gradsmetafile.m

本文中有两个函数m文件：model126.m是一个用于预测的完全没有用工具箱函数的补偿模糊神经网络主程序，用于仿真、对比训练数据和网络输出的差异；cb.m是一个非线性系统的数学模型，在model126.m中用“ode45”函数求解这个数学模型后，可以得到105个x1(t)、x2(t)和y(t)，从而建立起一个两输入一输出的补偿模糊神经网络。(failed to translate)

多天线系统小区用户最佳预编码顺序的确定.介绍了一种在实际下行广播信道中采用脏纸编码（DPC）设计系统的方法，其 中基站发送端为多天线，接收端为单天线。主要的目标是在下行信道中满足每个用户的最小 速率要求和最大误码率要求的条件下最小化总体的传输功率(Cell users the best pre-coding sequence to determine a multi-antenna system. Describes an actual downlink broadcast channel using dirty paper coding (DPC) design system, wherein the multi-antenna base station transmit side, the receiving terminal as a single antenna. The main goal is to meet the conditions required by the minimum rate and maximum bit error rate of each user to minimize the overall transmission power in the downlink channel)

K-mean matlab code example

关于Matlab中曲线的插值与拟合的程序代码(Interpolation and fitting)

用传输矩阵的方法和Matlab技术研究光子晶体的缺陷模式(With the transfer matrix method and Matlab research photonic crystal defect mode)

数值积分 romberg matlab编程(Numerical integration romberg matlab programming)

ant colony optimization technique for JSSP

基于模糊控制的变速恒频风力发电最大风能跟踪研究(The maximum follow-up study of wind speed constant frequency wind power generation based on fuzzy control)

Space-phasor model of induction motor

多目标优化 相对传统多目标优化方法, PSO在求解多目标问题上具有很大优势。首先, PSO的高效搜索能力有利于得到多目标意义下的最优解 其次, PSO通过代表整个解集的种群按内在的并行方式同时搜索多个非劣解,因此容易搜索到多个Pareto 最优解 再则, PSO的通用性使其适合于处理所有类型的目标函数和约束 另外, PSO 很容易与传统方法相结合,进而提出解决特定问题的高效方法。就PSO 本身而言,为了更好地解决多目标优化问题,必须解决全局最优粒子和个体最优粒子的选择问题(Compared with the traditional multi-objective optimization of multi-objective optimization method, PSO in solving multi-objective problem has a great advantage. First, PSO is conducive to the efficient search capabilities are more objective sense of the optimal solution Secondly, PSO representative of the entire solution set through the population by way of the inherent parallel search multiple non-inferior solution, and this can easily search for the most number of Pareto optimal solution Furthermore, PSO' s versatility make it suitable for handling all types of objective function and constraints addition, PSO is easy to integrate with the traditional method, and then propose an efficient way to solve specific problems. The PSO itself, in order to better address the multi-objective optimization problems, the need to address the global best particle and the individual selection of the optimal particle)