基于相关向量机的网络通信负载状态识别模型

为了改善网络通信负载状态识别效果,提出一种基于相关向量机的网络通信负载状态识别模型.首先提取影响网络通信质量的参数,分析它们与负载状态间的联系;然后将无线传感器网络吞吐率作为负载状态识别的标准,采用相关向量机构建网络通信负载状态的分类器,实现网络通信负载状态的识别;最后采用具体数据对网络通信负载状态识别性能进行测试.测试结果表明,相关向量机可准确识别网络通信负载状态,且网络通信负载状态识别正确率高于其他模型.

Autonomous Kernel Based Models for Short-Term Load Forecasting

The application of support vector machines to forecasting problems is becoming popular, lately. Several comparisons between neural networks trained with error backpropagation and support vector machines have shown advantage for the latter in different domains of application. However, some difficulties still deteriorate the performance of the support vector machines. The main one is related to the setting of the hyperparameters involved in their training. Techniques based on meta-heuristics have been employed to determine appropriate values for those hyperparameters. However, because of the high noneonvexity of this estimation problem, which makes the search for a good solution very hard, an approach based on Bayesian inference, called relevance vector machine, has been proposed more recently. The present paper aims at investigating the suitability of this new approach to the short-term load forecasting problem.

基于最优特征加权的图像分类算法

通过研究图像分类过程中特征选择和分类器构建问题,提出了一种基于最优特征加权的图像分类算法.首先提取图像的颜色和纹理特征,接着采用混沌粒子群优化算法对特征的权值进行求解,确定每一个特征对图像分类的贡献,最后采用相关向量机对加权的图像样本集进行学习,构建分类器,从而实现图像的分类.实验结果表明,相对于当前较流行的图像分类模型,该模型不仅能够提高图像的分类准确率,而且能够大幅度减少了图像的分类时间,具有一定的实际应用价值.