查询词典 fundamental algorithm

In Goertzel algorithm using MATLAB (first using FFT algorithm, reuse goertzel algorithm) simulat...

应用于双音多频信号的产生与检测。

With the idea of smoothing Newton method, we propose a new class of smoothing Newton methods for the nonlinear complementarity problem based on a class of special functions. In this paper, complementarity problem is converted into a series of smoothing nonlinear equations and a modified smoothing Newton algorithm is used to solve the equations. We use Newton direction and Gradient direction together in the algorithm which guarantees that our method is globally convergent. Also using another smoothing function, we reformulate the generalized nonlinear complementarity problems defined on a polyhedral cone as a system of smoothing equations and a smooth unconstrained optimization problem. Theoretical results that relate the stationary points of the merit function to the solution of the generalized nonlinear complementarity problems are presented, we use the modified smoothing Newton algorithm in generalized nonlinear complementarity problems, under mild hypothesis, a global convergence is proved.

本文一方面基于现有的各种光滑Newton法的思想和半光滑理论，利用著名的F-B互补函数的光滑形式，首先将互补问题的求解转化为求解一系列光滑的非线性方程组，然后给出了一种修正的光滑Newton法，该方法不仅放宽对函数F的要求，在Newton方程不可解时引入初始效益函数的最速下降方向，而且光滑因子的选择也比较简单可行，同时在适当的条件下，证明了其算法具有全局收敛性；另一方面，借助另一种F-B光滑函数，将多面体锥上的广义互补问题转化为一种光滑形式，讨论了优化问题的稳定点与广义非线性互补问题的解之间的理论关系，并将这种修正的光滑Newton法用于求解广义非线性互补问题中，在适当的条件下，该算法同样具有全局收敛性。

For the coordination of multiple agents in noncooperation environment, we considered the learning aim as finding a Nash equilibrium strategy through in learning function taking other agent's actions into account. Therefore, we expended the single agent Q-learning algorithm into multiagent Q-learning algorithm. In addition, we proved the convergence of multi-agent Q-learning algorithm under certain general sum stochastic game architecture for only one Nash equilibrium or multiple same Nash equilibrium.

对于非合作环境下的多智能体协调我们通过在学习函数中考虑了智能体的联合行动，提出把学习目标作为求取一个Nash平衡点策略，这样将单智能体Q-learning算法扩展到多智能体的Q-learning算法；证明了在一定的一般和随机对策结构下具有一个或多个相同Nash平衡点条件下多智能体Q-learning算法的收敛性。

To improve the performances of Saitou and Nei's algorithm and Studier and Kepler's improved algorithm for constructing the Neighbor-Joining phylogenetic trees, reducing the time complexity of the computation, a fast algorithm has been developed.

以混沌游戏为例子，利用二分技术对这类递推问题进行了并行算法设计，分析了这类算法的时间和空间复杂性以及适用范围。

In this paper we propose a reduced search soft-output detection algorithm fully based on the principle of M-algorithm for turbo-equalization, which is a suboptimum version of the Lee algorithm.

本文提出了一种完全基于M算法原理、应用于Turbo均衡的减少搜索的软输出检测算法，它是一种次最佳的Lee算法。

Combining the shortcoming of multi-objective evolutionary algorithm based on the idea of objective space dividing with high calculation complexicity, this paper proposed an improved new algorithm which processed following characters: transforming the dominance relationship among individuals' Pareto to the rank relationship of sum index of interval in dividing space; simple and efficient environmental choosing method based on index ranking; an individual crowding algorithm which rapidly choosing the nearest to origin.

针对现有基于目标空间分割思想的进化算法计算时间复杂度高的缺陷，提出了一种改进的基于目标空间分割的多目标进化算法。该算法具有以下特点：把个体之间的Pareto支配关系转换成分割区间索引值排序关系的目标空间分割算法；简单高效的基于区间索引值排序的环境选择算子；一种快速的优先选择最接近分割区间原点的个体拥挤机制。

The experiment proved that after the process of adopting improved selection algorithm, searching algorithm, integral transfer and quantity, the complication of calculation is decreased greatly. This method advanced the coding efficiency of video compressing. Finally, this paper is presented summarizer and future prospects the H.264/AVC algorithm.

实验证明，在采用改进的选择算法和变换与量化过程后，在视频图像信噪比基本上不变的情况下，运算复杂度大大降低，提高了视频压缩编码效率。

It is compared with basic particle swarm optimization algorithm and genetic algorithm.Results prove that this algorithm is effective.

在求解配送中心选址问题时，常用的定量分析方法是重心法、CFLP法[1]、遗传算法[2]等。

A noveldynamic clustering algorithm based on the combination ofFCM algorithm with particle swarm optimization algorithm is proposed in this paper.

将粒子群优化算法与模糊C-均值聚类算法相结合，提出一种新颖的动态聚类算法。

There are two kinds of models about the higher-order correlation networks. In the two-state model, the higher-order Hebbian algorithm is usually used. For this Hebbian algorithm can have better associative memory only to orthogonal or near orthogonal prototype patterns, another learning rule, higher-order projection algorithm, is proposed in tensor representation in this dissertation.

关于高阶关联网络有两种模型，即两状态离散时间模型和模拟状态连续时间模型，在两状态离散时间高阶关联网络中最常用的是高阶Hebb规则，这种规则对于正交或近似正交的原型模式才具有比较好的联想记忆性能，因而本文用张量表示法给出了另外一种学习规则—高阶投影规则。

I'm not an actor. I'm a professor of paleontology.

我不是演员，我是古生物学教授

Spider Network Web site that is a very image of the name.

网络蜘蛛即Web Spider，是一个非常形象的名字。