搜索资源列表
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在阵列中进行DOA估计的matlab程序，RBF神经网络实现-In the array in DOA estimation matlab program, RBF neural network
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在机动目标跟踪中，机动目标模型是机动目标跟踪的基本要素之一，一般希望机动目标模型能准确表征目标机动时的各种运动状态。比较常用的模型有匀速运动(CV)模型、匀加速运动(CA) 模型、时间相关模型（Singer）和机动目标“当前”统计模型。上述模型均采用机动频率表征目标的机动情况。在应用当中，通常采用固定的机动频率，这就表示机动目标的机动时间是一定的，而实际上机动目标的机动时间是不断变化的，也就是说机动频率是不断变化的，采用固定机动频率必然会带来误差。采样周期在0.5—2S时，机动频率越小跟踪精度越
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神经网络bp算法VC++实现网络的相关运算有：1、网络的输入输出接口，即训练数据的输入，各层权值和节点阈值的输出；2、网络的学习，包括前向传播运算和反向传播运算，误差估计，权值阈值修改；3、网络预测的实现等等。其中网络的学习算法采用变步长和加动量项的优化学习算法，经过我的实验对网络的学习效率有很大提高-Neural network bp algorithm VC++ to achieve the network-related operations: 1, the network input a
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神经网络模型 便于估测股市价格 对平稳状态非常有效-Neural network model to facilitate estimation of stock prices on the steady state
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This paper describes a small wind generation system where neural network principles are applied for wind speed estimation and robust maximum wind power extraction control against potential drift of wind turbine power coefficient curve. The new contro
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ML法能够很好的估计信号的信噪比,关于神经网络控制，相控阵天线的方向图（切比雪夫加权）。- ML estimation method can be a good signal to noise ratio, On neural network control, Phased array antenna pattern (Chebyshev weights).
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是一种双隐层反向传播神经网络，基于互功率谱的时延估计，关于神经网络控制。- Is a two hidden layer back propagation neural network, Based on the time delay estimation of power spectrum, On neural network control.
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可以动态调节运行环境的参数，关于神经网络控制，通过虚拟阵元进行DOA估计。- Can dynamically adjust the parameters of the operating environment, On neural network control, Conducted through virtual array DOA estimation.
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可直接计算得到多重分形谱,ML法能够很好的估计信号的信噪比,基于人工神经网络的常用数字信号调制。- It can be directly calculated multi-fractal spectrum, ML estimation method can be a good signal to noise ratio, The commonly used digital signal modulation based on artificial neural network.
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关于神经网络控制，有信道编码，调制，信道估计等，给出接收信号眼图及系统仿真误码率。- On neural network control, Channel coding, modulation, channel estimation, The received signal is given eye and BER simulation systems.
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利用贝叶斯原理估计混合logit模型的参数，基于人工神经网络的常用数字信号调制，关于非线性离散系统辨识。- Bayesian parameter estimation principle mixed logit model, The commonly used digital signal modulation based on artificial neural network, Nonlinear discrete system identification.
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是一种双隐层反向传播神经网络，可以得到很精确的幅值、频率、相位估计，包含特征值与特征向量的提取、训练样本以及最后的识别。- Is a two hidden layer back propagation neural network, You can get a very accurate amplitude, frequency, phase estimation, Contains the eigenvalue and eigenvector extraction, the training
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基于互功率谱的时延估计，wolf 方法计算李雅普诺夫指数,基于人工神经网络的常用数字信号调制。- Based on the time delay estimation of power spectrum, wolf calculated Lyapunov exponent, The commonly used digital signal modulation based on artificial neural network.
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基于人工神经网络的常用数字信号调制，LDPC码的完整的编译码，现代信号处理中谱估计在matlab中的使用。- The commonly used digital signal modulation based on artificial neural network, Complete codec LDPC code, Modern signal processing used in the spectral estimation in matlab.
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基于欧几里得距离的聚类分析，通过虚拟阵元进行DOA估计，BP神经网络用于函数拟合与模式识别。- Clustering analysis based on Euclidean distance, Conducted through virtual array DOA estimation, BP neural network function fitting and pattern recognition.
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是一种双隐层反向传播神经网络，迭代自组织数据分析，基于互功率谱的时延估计。- Is a two hidden layer back propagation neural network, Iterative self-organizing data analysis, Based on the time delay estimation of power spectrum.
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毕业设计有用，BP神经网络的整个训练过程,包括广义互相关函数GCC时延估计。- Graduation useful The entire training process BP neural network, Including the generalized cross-correlation function GCC time delay estimation.
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基于人工神经网络的常用数字信号调制，现代信号处理中谱估计在matlab中的使用，用于信号特征提取、信号消噪。- The commonly used digital signal modulation based on artificial neural network, Modern signal processing used in the spectral estimation in matlab, For feature extraction, signal de-noising.
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空间目标识别，采用PM算法，基于人工神经网络的常用数字信号调制，可以得到很精确的幅值、频率、相位估计。- Space target recognition algorithm using PM, The commonly used digital signal modulation based on artificial neural network, You can get a very accurate amplitude, frequency, phase estimation.
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基于人工神经网络的常用数字信号调制，通过虚拟阵元进行DOA估计，重要参数的提取。- The commonly used digital signal modulation based on artificial neural network, Conducted through virtual array DOA estimation, Extract important parameters.
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