模式分类是许多工程领域如自控监测、图像识别、故障诊断、物料配制、医疗诊断等领域广泛应用的一种关键技术。
Pattern classification is a kind of technology used in a lot of project fields including automatic control monitor, image recognition, troubled diagnose, supplies compound, medical diagnosis, etc.
在此基础上，研究了用球结构支持向量机作分类器，对滚动轴承内圈故障的劣化程度进行识别的理论和方法。
Basing on these, study theory and method of using Sphere-structured Support Vector Machines to recognize the roll bearing inside track's fault deterioration extent.
该文介绍了BP网络的学习过程以及从模式识别角度应用BP神经网络作为分类器进行机械故障诊断。
The paper introduces the studying process of the BP network and USES the BP network for the mechanical failure diagnoses as assorted organ in the mode identification.
该模型是按照故障征兆属性归类，通过分类识别缩小故障搜寻范围以利于故障的模糊诊断，然后再进行因子综合判断，对振动故障诊断的方法进行了研究。
The model categorizes the vibration fault according to its attributes, Narrows the range for searching fault by classification, then determines the fault by comprehensive multi-factor judgement.
结果表明，以CP神经网络构筑的故障模式识别器有很强的非线性映射能力，可对机械设备故障模式进行正确分类。
The result indicates that based on CP neural network, the fault pattern recognition system has strong nonlinear mapping ability, therefore it can be used to correctly classify the mechanical faults.
试验结果表明，PCA对正常和故障样本有较大的区分度，SVDD分类器能很好的分辨出轴承正常和故障状态，并且对未知故障有良好的识别能力。
The test result shows that after the extraction of PCA, the SVDD classifier distinguished the normal and fault condition finely, and it also has good recognized ability to unknown fault samples.
该方法利用模式识别中的近邻准则，使用元胞蚂蚁算法实现故障的分类，达到故障诊断的目的。
The method realizes classification of fault by near-neighborhood criteria of pattern recognition and cellular ant algorithm.
现已在图像处理、人工智能、计算机识别、模式识别与分类、故障检测等方面得到了广泛应用。
Edge detection has been extensively applied in a lot of fields, such as image processing, artificial intelligence, computer recognition, pattern recognition and classification, fault detection etc.
提出了一种基于核的多类别模式识别算法(简称核子空间法，KSPM)，依据此算法建立了多故障分类器。
A novel multi-class classifier with kernels, namely kernel Subspace Methods (KSPM), was presented, and a multi-fault classifier based on the algorithm was constructed.
研究结果表明训练成功的BP网络可作为智能分类器对斜轴泵的常见故障进行识别和诊断。
According to the fault features extracted from the pump vibration via wavelet transform, the working conditions and the faults of the pump can be classified and identified by the BP neural networks.
该方法将振动信号小波包分解后的频带能量作为特征向量，输入到由多个支持向量机构成的多故障分类器中进行故障识别和分类。
According to the method, the energy of different frequency bands after wavelet packet decomposition constitutes the input vectors of support vector machine as feature vectors.
充分利用了神经网络极强的模式分类能力，用神经网分类器对故障信号进行多参量识别。
Because neural networks have a very strong ability in pattern recognition, an NN classifier is used in the multi-parameter recognition of fault signals.
将优化得到的复合特片作为故障识别的分类器，并根据不同的检测环境，选取不同的终端符集，从而降低检测环境对诊断结果的影响。
Then the optimal compound feature is searched by GP in the statistics and operators library. The compound feature is taken as classifier of fault diagnosis.
将优化得到的复合特片作为故障识别的分类器，并根据不同的检测环境，选取不同的终端符集，从而降低检测环境对诊断结果的影响。
Then the optimal compound feature is searched by GP in the statistics and operators library. The compound feature is taken as classifier of fault diagnosis.
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