A projection pursuit cluster model is used to resolve the problem of Karstic water pollution evaluation. First, the projection pursuit cluster model is stated. Then, a genetic algorithm is applied to optimise the projection direction of the projection pursuit cluster model, and the steps of the genetic algorithm are introduced in detail. Finally, a case study of Karstic water pollution evaluation is presented. The projection pursuit cluster model is a powerful tool in analysing complex multifactor problems, and it is also a new method for Karstic water pollution evaluation.
This paper presents an approach based on Projection Pursuit and fuzzy rule extraction combining new hybrid method of classification system. This method is the first to use projection pursuit technology to deal with training set of sample dimensionality reduction and in accordance with the sample classification. According to the results of the classification and the best value projection, using trapezoid distribution method extraction fuzzy rules, producing three types of fuzzy membership function. Finally, in accordance with Fuzzy nearness value to determined the sample under the credit level. The results of experiment showed this method of classification accuracy than the traditional credit evaluation techniques have markedly improved, the reliability is better, has a good application value.
We prove that groups acting geometrically on delta-quasiconvex spaces contain no essential Baumslag-Solitar quotients as subgroups. This implies that they are translation discrete, meaning that the translation numbers of their nontorsion elements are bounded away from zero.