It is possible to detect visual surface defects with software in industrial tile production and increase productivity by automating the quality control process. In this process, low error rate cost are important indicators. order eliminate negativity effect of human factor, detection has been developed an artificial intelligence-based vision environment. Spots, scratches, cracks, pore defects, ...

the prediction of lithology is necessary in all areas of petroleum engineering. this means that todesign a project in any branch of petroleum engineering, the lithology must be well known. supportvector machines (svm’s) use an analytical approach to classification based on statistical learningtheory, the principles of structural risk minimization, and empirical risk minimization. in thisresearc...

Radial basis function network (RBF) kernels are widely used for support vector machines (SVMs). But for model selection of an SVM, we need to optimize the kernel parameter and the margin parameter by time-consuming cross validation. In this paper we propose determining parameters for RBF and Mahalanobis kernels by maximizing the class separability by the second-order optimization. For multi-cla...

The prediction of lithology is necessary in all areas of petroleum engineering. This means that to design a project in any branch of petroleum engineering, the lithology must be well known. Support vector machines (SVM’s) use an analytical approach to classification based on statistical learning theory, the principles of structural risk minimization, and empirical risk minimization. In this res...

In this communication, we analyze several regularized types of Radial Basis Function (RBF) Networks for crop classification using hyperspectral images. We compare the regularized RBF neural network with Support Vector Machines (SVM) using the RBF kernel, and AdaBoost Regularized (ABR) algorithm using RBF bases, in terms of accuracy and robustness. Several scenarios of increasing input space dim...

Conditional expectiles are becoming an increasingly important tool in finance as well as in other areas of applications. We analyse a support vector machine type approach for estimating conditional expectiles and establish learning rates that are minimax optimal modulo a logarithmic factor if Gaussian RBF kernels are used and the desired expectile is smooth in a Besov sense. As a special case, ...

Remote sensing data is a collection of images and interpretation of information about an object, area, or event without any physical contact with it. Aircraft and satellites are common remote sensing platforms for earth and its natural sources. Remote sensing’s ability to identify and monitor land surfaces and environmental conditions expanded over years with remote sensed data being essential ...

This paper presents a structure-adaptive hybrid RBF-BP (SAHRBF-BP) classifier with an optimized learning strategy. SAHRBF-BP is composed of a structure-adaptive RBF network and a BP network of cascade, where the number of RBF hidden nodes is adjusted adaptively according to the distribution of sample space, the adaptive RBF network is used for nonlinear kernel mapping and the BP network is used...

While kernel support vector machines are powerful classification algorithms, their computational overhead can be significant, especially for large and high-dimensional data sets. A recent biomedical dataset, for instance, could take as long as 3 weeks to compute its RBF kernel matrix on a modern, single-processor workstation. In this paper, we develop methods for high-performance parallel compu...

Circuit faults are caused by the change of device parameters in analog circuit. Aiming at problems that fault feature extraction is difficult and signal cannot be effectively classified, an enhanced Harris Hawks algorithm proposed to optimize parameter optimization process RBF neural network, so as realize identification diagnosis Based on wavelet packet analysis, output response circuit decomp...