Support Vector Machine Ensemble Based on Genetic Algorithm

Support vector machines （SVMs） have been introduced as effective methods for solving classification problems. However, due to some limitations in practical applications, their generalization performance is sometimes far from the expected level. Therefore, it is meaningful to study SVM ensemble learning. In this paper, a novel genetic algorithm based ensemble learning method, namely Direct Genetic Ensemble （DGE）, is proposed. DGE adopts the predictive accuracy of ensemble as the fitness function and searches a good ensemble from the ensemble space. In essence, DGE is also a selective ensemble learning method because the base classifiers of the ensemble are selected according to the solution of genetic algorithm. In comparison with other ensemble learning methods, DGE works on a higher level and is more direct. Different strategies of constructing diverse base classifiers can be utilized in DGE. Experimental results show that SVM ensembles constructed by DGE can achieve better performance than single SVMs, hagged and boosted SVM ensembles. In addition, some valuable conclusions are obtained.     

Combination of Multi-class Probability Support Vector Machines for Fault Diagnosis

To deal with multi-source multi-class classification problems, the method of combining multiple multi-class probability support vector machines （MPSVMs） using Bayesian theory is proposed in this paper. The MPSVMs are designed by mapping the output of standard support vector machines into a calibrated posterior probability by using a learned sigmoid function and then combining these learned binary-class probability SVMs. Two Bayes based methods for combining multiple MPSVMs are applied to improve the performance of classification. Our proposed methods are applied to fault diagnosis of a diesel engine. The experimental results show that the new methods can improve the accuracy and robustness of fault diagnosis.     

An IEEE 802.3 Compatible Real Time Medium Access Control with Length-based Priority

IntroductionEthernet has become the most successful LANtechnology today .It is even regarded as the core to conceiveunified data communication systemin the future . However ,due to the non-deterministic essence of CSMA/CD,Ethernetis unsuitable for applications with stringent ti me constrains ,as inindustrial floors , where violation of such requirementswill result in great degradation of system performance oreven make whole systemparalyzed .Determinismproblemhas been studiedinthelast decad…     

实时无线局域网介质访问控制协议的仿真

OPNET是一用来模拟通信系统和预测网络性能的强大仿真工具。传统无线局域网由于时延不确定性问题在工业控制领域不能得到直接应用。考虑了工业应用场合的特点，介绍了一种无线局域网实时介质访问控制协议。给出了节点的仿真构造、模块内部的状态转移图，实时和非实时节点的时延曲线。仿真结果表明该介质访问控制协议能满足实时节点的通信要求，同时非实时节点通过冲突避免减少了冲突增大了网络的吞吐量。     

Modeling and simulation for a new virtual-clock-based collision resolution algorithm

Virtual time Ethemet is a multiple access protocol proposed to provide FCFS transmission service over the predominant Ethemet bus. It incorporates a novel message-rescheduling algorithm based on the virtual clock mechanism. By manipulating virtual clocks back up over a common virtual time axis and performing timely collision resolution, the algorithm guarantees the system＇s queuing strictness. The protocol is particularly modeled as a finite state machine and implemented using OPNET tools. Simulation studies prove its correctness and effectiveness.