Hypersphere support vector machines based on generalized multiplicative updates

This paper proposes a novel hypersphere support vector machines （HSVMs） based on generalized multiplicative updates. This algorithm can obtain the boundary of hypersphere containing one class of samples by the description of the training samples from one class and use this boundary to classify the test samples. The generalized multiplicative updates are applied to solving boundary optimization progranmning. Multiplicative updates available are suited for nonnegative quadratic convex programming. The generalized multiplicative updates are derived to box and sum constrained quadratic programming in this paper. They provide an extremely straightforward way to implement support vector machines （SVMs） where all variables are updated in parallel. The generalized multiplicative updates converge monotonically to the solution of the maximum margin hyperplane. The experiments show the superiority of our new algorithm.

A User-Transformer Relation Identification Method Based on QPSO and Kernel Fuzzy Clustering

User-transformer relations are significant to electric power marketing,power supply safety,and line loss calculations.To get accurate user-transformer relations,this paper proposes an identification method for user-transformer relations based on improved quantum particle swarm optimization(QPSO)and Fuzzy C-Means Clustering.The main idea is:as energymeters at different transformer areas exhibit different zero-crossing shift features,we classify the zero-crossing shift data from energy meters through Fuzzy C-Means Clustering and compare it with that at the transformer end to identify user-transformer relations.The proposed method contributes in three main ways.First,based on the fuzzy C-means clustering algorithm(FCM),the quantum particle swarm optimization(PSO)is introduced to optimize the FCM clustering center and kernel parameters.The optimized FCM algorithm can improve clustering accuracy and efficiency.Since easily falls into a local optimum,an improved PSO optimization algorithm(IQPSO)is proposed.Secondly,considering that traditional FCM cannot solve the linear inseparability problem,this article uses a FCM(KFCM)that introduces kernel functions.Combinedwith the IQPSOoptimization algorithm used in the previous step,the IQPSO-KFCM algorithm is proposed.Simulation experiments verify the superiority of the proposed method.Finally,the proposed method is applied to transformer detection.The proposed method determines the class members of transformers and meters in the actual transformer area,and obtains results consistent with actual user-transformer relations.This fully shows that the proposed method has practical application value.

Application of Improved Compact Particle Swarm Optimization to Large Ontology Alignment Task

Ontology occupies an important position in artificial intelligence,computer linguistics and knowledge management.However,when different ontologies are constructed to represent the same information in a domain,the so-called heterogeneity problem arises.In order to address this problem,a key task is to discover the semantic relationship of entities between given two ontologies,called ontology alignment.Recently,the meta-heuristic algorithms have already been regarded as an effective approach for solving ontology alignment problem.However,firstly,as the ontologies become increasingly large,meta-heuristic algorithms may be easier to find local optimal alignment in large search spaces.Secondly,many existing approaches exploit the population-based meta-heuristic algorithms so that the massive calculation is required.In this paper,an improved compact particle swarm algorithm by using a local search strategy is proposed,called LSCPSOA,to improve the performance of finding more correct correspondences.In LSCPSOA,two update strategies with local search capability are employed to avoid falling into a local optimal alignment.The proposed algorithm has been evaluated on several large ontology data sets and compared with existing ontology alignment methods.The experimental results show that the proposed algorithm can find more correct correspondences and improves the time performance compared with other meta-heuristic algorithms.