搜索资源列表
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rbf训练用于模式识别，分类，故障类别的诊断，收敛快-rbf training for pattern recognition, classification, fault type of diagnosis, fast convergence
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线性神经网络，BP神经网络，Hopfield神经网格，Elman神经网络，RBF神经网络；在模型应用模块中实现了六种实际应用：RBF网络的船用柴油机故障诊断，BP网络的齿轮箱故障诊断，SOM网络的回热系统故障诊断，BP网络的设备状态分类器，SOM网络的人口比例样本分类，SOM网络的土壤性状样本分类。-Linear neural network, BP neural network, Hopfield neural network, Elman neural network, RBF neural
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故障诊断 基于BP神经网络的故障诊断系统 1生成BP网络2.训练和自适应调整-Fault diagnosis based on BP neural network fault diagnosis system for a generation of BP network 2. Training and adaptive adjustment
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rbf神经网络用于分类识别，故障诊断，模式识别，自己编写的-rbf neural network for classification and recognition, fault diagnosis, pattern recognition, have written
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RBFNN single phase fault classification
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使用MATLAB编程 实现了基于BP神经网络的故障分类功能 -Using the MATLAB programming based on BP neural network fault classification function
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pnn神经网络算法，通过训练后，可实现对电力变压器故障的分类-pnn neural network algorithm, by training, can be realized on the power transformer fault classification
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采用matlab的神经网络工具箱对故障进行分类-Matlab neural network toolbox used for fault classification
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神经网络案例：SOM神经网络的数据分类--柴油机故障诊断-Neural network case: SOM neural network data classification- Diesel Engine Fault Diagnosis
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陆振波——支持向量机方法——故障分类程序-Lu Zhenbo- support vector machines- Fault classification procedures
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人工智能——神经网络——故障诊断分类程序-Artificial intelligence- neural networks- Fault diagnostic classification procedures
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在柴油机故障诊断中进行故障分类的RBF的程序设计-In the diesel engine fault diagnosis for fault classification of RBF program design
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概率神经网络的分类预测 基于PNN的变压器故障诊断-The classification of probabilistic neural network prediction- transformer fault diagnosis based on PNN
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用粒子群算法PSO,优化支持向量机SVM，提高故障分类精度。-Using particle swarm optimization (PSO, optimization of support vector machine SVM, improve the fault classification accuracy.
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用简单粒子群算法PSO优化支持向量机SVM，提高故障分类精度。-Using particle swarm optimization (PSO, optimization of support vector machine SVM, improve the fault classification accuracy.
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人工鱼群算法AFSA优化支持向量机SVM,提高故障分类精度。-Artificial fish algorithm AFSA optimization support vector machine SVM, improve the fault classification accuracy.
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将数量较少的故障样本分为训练集和测试集，实现故障的分类和识别(A small number of fault samples are divided into training set and test set to realize fault classification and recognition.)
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处理一维振动信号，用于故障分类，模式识别(1D CNN for fault classification)
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bp神经网络，用于风力发电机的故障分类，可用于二分类和多分类，已经线性回归和非线性回归(Bp neural network for fault classification of wind turbines, can be used for two-class and multi-classification, has linear regression and nonlinear regression)
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卷积神经网络实现CWRU滚动轴承数据集故障分类(Fault classification of volume and network)
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