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对于给定的一组轨迹点，用优化方法实现铰链四杆机构轨迹复演。优化方法为共轭梯度法。(For a set of track points,we use a optimization method to optimize the track of Hinge four-bar mechanism. The optimization method is conjugate gradient.)

基于FFT的海面模拟。能够实现海面的复制，波浪模拟效果较好。(FFT-based simulation of the sea. Be able to achieve replication of the sea, wave simulation better.)

说明：  使用EPTREE可以很好的对，C,C++，Java代码进行解析。绘出函数树图，计算堆栈大小等。 <输出的文件内容> 功能树 INKURUDOFAIRUTSURI 测量若干步骤（有条件汇编） 函数索引（使用堆栈大小为每个功能输出） 使用类名单 全局变量，变结构，对象名单 ＃定义定义列表 清单中所使用 列表名称 信息分布函数的行数(EPTREE can be used for good, C, C++, Java code analysis. Mapping function tree diagram, calculate the stack size. )

第一个把专家青睐的数学模型技术（NNM 算法模型）引入词汇记忆软件中，并取得了巨大的成功。NNM 算法模型与经典的“艾滨豪斯遗忘曲线”真正的融合于一体，使得该著名的理论在计算机与数学模型下第一次得到了淋漓尽致的发挥。(First a mathematical model of the popular technology expert (NNM algorithm model) the introduction of vocabulary learning software, and has achieved great success. NNM algorithm model and the classic " Ai Bin House forgetting curve" true integration in one, making the well-known theory in computer and mathematical models has been most vividly the first time under play.)

基于GWBASIC实现的无约束寻优算法，内附GWBASIC主程序和说明(Non-binding on GWBASIC achieve optimization algorithm, the main program and instructions included GWBASIC)

C++编写“三维粱单元有限元计算内核”，对杆系有限元的理论和C++的理解有了很大提高(C++ written " three-dimensional beam finite element kernel module" , and on finite element theory and the understanding of C++ has been greatly improved)

基于时间序列法的超短期电力负荷预测 利用时间序列法进行数学建模(Mathematical modeling based on very short-term power load time series forecasting using time series method)

多目标遗传算法，求得pareto最优解集，可以用于各种工程的数学计算(Multi-objective genetic algorithm, pareto optimal solution set is obtained, can be used for a variety of engineering mathematics)

用c++实现m次多项式的曲线拟合，改程序简单易懂，可读性强(it s easy)

针对传统的算法如遗传算法、粒子群算法等在TSP问题上求解精确性和求解规模上都还有一定的不足，本文提出了一种基于动态规划思想的粒子群优化算法。该算法用动态规划的方法实现粒子间的信息交互和粒子的进化，并且将粒子群中的粒子按无标度信息指导网络拓扑图的方式进行连接。仿真结果表明该方法能有效地减小误差率，提高解的精确，同时还保持了较低的计算复杂度，具有良好的稳健性。(TSP problem solving for the traditional algorithms such as genetic algorithms, particle swarm optimization accuracy and solving the scale, there is a certain lack of a dynamic programming algorithm-based particle swarm optimization algorithm. The algorithm using dynamic programming method to achieve the evolution of information exchange between the particles and the particles, and particle swarm particle scale-free information guide network topology map to connect. The simulation results show that this method can effectively reduce the error rate and improve the solution precision, while maintaining a low computational complexity, good soundness.)