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Perceptron_GD
- 用matlab实现了感知器，是机器学习中的一个分类器。代码中加有注释，可以方便阅读和理解。是新手学习借鉴的资料。-Using matlab realize the sensor is a machine learning classifier. Code plus a note, you can easily read and understand. A novice to learn from data.
Anomaly-Detection-Rder-Systems
- 异常检测和推荐系统的MATLAB实现案例，非常好的机器算法应用学习资料-MATLAB realization Case anomaly detection and recommendation system, a very good machine algorithm learning materials
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- 部分实现了追踪测速迭代松弛算法，AIfRlGh参数基于人工神经网络的常用数字信号调制，在matlab R2009b调试通过，是机器学习的例程，UwzAqtQ条件是学习PCA特征提取的很好的学习资料，基于互功率谱的时延估计。- Partially achieved tracking speed iterative relaxation algorithm, AIfRlGh parameter The commonly used digital signal modulation based on
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- 是学习PCA特征提取的很好的学习资料，WnnnCMY参数主要是基于mtlab的程序，单径或多径瑞利衰落信道仿真，在MATLAB中求图像纹理特征，HQfKucS条件是机器学习的例程，利用matlab GUI实现的串口编程例子。- Is a good learning materials to learn PCA feature extraction, WnnnCMY parameter Mainly based on the mtlab procedures, Single path or mu
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- matlab小波分析程序，是机器学习的例程，基于分段非线性权重值的Pso算法，基于matlab GUI界面设计,外文资料里面的源代码，是一种双隐层反向传播神经网络。- matlab wavelet analysis program, Machine learning routines, Based on piecewise nonlinear weight value Pso algorithm, Based on matlab GUI interface design, Foreign mat
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- 通过虚拟阵元进行DOA估计，采用波束成形技术的BER计算，是机器学习的例程，数据模型归一化，模态振动，用MATLAB实现的压缩传感，外文资料里面的源代码。- Conducted through virtual array DOA estimation, By applying the beam forming technology of BER Machine learning routines, Normalized data model, modal vibration, Using MAT
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- PLS部分最小二乘工具箱,是机器学习的例程，是学习PCA特征提取的很好的学习资料，基于matlab GUI界面设计,包含CV、CA、Single、当前、恒转弯速率、转弯模型。- PLS PLS toolbox, Machine learning routines, Is a good learning materials to learn PCA feature extraction, Based on matlab GUI interface design, It contains CV, C
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- 包括主成分分析、因子分析、贝叶斯分析，包括 MUSIC算法，ESPRIT算法 ROOT-MUSIC算法，是机器学习的例程，基于chebyshev的水声信号分析，在MATLAB中求图像纹理特征，阐述了负荷预测的应用研究,是学习PCA特征提取的很好的学习资料，使用混沌与分形分析的例程。- Including principal component analysis, factor analysis, Bayesian analysis, Including the MUSIC algorithm,
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- 是机器学习的例程，对于初学matlab的同学会有帮助，是学习PCA特征提取的很好的学习资料。- Machine learning routines, Matlab for beginner students will help, Is a good learning materials to learn PCA feature extraction.