基于支持向量机的高炉煤气利用率预测建模

煤气利用率是反映高炉能耗和平稳运行的重要指标。为了实现对高炉煤气利用率的准确预测,首先依据最大信息系数选择合适的输入参数,分别选取次于该状态参数时刻1 和2 h后的煤气利用率作为输出参数,并在建模之前对数据进行标准化处理。在此基础上建立基于支持向量回归(SVR)的高炉煤气利用率预测模型,并利用高炉的部分生产数据将该模型的预测结果与多层感知器(MLP)模型进行对比。最终预测结果表明,SVR模型在预测1和2 h后的煤气利用率时精确度更高,达到了更好的预测效果。     

New computational solution to quantify synthetic material porosity from optical microscopic images

This paper presents a new computational solution to quantify the porosity of synthetic materials from optical microscopic images. The solution is based on an artificial neuronal network of the multilayer perceptron type and a backpropagation algorithm is used for training. To evaluate this new solution, 40 sample images of a synthetic material were analysed and the quality of the results was confirmed by human visual analysis. In addition, these results were compared with ones obtained with a commonly used commercial system confirming their superior quality and the shorter time needed. The effect of images with noise was also studied and the new solution showed itself to be more reliable. The training phase of the new solution was analysed confirming that it can be performed in a very easy and straightforward manner. Thus, the new solution demonstrated that it is a valid and adequate option for researchers, engineers, specialists and other professionals to quantify the porosity of materials from microscopic images in an automatic, fast, efficient and reliable manner.     

多层感知器在变压器故障诊断中的应用

提出采用多层感知器模型应用于变压器故障诊断系统,将遗传算法全局搜索能力强的特点和梯度下降法局部搜索能力强的特点有效结合,增强了神经网络模型的识别效果。     

Classification error of multilayer perceptron neural networks

In subject classification, artificial neural networks (ANNS) are efficient and objective classification methods. Thus, they have been successfully applied to the numerous classification fields. Sometimes, however, classifications do not match the real world, and are subjected to errors. These problems are caused by the nature of ANNS. We discuss these on multilayer perceptron neural networks. By studying of these problems, it helps us to have a better understanding on its classification.     

Development and performance evaluation of neural network classifiers for Indian internet shoppers

The rapid growth of usage of internet has paved the way towards the use of online shopping. Consumers’ behavior is one of the significant aspects that is considered by the service providers for the improvement of various services. Consumers are generally satisfied if their needs are fulfilled. In this paper an in depth investigation is made on the behavior of Indian consumers towards online shopping. Factor analysis is carried out to extract significant factors that affect online shopping of Indian consumers and these consumers are clustered based on their behavior, towards online shopping using hierarchical clustering. Employing the results of clustering in training of multilayer perceptron (MLP), functional link artificial neural network (FLANN) and radial basis function (RBF) networks efficient classifier models are developed. The performance of these classifiers are evaluated and compared with those obtained by conventional statistical based discriminant analysis. The simulation study demonstrates that the RBF network provides best classification performance of internet shoppers compared to those given by the FLANN, MLP and discriminant analysis based methods. The simulation study on the impact of different combination of inputs demonstrates that demographic input has least effect on classification performance. On the other hand the combination of psychological and cultural inputs play the most significant role in classification followed by psychological and then cultural inputs alone.     

An Electronic Commerce Application of the Bayesian Framework for MLPs: The Effect of Marginalisation and ARD

Within the Bayesian approach to the training of multi-layer perceptrons for classification problems, the interpretation of the outputs as posterior probabilities of class-membership requires us to integrate out (marginalise) the network function over the distribution of network weights. MacKay [1] suggests an approximation of such an analytically intractable integral, in which the integration is over the network output preactivations. The network predictions can be over-optimistic if this process of marginalisation is ignored. This study attempts to assess the effect of marginalisation, with the approximation mentioned above, on two Bayesian neural network models: one with a single regularisation term; and another giving way to a process known as   Automatic Relevance Determination (ARD), with multiple regularisation terms. A real-world classification problem, concerning the discrimination of online purchasers and non-purchasers using Internet’s WWW users’ opinions, is the test-bed for this assessment.     

红外光谱法结合化学计量学对快递包装纸盒的检验研究

目的 研究一种红外光谱法与化学计量学相结合的方法，以对现场提取的快递包装纸盒样品进行快速检验分类。方法 利用红外光谱法对53个快递包装纸盒样品进行检验，依据其主要填料差异进行分类，并利用系统聚类进行分组。基于该分组，训练随机森林模型、多层感知器判别、Fisher判别3种预测模型，实现对新样品组别的分类预测。结果 53个快递包装纸盒样品被分为3类，而后进一步细分为9组，训练得到的3种判别模型中的Fisher判别预测准确率较高。结论 该检验方法快速、无损、准确，依据化学计量学实现对快递包装纸盒样品的快速检验，为公安机关检验此类物证提供依据。     

Electric power demand forecasting using interval time series: A comparison between VAR and iMLP

Electric power demand forecasts play an essential role in the electric industry, as they provide the basis for making decisions in power system planning and operation. A great variety of mathematical methods have been used for demand forecasting. The development and improvement of appropriate mathematical tools will lead to more accurate demand forecasting techniques.