Improving underwater signal detection capability by using support vector machine 利用支持向量机提高水声信号的检测能力
Traffic sign regions are detected and extracted from real world scenes on the basis of their color and shape features using non-linear classification capability of support vector machine, then re-cognized by fuzzy immune networks which has diversity and well tolerating noise capability. 该方法根据交通标志的颜色和形状,利用支持向量机的非线性分类能力将其图像区域从实景图像中检测和提取出来,然后利用具有多样性、较强容噪能力的模糊免疫网络来识别。
Fuzzy hypersphere support vector machine ( FHS-SVM) has stronger generalization capability than hyperplane support vector machine in the one-class classification problem, being successful in radar target detection. 模糊超球面支持向量机(FHS-SVM)在处理一类分类问题时比超平面支持向量机泛化能力更强,特别是在雷达目标检测中得到了成功应用。
The structure of the model is established after training and learning. And the control capability of model parameter and kernel is also researched thoroughly. The veracity and validity of the infrared methane sensor mathematical model based on support vector machine are confirmed by the experimentation. 通过对模型进行训练和测试确定了模型结构,同时研究了模型参数的控制作用,实验结果验证了该模型的有效性和准确性。
At the same time, the network model that used to implement the states monitoring of tool wear was established by using the self-learning, adaptive, fault tolerance and non-linear mapping capability of support vector machine. 同时,利用支持向量机的自学习、自适应、容错性以及非线性映射能力,建立分类模型用于实现刀具磨损状态的识别。
It show that the prediction capability of support vector machine model is better than weights-of-evidence and the logistic regression model. 可以看出,支持向量机模型的预测能力优于证据权法和Logistic回归模型。