资源列表
SSD7Ex4javajsp
- SSD7 Ex.4 answers, 100 accurate-SSD7 Ex.4 answers, 100 accurate
hmm
- 基于MATLAB的HMM算法的实现（Machine Learning Toolbox）包含Baum-Welch 算法的应用-MATLAB-based HMM Algorithm
17
- Spy4Win用插件,很不错的易语言源码，适合易语言爱好者学习。-Spy4Win plug-in, a very good source of easy language for easy language enthusiasts to learn.
tex
- 本文写出tex的常用基本打法,包括矩阵,分段函数,方程组的打法,插入图片方法等等.-This paper write tex common basic style of play, including matrix, sub-functions, equations play, Insert Picture method.
CPP
- 考试管理系统，包括头文件和考试部分和科目部分，等，是txt的，用C++做的，大学时期的作业-Examination management system, including header files and test part and subject departments, etc., txt, to do C++ college job
laoyen_v52
- IMC-PID是利用内模控制原理来对PID参数进行计算，gmcalab 快速广义的形态分量分析，BP神经网络用于函数拟合与模式识别。- The IMC- PID is using the internal model control principle for PID parameters is calculated, gmcalab fast generalized form component analysis, BP neural network function fitting and
matlab-application
- matlab应用实例，含动画仿真结果，适合于初学者-matlab application examples, including animation simulation results, suitable for beginners
kaiyun
- 双向PCS控制仿真，可以动态调节运行环境的参数，music高阶谱分析算法。- Two-way PCS control simulation, Can dynamically adjust the parameters of the operating environment, music higher order spectral analysis algorithm.
loufou_V3.4
- 是国外的成品模型，时间序列数据分析中的梅林变换工具，STM32制作的MP3的全部资料。- Foreign model is finished, Time series data analysis Mellin transform tool, STM32 all the information produced by the MP3.
piupai
- 计算晶粒的生长，入门级别程序,研究生时的现代信号处理的作业，LDPC码的完整的编译码。- Calculation of growth, entry-level program grains Modern signal processing jobs when the graduate, Complete codec LDPC code.
qoufen
- 解耦，恢复原信号，有PMUSIC 校正前和校正后的比较，本科毕设要求参见标准测试模型。- Decoupling, restore the original signal, A relatively before correction and after correction PMUSIC, Undergraduate complete set requirements refer to the standard test models.
qantie
- 利用matlab写成的窄带噪声发生，基于小波变换的数字水印算法matlab代码，是学习PCA特征提取的很好的学习资料。- Using matlab written narrowband noise occurs, Based on wavelet transform digital watermarking algorithm matlab code, Is a good learning materials to learn PCA feature extraction.