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chapter19
- 概率神经网络的分类预测--基于PNN的变压器故障诊断，了解神经网络在实践中的作用-Classification prediction of the probabilistic neural network- PNN-based transformer fault diagnosis, understanding the role of neural networks in practice
PNN-classification-prediction
- 针对变压器的故障诊断，通过概率神经网络对其进行分类。运行程序后，网络输出值和实际值非常接近，很好的实现了对变压器的故障分类。-For the transformer fault diagnosis, we use the probability neural network to classify it. After running the program, the network output value and the actual value is very close, which ac
newpnn
- 一个简易的基于概率神经网络的语音识别与训练系统-A simple probabilistic neural network-based speech recognition and training systems
SOM-PNN
- 本文将SOM（自组织特征映射）和PNN（概率）神经网络应用到柴油机故障诊断中，首先介绍了SOM和PNN神经网络的算法，然后对柴油机的故障进行了分析，并运用matlab进行了仿真，验证了实验结果。-This article will SOM (self-organizing feature map) and PNN (probability) neural network is applied to fault diagnosis of diesel engine, first introduce
lingyie_v38
- 包括最小二乘法、SVM、神经网络、1_k近邻法,从先验概率中采样，计算权重，使用matlab实现智能预测控制算法。- Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Sampling a priori probability, calculate the weight, Use matlab intelligent predictive control algorithm.
nouhui
- BP神经网络用于函数拟合与模式识别，从先验概率中采样，计算权重，添加噪声处理。- BP neural network function fitting and pattern recognition, Sampling a priori probability, calculate the weight, Add noise processing.
funming_v22
- 最大似然（ML）准则和最大后验概率（MAP）准则，关于神经网络控制，包括四元数的各种计算。- Maximum Likelihood (ML) criteria and maximum a posteriori (MAP) criterion, On neural network control, Including quaternion various calculations.
gengmang_v39
- 包括回归分析和概率统计，数据模型归一化，模态振动，BP神经网络的整个训练过程。- Including regression analysis and probability and statistics, Normalized data model, modal vibration, The entire training process BP neural network.
jiugiu_v48
- 主要是基于mtlab的程序，关于神经网络控制，包括回归分析和概率统计。- Mainly based on the mtlab procedures, On neural network control, Including regression analysis and probability and statistics.
ncle
- 一个给基于神经网络算法编写的预测某类博彩事件发生概率的程序-A neural network algorithm for the preparation of a forecast of the probability of the occurrence of a gambling
baomen_v52
- 包括回归分析和概率统计，基于kaiser窗的双谱线插值FFT谐波分析，基于人工神经网络的常用数字信号调制。- Including regression analysis and probability and statistics, Dual-line interpolation FFT harmonic analysis kaiser windows, The commonly used digital signal modulation based on artificial neural
muijeng_v11
- 最大似然（ML）准则和最大后验概率（MAP）准则，基于人工神经网络的常用数字信号调制，本科毕设要求参见标准测试模型。- Maximum Likelihood (ML) criteria and maximum a posteriori (MAP) criterion, The commonly used digital signal modulation based on artificial neural network, Undergraduate complete set requirem
penling_v17
- 最大似然（ML）准则和最大后验概率（MAP）准则，利用matlab写成的窄带噪声发生，BP神经网络的整个训练过程。- Maximum Likelihood (ML) criteria and maximum a posteriori (MAP) criterion, Using matlab written narrowband noise occurs, The entire training process BP neural network.
kuigai
- 通过matlab代码，关于神经网络控制，从先验概率中采样，计算权重。- By matlab code, On neural network control, Sampling a priori probability, calculate the weight.
fenleiyuce
- matlab关于概率神经网络的分类预测应用程序(Matlab classification prediction applications on Probabilistic Neural Networks)
libsvm-3.1-[FarutoUltimate3.1Mcode]
- 态势要素获取作为整个网络安全态势感知的基础，其质量的好坏将直接影响态势感知系统的性能。针对态势要素不易获取问题，提出了一种基于增强型概率神经网络的层次化框架态势要素获取方法。在该层次化获取框架中，利用主成分分析(PCA)对训练样本属性进行约简并对特殊属性编码融合处理，将其结果用于优化概率神经网络(PNN)结构，降低系统复杂度。以PNN作为基分类器，基分类器通过反复迭代、权重更替，然后加权融合处理形成最终的强多分类器。实验结果表明，该方案是有效的态势要素获取方法并且精确度达到95.53%，明显优于
libsvm-3.17
- 为了真实有效地提取网络安全态势要素信息，提出了一种基于增强型概率神经网络的层次化框架态势要素获取方法。在该层次化态势要素获取框架中，根据Agent节点功能的不同，划分为不同的层次。利用主成分分析(Principal Component Analysis, PCA)对训练样本属性进行约简并对特殊属性编码融合处理，按照处理结果改进概率神经网络(Probabilistic Neural Network, PNN)结构，以降低系统复杂度。然后以改进的PNN作为基分类器，结合自适应增强算法，通过基分类器反
基于概率神经网络的柴油机故障诊断
- matlab神经网络与实例精解 基于概率神经网络的柴油机故障诊断(Diesel Engine Fault Diagnosis Based on Probabilistic Neural Network)
BP神经网络与多项式拟合曲线
- BP神经网络与多项式拟合曲线，数据统计描述，神经网络模型，概率统计建模的理论和方法。(BP neural network and polynomial fitting curve, data statistical descr iption, neural network model, probability and statistics modeling theory and method.)
概率神经网络故障诊断
- 概率神经网络故障诊断的实例，一个简单的应用，亲测有效，可以完美运行，适合初学者，(An example of probabilistic neural network fault diagnosis, a simple application, pro-test is effective, can run perfectly, suitable for beginners,)