This paper presents a PCA (Principal Component Analysis) data dimensionality reduction algorithm based on OPNs (Ordered Pair of Normalized Real Numbers), referred to as OPNs-PCA. This algorithm aims ...

Specialization: Machine Learning Instructor: Geena Kim, Assistant Teaching Professor Prior knowledge needed: Calculus, Linear algebra, Python Learning Outcomes Explain what unsupervised learning is, ...

Results show that the PCA-PSO-LSSVM fault diagnosis model has a maximum fault recognition efficiency that is 10.4% higher than the other three models, the test sample classification time is reduced by ...

BP neural network inherits many disadvantages such as slow convergence speed and easily converging to local minimum. The input data generally has a high-dimensional feature. To improve the performance ...