Nomoto

code for fitness distance ratio PSO

smart antenna. WS=Z where Z=[1 0 0..]T & S is the stearing vector.Find W? STEP1:Input Number of Array Elements, The Separation Distance Between Elements, The Operation Frequency, The Angle of Incidence of The Desired Source Signal , The Angle of Incidence of The Interference Un-Desired Source Signals Respectively, The Angle of Incidence of the Undesired Interference Source Signal STEP2: Estimatie The Weight Vector of A Null Steering Beamformer STEP3:Inverse of Steering Beamformer STEP4:Find Weights Vector by using equation W=S-1ZT STEP5:Plot

ahp算法源程序，希望大家一起交流共同进取拉拉阿拉(the program of AHP)

Help psoToolbox V1.0 psoToolbox provides an interective GUI based Toolbox to solve optimization problems using particle swarm optimization. Creat a fitness function in M-file. Inputs: Function : Function handle of fitness function. Nvars : Number of variable to be optimized. LB : Lower Bound of Nvars (1 X Nvars) UB : Upper Bound of Nvars (1 X Nvars) Parameters: C1 : Cognative Attraction C2 : Social Attraction W : Inertial Population Size : Number of Swarms Max Iterations : Maximum number of epochs. Click on " RUN PSO " button to start PSO search. You will get the out put at Edit box below the axes.

说明：  Kalman 滤波实际应用，对于初学者来说是一份非常好的学习参考资料，呵呵！(The practical application of Kalman filtering, for beginners is a very good learning references, Ha ha!)

describe the chaotic map and its precision IEEE.584 or fix and the measure of periodicity

焊接应力分析很好的代码 对于焊接专业很有帮助(Good welding stress analysis code for the welding professional helpful)

Dimensionality Reduction for Distributed Estimation in the Infinite Dimensional Regime

首先从n个数据对象任意选择 k 个对象作为初始聚类中心；而对于所剩下其它对象，则根据它们与这些聚类中心的相似度（距离），分别将它们分配给与其最相似的（聚类中心所代表的）聚类；然后再计算每个所获新聚类的聚类中心（该聚类中所有对象的均值）；不断重复这一过程直到标准测度函数开始收敛为止。一般都采用均方差作为标准测度函数. k个聚类具有以下特点：各聚类本身尽可能的紧凑，而各聚类之间尽可能的分开。(First, a data object from the n choose k objects as initial cluster centers and for the rest of the other objects, according to their similarity with the cluster center (distance), respectively, assign them to their most similar (represented by cluster center) clustering then calculated for each cluster center received a new clustering (all objects in the cluster mean) repeats this process until the convergence criteria begin until the measure function. Standard deviation is generally used as a standard measure function. K a cluster has the following characteristics: the cluster itself as a compact, but separated as much as possible between each cluster.)

用于计算二维光子晶体（用于计算二维光子晶体）(Used to calculate the two-dimensional photonic crystals (used to calculate the two-dimensional photonic crystal))