Product Service System Configuration Based on a PCA-QPSO-SVM Model

To achieve sustainable development and improve market competitiveness, many manufacturers are transforming from traditional product manufacturing to service manufacturing. In this trend, the system (PSS) has become mainstream of supply satisfy customers with individualized products combinations. The diversified customer requirements can be realized by PSS configuration based on modular design. deemed as a multi-classification problem. Customer input, specific is output. This paper proposes an improved support vector machine (SVM) model optimized principal component analysis (PCA) quantum particle swarm optimization (QPSO) algorithm, which defined PCA-QPSO-SVM model. used solve PCA method reduce dimension requirements, QPSO optimize internal parameters SVM prediction accuracy classifier. case study, dataset for central air conditioning construct test model, optimal predicted well requirements.