一类不确定支持向量机问题的鲁棒可行性半径精确公式

文章旨在研究一类不确定支持向量机问题的鲁棒可行性.首先借助鲁棒优化方法,引入该不确定支持向量机问题的鲁棒对等问题.随后给出鲁棒对等问题的重构优化问题.最后借助该重构问题和上图集,得到该不确定支持向量机问题的鲁棒可行性半径的精确计算公式.

TWIN SUPPORT TENSOR MACHINES FOR MCS DETECTION

Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier.In this paper, we generalize the vector-based learning algorithm TWin Support Vector Machine(TWSVM) to the tensor-based method TWin Support Tensor Machines(TWSTM), which accepts general tensors as input.To examine the effectiveness of TWSTM, we implement the TWSTM method for Microcalcification Clusters(MCs) detection.In the tensor subspace domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and TWSTM is used as a classifier to make decision for the presence of MCs or not.A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm.By comparison with TWSVM, the tensor version reduces the overfitting problem.

Data-driven nonlinear control of a solid oxide fuel cell system

Solid oxide fuel cells (SOFCs) are considered to be one of the most important clean,distributed resources. However,SOFCs present a challenging control problem owing to their slow dynamics,nonlinearity and tight operating constraints. A novel data-driven nonlinear control strategy was proposed to solve the SOFC control problem by combining a virtual reference feedback tuning (VRFT) method and support vector machine. In order to fulfill the requirement for fuel utilization and control constraints,a dynamic constraints unit and an anti-windup scheme were adopted. In addition,a feedforward loop was designed to deal with the current disturbance. Detailed simulations demonstrate that the fast response of fuel flow for the current demand disturbance and zero steady error of the output voltage are both achieved. Meanwhile,fuel utilization is kept almost within the safe region.

Fault diagnosis using a probability least squares support vector classification machine

Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines is addressed by a proposed Probability Least Squares Support Vector Classification Machine (PLSSVCM). Samples that cannot be definitely determined as belonging to one class will be assigned to a class by the PLSSVCM based on a probability value. This gives the classification results both a qualitative explanation and a quantitative evaluation. Simulation results of a fault diagnosis show that the correct rate of the PLSSVCM is 100%. Even though samples are noisy, the PLSSVCM still can effectively realize multi-class fault diagnosis of a roller bearing. The generalization property of the PLSSVCM is better than that of a neural network and a LSSVCM.

Atmospheric Environmental Quality Prediction Based on Support Vector Machine

The economic development bring serious environmental problems to China, the quality of atmospheric environment has an important bearing on the forecast. Be aimed at the complexity and non-linear of the quality of atmospheric environment, chaos theory has been put forward which takes full advantage of using date information. Based on the reconstruction of time-series phase space of the quality of atmospheric environment with the use of least squares support vector machine theory, the atmospheric environment prediction model was built up, and 25 years of historical data viewed as the raw data of the quality of the environment in Benxi City, giving a practical example, it shows that the results forecasted by the least squares support vector machine and the actual results match the better the forecast accuracy also meet the engineering application.

NEW ROBUST UNSUPERVISED SUPPORT VECTOR MACHINES

This paper proposes robust version to unsupervised classification algorithm based on modified robust version of primal problem of standard SVMs, which directly relaxes it with label variables to a semi-definite programming. Numerical results confirm the robustness of the proposed method.