Fatigue driving judgment method based on unsupervised extreme learning machine multi-clustering algorithm

• Main Authors: SI YIHAO, LI SHIWU, GUO MENGZHU, SUN WENCAI
• Format: Patent
• Language: Chinese; English
• Published: 04.02.2020
• Subjects: CALCULATING; COMPUTING; COUNTING; HANDLING RECORD CARRIERS; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS

The invention discloses a fatigue driving judgment method based on an unsupervised extreme learning machine multi-clustering algorithm, belongs to the technical field of driving safety, and determinesan optimal classification cluster number and a probability density distribution function under each class through a Gaussian mixture model and a Bayesian information criterion, and determines an optimal identification model in a fatigue identification data set. Through a feature extraction non-iterative algorithm of an unsupervised extreme learning machine, a minimum value converged to the wholeenvironment is obtained, and an output matrix is obtained; the advantages of four clustering algorithms under unsupervised extreme learning machine feature extraction under different feature divisionlearning are fully utilized through a PCA algorithm; and component score coefficient matrix calculation is performed on the fatigue identification point identification accuracy matrix, and a normalized score coefficient is conv