登录
首页 » matlab » ClassificationMatLabToolbox

ClassificationMatLabToolbox

于 2007-09-12 发布 文件大小:622KB
0 101
下载积分: 1 下载次数: 41

代码说明:

  关于MATLAB分类的工具箱。 希望对你有所帮助!(MATLAB toolbox on the classification. I hope for your help!)

文件列表:

Classification MatLab Toolbox
.............................\Classification MatLab Toolbox

.............................\.............................\Ada_Boost.m
.............................\.............................\ADDC.m
.............................\.............................\AGHC.m
.............................\.............................\Backpropagation_Batch.m
.............................\.............................\Backpropagation_CGD.m
.............................\.............................\Backpropagation_Quickprop.m
.............................\.............................\Backpropagation_Recurrent.m
.............................\.............................\Backpropagation_SM.m
.............................\.............................\Backpropagation_Stochastic.m
.............................\.............................\Balanced_Winnow.m
.............................\.............................\Bayesian_Model_Comparison.m
.............................\.............................\Bhattacharyya.m
.............................\.............................\BIMSEC.m
.............................\.............................\C4_5.m
.............................\.............................\calculate_error.m
.............................\.............................\calculate_region.m
.............................\.............................\CART.m
.............................\.............................\CARTfunctions.m
.............................\.............................\Cascade_Correlation.m
.............................\.............................\Chernoff.m
.............................\.............................\chess.mat
.............................\.............................\Classification.txt
.............................\.............................\classification_error.m
.............................\.............................\classifier.m
.............................\.............................\classifier.mat
.............................\.............................\classifier_commands.m
.............................\.............................\click_points.m
.............................\.............................\clouds.mat
.............................\.............................\Competitive_learning.m
.............................\.............................\Components_without_DF.m
.............................\.............................\Components_with_DF.m
.............................\.............................\contents.m
.............................\.............................\decision_region.m
.............................\.............................\Deterministic_annealing.m
.............................\.............................\Deterministic_Boltzmann.m
.............................\.............................\Deterministic_SA.m
.............................\.............................\Discrete_Bayes.m
.............................\.............................\Discriminability.m
.............................\.............................\DSLVQ.m
.............................\.............................\EM.m
.............................\.............................\enter_distributions.m
.............................\.............................\enter_distributions.mat
.............................\.............................\enter_distributions_commands.m
.............................\.............................\feature_selection.m
.............................\.............................\feature_selection.mat
.............................\.............................\Feature_selection.txt
.............................\.............................\feature_selection_commands.m
.............................\.............................\FindParameters.m
.............................\.............................\FindParameters.mat
.............................\.............................\FindParametersFunctions.m
.............................\.............................\find_classes.m
.............................\.............................\FishersLinearDiscriminant.m
.............................\.............................\fuzzy_k_means.m
.............................\.............................\GaussianParameters.m
.............................\.............................\GaussianParameters.mat
.............................\.............................\generate_data_set.m
.............................\.............................\Genetic_Algorithm.m
.............................\.............................\Genetic_Culling.m
.............................\.............................\Genetic_Programming.m
.............................\.............................\Gibbs.m
.............................\.............................\HDR.m
.............................\.............................\high_histogram.m
.............................\.............................\Ho_Kashyap.m
.............................\.............................\ICA.m
.............................\.............................\ID3.m
.............................\.............................\Infomat.m
.............................\.............................\Interactive_Learning.m
.............................\.............................\Kohonen_SOFM.m
.............................\.............................\Koller.m
.............................\.............................\k_means.m
.............................\.............................\Leader_Follower.m
.............................\.............................\LMS.m
.............................\.............................\load_file.m
.............................\.............................\Local_Polynomial.m
.............................\.............................\LocBoost.m
.............................\.............................\LocBoostFunctions.m
.............................\.............................\loglikelihood.m
.............................\.............................\LS.m
.............................\.............................\LVQ1.m
.............................\.............................\LVQ3.m
.............................\.............................\make_a_draw.m
.............................\.............................\Marginalization.m
.............................\.............................\MDS.m
.............................\.............................\Minimum_Cost.m
.............................\.............................\min_spanning_tree.m
.............................\.............................\ML.m
.............................\.............................\ML_diag.m
.............................\.............................\ML_II.m
.............................\.............................\multialgorithms.m
.............................\.............................\multialgorithms.mat
.............................\.............................\multialgorithms_commands.m
.............................\.............................\Multivariate_Splines.m
.............................\.............................\NDDF.m
.............................\.............................\NearestNeighborEditing.m
.............................\.............................\Nearest_Neighbor.m
.............................\.............................\NLPCA.m
.............................\.............................\None.m

