Classification of water quality based on intelligent total margin adaptive fuzzy support vector machine
基于智能全间隔自适应模糊支持向量机的水质分类

In this study, Total-margin Adaptive Fuzzy Support Vector Machine (TAFSVM) of good quality was proposed. In addition, Real-valued Genetic Algorithm (RGA) optimized its parameters. Subsequently, the model of Real Genetic Algorithms Total Margin Adaptive Fuzzy Support Vector Machine (RGATAFSVM) was used to classify four kinds of data sets of water quality. The experimental results show that the proposed model can achieve higher classification accuracy and stability than standard support vector machine, BP neural networks and single factor assessment. Consequently, the RGATAFSVM model provides a promising alternative for classification in water quality.