Projection algorithm.

详细介绍了最大强度投影算法及其改进算法——局部最大强度投影算法。

Genetic-based fuzzy neural network is used to fuse a lot of data of information handling unit, and the method of using Genetic Algorithm to optimize general network parameter and using BP Algorithm to modulate and optimize local parameter is presented.

采用基于遗传算法的模糊神经网络对信息处理单元的大量数据进行数据融合，提出了采用遗传算法优化具有全局性的网络参数，用BP算法调节和优化具有局部性的参数。

It can draw a conclusion that 1-bit algorithm is feasible and valid method for signal time delay estimation replacing with cross-correlation principle The paper gives an overview about the localization principle with temperature sensors arrays based on the far-field algorithm and the near-field algorithm, and discusses the performance of fire source localization principle based on combination of the two algorithms.

本文介绍了将热气流温度波前视为平面波的远场算法和将热气流温度波前视为圆形波的近场算法，讨论了远、近场算法相结合的火灾定位性能。

Since the model is a large scale and NP-hard combinational optimization problem in the real navigation environment, a hybrid simulated annealing algorithm is employed to search the suboptimal solution composed of a lockage timetable and a ship scheduling, The search starts by trimming the existing lockage timetable based on heuristic rules; the result is then used to compute the ship scheduling by the depth-first-search algorithm. The obtained solution is finally updated by the Metropolis rule.

本文首先提出了该问题的混合整数非线性规划模型，在实际通航调度环境中，该模型属于强NP-hard复杂度的大规模组合优化问题，因此设计了一种混合模拟退火算法来搜索次优化调度方案，该算法将解分解为闸次时间表和船舶调度计划两部分，在搜索过程中用启发式规则对闸次时间表进行调整，然后用深度优先搜索算法根据闸次时间表求解船舶调度计划，最后根据Metropolis规则对当前解进行更新。

This paper deals with the uncertainty of parameters in the looper control in rolling mills and the multi-objective robust PID control is proposed.

针对热连轧活套控制中的模型参数不确定性问题，文章建议采用多目标鲁棒PID控制算法处理这类问题；该算法不仅可以保证系统的稳定性，而且对具有一定不确定性的系统也能保证其动态特性品质，同时其设计与实现相对简单，该控制算法的效果通过仿真结果加以验证。

To the discrete-time systems, we introduce a new discrete-time norm, the algorithm given is not depend upon the initial data. For a class of uncertain discrete-time systems, we also adopt the method of iterative learning control, we convert the problem of learning control into a global stability of trivial solution for lower triangular matrix dynamic systems, and obtain the result of tracking to desired state of the system.

对于一般非线性离散系统，引进了离散的范数，所给的算法不依赖于学习控制算法中的初始数据，对于一类不确定离散系统的状态跟踪问题，我们也采用迭代学习控制的方法，将相应的学习控制问题转化为一个下三角动力系统的平凡解全局稳定性问题，获得了对系统理想状态的跟踪结果，并对所给的算法的收敛性进行了分析。

Ant Colony Algorithm is a new developed bionic optimization algorithm, which simulate the ants'finding food process. Because of its simple mechanism, plus-feedback parallelization, strong lustiness and excellent distributed compu- tational mechanism, it has shown its excellent capability and huge developing potential in solving many complicated optimization problems, and it has been successfully applied to production problem such as scheduling problems and routing problems. But it also has some disadvantages, it need longer searching time than others and tend to precocity and stagnation.

蚁群算法是一种最新发展的模拟昆虫王国中蚂蚁觅食行为的仿生优化算法，该算法采用了正反馈并行自催化机制，具有较强的鲁棒性、优良的分布式计算机制、易于与其它方法结合等优点，在解决许多复杂优化问题方面已经展现出其优异的性能和巨大的发展潜力，并已成功地应用于诸如生产调度、布线等生产问题。

Ant Colony Algorithm is a new developed bionic optimization algorithm,which simulates the ants'finding food process.Because of its simple mechanism,plus-feedback parallelization,strong lustiness and excellent distributed computational mechanism,it has shown huge developing potential in solving many complicated optimization problems.

蚁群算法是一种最新发展的模拟昆虫王国中蚂蚁觅食行为的仿生优化算法，该算法具有较强的鲁棒性、优良的分布式计算机制、易于与其它方法结合等优点。

The split between the two groups can hardly be papered over.

这两个团体间的分歧难以掩饰。

This approach not only encourages a greater number of responses, but minimizes the likelihood of stale groupthink.

这种做法不仅鼓励了更多的反应，而且减少跟风的可能性。