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • F16_dyn
    F16开环动力学系统模型。输入为油门及三个舵面控制,以及系统低阶、高阶选择器。输出为飞机飞行状态以及油门大小。采用Quaternion四元素法,可以有效避免俯仰角theta达到90°时数学奇异现象。(F16 open-loop dynamic system model. Throttle input and three rudder control, and system low, high selector. Output is the size of aircraft status, and throttle. Quaternion method using the four elements, can effectively avoid the pitch angle theta to 90 °, mathematics strange phenomenon.)
    2011-09-30 11:04:51下载
    积分:1
  • fce_choice
    This function detect the area with the maximum power in a time/frequency signal. Developped by Etienne Combrisson.
    2012-08-13 19:28:21下载
    积分:1
  • 数字下变频仿真
    说明:  MATLAB仿真程序,用于数字下变频,并附有代码(For digital down conversion)
    2020-04-24 16:30:10下载
    积分:1
  • radar-ambiguity-
    雷达模糊函数、模拟距离模糊函数,多普勒模糊函数,用多种视图显示模拟结果。(Radar ambiguity function, analogue distance ambiguity function, the Doppler ambiguity function, using a variety of view shows the simulation results.)
    2013-05-30 10:52:19下载
    积分:1
  • IIR
    IIR滤波器设计,分离出250hz,500hz,1000hz单频(IIR filter design, isolated 250hz, 500hz, 1000hz single frequency)
    2010-12-27 20:09:32下载
    积分:1
  • 8
    说明:  精通MATLAB优化计算算法,约束优化问题。共7个程序。(Proficient in MATLAB optimization calculation algorithm, constrained optimization problem. A total of seven procedures.)
    2010-03-24 09:46:55下载
    积分:1
  • zixiangguanjiqilvbo
    说明:  怎么在函数中加入高斯噪声,白噪声,比较它们的性质,并求它们的相关性(how to insert gaosi noise,white noise and then how to konw their correlation)
    2011-03-16 15:17:40下载
    积分:1
  • 1
    说明:  salam. this file is about cryptography.
    2009-12-31 20:38:42下载
    积分:1
  • pid
    以中等纯度的精馏塔为研究对象,考虑到不等分子溢流的影响和非理想的汽液平衡,可以得到塔顶产品轻组分含量Y与回流量L之间的传递函数为: 其中由于现场环境干扰,输出带有测量噪声是(0,1)的正态分布序列,它的方差为ɑ=0.5。由于输出中带有很大的噪声信号,故将数字滤波技术中常见的低通滤波器由于偏差控制,滤掉其中的噪声信号,然后在对其进行PID计算,得到实际的控制量。在该方法在噪声较强的环境下,可以得到较好的控制效果。 控制要求: 1、 采用带低通滤波器的增量式PID将塔顶轻组分含量控制在0.99 2、 用ISTE法整定PID参数 3、 在控制过程中,到150周期加入幅度为-20 的阶跃干扰,在第300周期干扰消失。分析PID参数的抗干扰性。 (Medium-purity distillation column, taking into account ranging from molecular overflow and non-ideal vapor-liquid equilibrium, the transfer function between the light component content of the top product Y and the back flow L can be obtained as: Because the field of environmental interference, output with the measurement noise is a normal distribution (0,1) sequence, its variance ɑ = 0.5. As with a lot of noise in the output signal, it will be common in low-pass filter in the digital filtering techniques due to the bias control, filter out the noise signal, then its PID, the actual amount of control. In the strong noise environment, you can get better control effect. Control requirements: 1, using the incremental PID with a low-pass filter to the top of the tower content of light components in the 0.99 2, the method of ISTE tuning PID parameters 3, in the control process, to 150 cycles join the range of-20 step disturbance, disappearance of interference in the first 300 c)
    2012-04-07 07:27:43下载
    积分:1
  • xuanzhi
    缴费点选址最优化模型代码,超强推荐,数学建模经典例子(Payment Point location optimization model code, super recommended classic example of mathematical modeling)
    2011-05-14 23:25:53下载
    积分:1
  • 696518资源总数
  • 104349会员总数
  • 32今日下载