37 results about "Relief algorithm" patented technology

The invention discloses a feature selection method facing to SNP (Single Nucleotide Polymorphism) data, which specifically comprises the following steps: firstly, carrying out data pre-processing; secondly, removing unrelated SNP features by using a newly-designed Relief algorithm; thirdly, carrying out critical degree sorting on the SNP features by using an improved SVM-RFE algorithm; and finally, screening the critical SNP sorting by using cross validation. The feature selection method has the beneficial effects that the advantages of Filter feature selection and Wrapper feature selection are combined, and a secondary division method is used in the machine learning process, so that the problems of a high-dimensional small sample in the SNP feature selection and a SNP pathogenic combination mode are solved, and the analysis efficiency and the accuracy are improved.

The invention provides a driving fatigue detection method based on voice personality characteristics and models. The method comprises the following steps of: firstly, extracting linear characteristics and non-linear characteristics of driver voice samples; secondly, utilizing a speaker identification algorithm based on VQ to judge the identity of a driver; then, according to the individual fatigue characteristic differences of the driver, utilizing a Relief algorithm to screen out voice characteristics fully reflecting fatigue information thereof, and constructing fatigue personality characteristic vectors; and finally, adopting an SVM classification algorithm to establish a self-adaptive fatigue detection model of the driver himself, and carrying out sample training and driving fatigue detection on the model. According to the invention, the voice linear characteristics and non-linear characteristics are combined in a complementation manner, and the voice personality characteristics fully reflecting fatigue information of the driver are screened out from the individual differences of the driver for driving fatigue detection, the influences of the individual voice differences of the driver on fatigue detection are effectively reduced, and the detection accuracy is improved.

The invention discloses a star map deblurring method based on an improved RL deconvolution algorithm, comprising: (1) setting basic simulation parameters in a star map simulator, using the basic simulation parameters to simulate a clear star map and corresponding fuzzy stars; (2) calculate the point spread function of each fuzzy star map according to the inertial sensor element data; (3) use the RL deconvolution algorithm to complete the defuzzification of the star map, and record the optimal number of iterations and iteration steps of the deblurring effect (4) The point spread function, number of iterations and iteration step of each blurred image are used as input for offline training of the neural network prediction model. After the training is completed, the prediction model is obtained, and the output is the number of iterations and the iteration step. The invention overcomes the disadvantage that the traditional RL deconvolution defuzzification algorithm needs to manually test the number of iterations and iteration steps to complete the defuzzification of the star map, improves the real-time performance of the algorithm and significantly improves the effect of the star map defuzzification.

The invention relates to a feature selection-based arrhythmia classification method. The method comprises the following steps: preprocessing an ECG (electrocardiograph) signal; extracting a morphological feature and a time-frequency feature according to a detected position R, and constructing an original feature vector; calculating feature weights, namely calculating the weight of each feature in the original feature vector by using a Relief algorithm; instructing a population to be initialized according to the feature weights, respectively performing selection, crossover and mutation operation according to a selection probability, a crossover probability and a mutation probability according to the adaptation degree of an individual to obtain a next generation, repeating the operation until the maximum number of iteration times termination condition is met, and then outputting the individual with the highest adaptation degree as an optimal feature; and enabling a plurality of di-classifiers to form an identification classifier through a multi-classification strategy to realize identification of various arrhythmias. According to the feature selection-based arrhythmia classification method, the dimensions of the features can be reduced and the accuracy of identification of the various arrhythmias can be improved.

The invention discloses a power system transient stability prediction method based on disturbance voltage trajectory cluster features. According to the method, historical information collected by a WAMS in a power system is primarily screened, time domain trajectory clusters of all key power generator node voltage amplitudes after the failure are obtained to form an original data set; 27 geometrical characteristics of the trajectory clusters are calculated according to original data; a Relief algorithm is utilized for carrying weight calculation on all characteristic quantities, a plurality of wide-range fault features strongly correlated to the system transient stability are selected and used as an input data set of a transient stability prediction algorithm; on the basis of the input data set, an SVM prediction model is built. An analysis model of the power system is not built, and according to response information, obtained through the WAMS, of the power system, the transient stability of the power system is directly predicated. The system stability can be rapidly predicted, and high adaptability and robustness are achieved.