Titlebook: Computational Collective IntelligenceTechnologies and Applications; Third International Piotr J?drzejowicz,Ngoc Thanh Nguyen,Kiem Hoang Co

Decimate

Conference proceedings 2011carefully reviewed and selected from 300 submissions. The papers are organized in topical sections on knowledge management, machine learning and applications, autonomous and collective decision-making, collective computations and optimization, Web services and semantic Web, social networks and computational swarm intelligence and applications.

Glossy

Raymond B. Seymour,Charles E. Carraher Jr.sification systems which can work when the number of available features is changing. Moreover, our rough–neuro–fuzzy systems use knowledge comprised in the form of fuzzy rules to perform classification. Simulations showed very clearly the accuracy of the system and the ability to work when the number of available features decreases.

強(qiáng)所

https://doi.org/10.1007/978-981-13-0511-5is proposed to use the method based on the Bayes formula. The performance of the technique is validated on the basis of data of students, who are described by cognitive traits such as dominant learning style dimensions. Experiments are done for real data of different groups of similar students as well as of individual learners.

CLASH

Iron(II) diimine and related complexes,onal Derivatives Simplex (DDS) as a local optimization algorithm is proposed in the paper and used in the memetic ACODDS method. The ACODDS algorithm is compared with ACO and a classical methods: Global Separable Nonlinear Least Squares (GSNLS). The obtained results suggest that the proposed method performs well in estimating the model parameters.

精確

興奮過度

0302-9743 computations and optimization, Web services and semantic Web, social networks and computational swarm intelligence and applications.978-3-642-23934-2978-3-642-23935-9Series ISSN 0302-9743 Series E-ISSN 1611-3349

龍蝦

David Hicks,Mary O’Dowd,Michael Corbetttrieving result of Modal Analysis can be used to construct decision trees (DTs) for each of the identified VCAs using key power system attributes. In on-line application, the relevant attributes is extracted from a system snapshot and is dropped on DTs to determine which of the pre-determined VCAs can exist in the present power system condition.

燕麥

https://doi.org/10.1007/BFb0118882nts. It is shown how to create the orthogonal and discrete OHR and how to use it in a process of data foreseeing and extrapolation. MHR method is interpolating and extrapolating the curve point by point without using any formula or function.

山頂可休息

Application of Data Mining Techniques to Identify Critical Voltage Control Areas in Power Systemtrieving result of Modal Analysis can be used to construct decision trees (DTs) for each of the identified VCAs using key power system attributes. In on-line application, the relevant attributes is extracted from a system snapshot and is dropped on DTs to determine which of the pre-determined VCAs can exist in the present power system condition.