一个模式识别领域的数据处理调试程序

MATLAB Simulink Conference 2007 cd1

ASK,FSK,PSK信号调制及用FFT法进行频率估计(ASK signal modulation and the FFT algorithm with frequency estimation)

说明：  二进制差分编码解码，二进制差分相移键控二进制幅移键控，二进制相移键控，二进制频移键控最小频移键控的调制与解调(Differential encoding and decoding binary, binary differential phase shift keying binary amplitude shift keying, BPSK, binary frequency shift keying Minimum Shift Keying modulation and demodulation)

说明：  最优化算法中得拟牛顿法和共轭梯度法,以及Armijo型搜索和最优步长搜索。(Congradient and NiNewton algorithms in optimazation based on MATLAB)

说明：  matlab simulink 用于学习VIENNA整流器 只用 仅供参考学习(matlab simulink For reference only)

klt经典程序，可以在matlab里面直接运行(KLT classical procedures, can be run directly in matlab)

给出了用MATLAB实现FFT算法，压缩包内还有源程序(Given by MATLAB realize FFT algorithm, compressed source code package also)

说明：  近年来，基于启发式的多目标优化技术得到了很大的发展，研究表明该技术比经典方法更实用和高效。有代表性的多目标优化算法主要有NSGA、NSGA-II、SPEA、SPEA2、PAES和PESA等。粒子群优化(PSO)算法是一种模拟社会行为的、基于群体智能的进化技术，以其独特的搜索机理、出色的收敛性能、方便的计算机实现，在工程优化领域得到了广泛的应用，多目标PSO(MOPSO)算法应用到了不同的优化领域[9~11]，但存在计算复杂度高、通用性低、收敛性不好等缺点。 多目标粒子群（MOPSO）算法是由CarlosA. Coello Coello等在2004年提出来的(In recent years, heuristic-based multi-objective optimization technology has been greatly developed, and research shows that this technology is more practical and efficient than classical methods. Representative multi-objective optimization algorithms mainly include NSGA, NSGA-II, SPEA, SPEA2, PAES and PESA. Particle Swarm Optimization (PSO) algorithm is an evolutionary technology based on swarm intelligence that simulates social behavior. With its unique search mechanism, excellent convergence performance, and convenient computer implementation, it has been widely used in the field of engineering optimization. The objective PSO (MOPSO) algorithm is applied to different optimization fields [9~11], but it has shortcomings such as high computational complexity, low versatility, and poor convergence. The multi-objective particle swarm optimization (MOPSO) algorithm was proposed by Carlos A. Coello Coello et al. in 2004)

NP是美国匹兹堡大学的T.L.Saaty 教授于1996年提出了一种适应非独立的递阶层次结构的决策方法，它是在网络分析法（AHP）基础上发展而形成的一种新的实用决策方法。其关键步骤有以下几个： 1 确定因素，并建立网络层和控制层模型。 2 创建比较矩阵。 3 按照指标类型针对每列进行规范化。 4 求出每个比较矩阵的最大特征值和对应的特征向量。 5 一致性检验。如果不满足，则调整相应的比较矩阵中的元素。 6 将各个特征向量单位化(归一化)，组成判断矩阵。 7 将控制层的判断矩阵和网络层的判断矩阵相乘，得到加权超矩阵。 8 将加权超矩阵单位化(归一化)，求其K次幂收敛时的矩阵。其中第j列就是网络层中各元素对于元素j的极限排序向量。 (NP is a professor at the University of Pittsburgh TLSaaty presented in 1996, an adaptation of non-independent Hierarchy of decision-making method, which is the analytic network process (AHP) formed on the basis of the development of a new and practical decision-making method . The key steps are the following: A determining factor, and a network layer and control layer model. 2 create a comparison matrix. For each of the three types of indicators in accordance with normalized columns. 4 find the maximum for each comparison matrix eigenvalue and the corresponding eigenvectors. 5 consistency test. If not satisfied, then the comparison to adjust the corresponding matrix elements. 6 will each feature vector units of (normalized), to determine the composition of matrix. 7 to determine the control layer and network layer to determine matrix matrix multiplication, to be weighted super-matrix. 8 of the weighted super-matrix units of (normalized), seeking the powe)

describe a gaussian beam 2D