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Newton-Raphson 算法 用来求解非线性方程组根的解法(Newton-Raphson algorithm used to solve the root of the solution of nonlinear equations)

此函数专为求解球贝塞尔函数 利用柱贝塞尔函数与球贝塞尔函数之间的关系得到 十分实用 (This function is designed to solve the spherical Bessel functions to take advantage of the relationship between the column Bessel functions and spherical Bessel functions very useful)

ARMA 时间序列建模、预测、检验和说明(ARMA MODELS IN EVIEWS AND DOCUMENTS)

his packet is a IS-95 baseband simulation for 1 data channel of 9.6 KBps rate. The simulation is written for static channel and AWGN noise. The packet include: 1) Packet Builder (Viterbi Encoding, Interleaver, PN generation) 2) Modulator (RRC filter) 3) Demodulator (Matched Filter, RAKE receiver) 4) Receiver (HD or SD) (Deinterleaver, Viterbi Decoder). You should run "Simulation.m" function that include all modules.(his packet is a IS-95 baseband simulation f or a data channel rate of 9.6 KBps. The simulatio n is written for static channel and AWGN noise. T he packet include : 1) Packet Builder (Viterbi Encoding. Interleaver. PN generation) 2) Modulator (RRC filter) 3) Dem odulator (Matched Filter, RAKE receiver) 4) Receiver (HD or SD) (Deinterl eaver. Viterbi Decoder). You should run "Simulation. m "function that include all modules.)

功能描述见readme文档 包括GPS的捕获算法和数据(Function described in readme file including GPS algorithms and data capture)

with simplified pulse coupled neural network for image binary segmentation of surveying and mapping science and technology papers

说明：  EMD处理数据的全流程以及经验模态分解的一个经典例子(The whole process of EMD data processing and a classical example of empirical mode decomposition)

在MATLAB中用遗传算法解决TSP问题。(solve the TSP problem by GA)

cybership 2 model supply vessel

1-8遗传算法，9 多目标Pareto最优解搜索算法，10 基于多目标Pareto的二维背包搜索算法，11-12免疫算法，13-17粒子群算法，18鱼群算法，19-21模拟退火算法，22-24蚁群算法，25-27神经网络，28 支持向量机的分类，29 支持向量机的回归拟合，30 极限学习机的回归拟合及分类(1-8 genetic algorithm, 9 multi-objective Pareto optimal solution search algorithm, 10 multi-objective Pareto based two-dimensional knapsack search algorithm, 11-12 immune algorithm, 13-17 particle swarm algorithm, 18 fish swarm algorithm, 19-21 simulated annealing algorithm, 22-24 ant colony algorithm, 25-27 neural network, 28 support vector machine classification, 29 support vector machine regression simulation Regression Fitting and Classification of Combination, 30 Extreme Learning Machines)