2869 results about "Hidden layer" patented technology

The invention belongs to the field of network resource management, relates to the cooperative filtration technique of network resources and discloses a network resource personalized recommended method based on an ultrafast neural network. The network resource personalized recommended method based on ultrafast neural network comprises the following steps: firstly, data preprocessing, reading information from the journal files of a system user and generating a global user interested matrix, exchanging the global user interested matrix into a single user interested matrix of a current user, and then transforming and reducing dimensionality to mark out a training set A1 and a predicting set A2, secondly, model training, building an interest predicting model with single hidden layer neural network SLFN s structure for a target user, adopting ultrafast learning machine technique to carry out training on the training set A1 and getting various connection power values and hidden layer threshold values of the neural network model with single hidden layer, thirdly, prediction recommending, utilizing the obtained predicting model to calculate the scoring values of every resource in the predicting set A2 given by the target user and then recommending the first several resources with highest predicting scores to the target user.

The invention discloses an automatic classification method for electrocardiogram signals. The method is achieved according to the following steps of firstly, obtaining electrocardiogram signals of a human body, conducting filtering on the electrocardiogram signals, and detecting R waves of the electrocardiogram signals where filtering is conducted; secondly, establishing a data set after the R waves are detected, wherein the data set is composed of multiple sets of cardiac beat data, and each set of cardiac beat data has a label; thirdly, establishing a sparse automatic coding deep learning network; fourthly, training the sparse automatic coding deep learning network step by step; fifthly, inputting the to-be-measured cardiac beat data into the sparse automatic coding deep learning network according to the network weight, obtained in the fourth step, of the first hidden layer, the network weight, obtained in the fourth step, of the second hidden layer and the network weight, obtained in the fourth step, of the softmax classifier so as to obtain cardiac data which are output in a classified mode. The sparse automatic coding deep learning network is applied to the classification of the cardiac beat data, and by means of the autonomous leaning capacity and the deep characteristic excavation characteristic of the sparse automatic coding deep learning network, deeper characteristics of signals are extracted, and the cardiac beat data are classified.