49results about How to "Reduce the amount of training" patented technology

The invention provides a method for training a wake-up model and a device thereof, and the method comprises the steps: obtaining a first training set and a second training set when the model trainingis triggered; respectively inputting the first training set into an initial acoustic model and a current acoustic model, and determining a first difference parameter by comparing output results of theinitial acoustic model and the current acoustic model; inputting the second training set into a current acoustic model, and determining a second difference parameter by comparing an output result ofthe current acoustic model with a one-hot code corresponding to wake-up voice which can be recognized by the current acoustic model; and adjusting model parameters of the current acoustic model according to the first difference parameter and the second difference parameter. By utilizing the method provided by the invention, the current acoustic model can be compatible with the initial voice underthe condition of ensuring that the current acoustic model adapts to the current scene, the risk of unstable performance of the wake-up model caused by updating is reduced, and the trained acoustic model is well compatible with the previous initial wake-up scene on the premise of adapting to a more complex scene.

The invention relates to a die profile local springback compensation method based on elliptical surface mapping drive. The die profile local springback compensation method based on the elliptical surface mapping drive comprises the following steps that firstly, a local springback distribution cloud map of a part is obtained; secondly, a drive coordinate system is established according to the cloud map, an elliptical sketch is drawn, an interior area of the elliptical sketch is defined as a springback compensation area, and a reference curved surface and a target curved surface are established according to the springback compensation area and the springback compensation amount; and finally, the die profile is driven to deform in a mapping manner with the reference curved surface and the target curved surface as the deformation criterion and with the Z-axis direction of the drive coordinate system as a deformation direction, and the local springback compensation of the product is completed. Compared with the prior art, the die profile local springback compensation method based on the elliptical surface mapping drive has the beneficial effects that the efficiency of local springback compensation of the profile of a stamping die is improved, the local springback compensation precision of the profile of the die and the curved surface quality of the compensated profile of the die are effectively guaranteed, the die research and study amount is reduced, and the manufacturing cycle of the die is shortened.

The embodiment of the invention provides a driving event recognition method and device, a training method and device, equipment and a storage medium. The method comprises the following steps: collecting data representing a driving event as a driving parameter, and dividing the driving parameters in the first time period into a first target parameter and a second target parameter; in an event recognition model matched with the type of a vehicle, searching an event recognition model suitable for processing the second target parameter as an original event recognition model, training an original event recognition model by taking the first target parameter as a sample for identifying urgency and the second target parameter as a sample for identifying non-urgency; obtaining a target event recognition model, wherein the target event recognition model is called to recognize the emergency driving event from the driving parameters in the second time period, training is continued on the basis ofthe previous event recognition model, the training amount is small, the real-time requirement is met, the event recognition model conforming to the driving style of the user is learned, and the personalized driving event is recognized.

The invention discloses a method for effectively acquiring event-related magnetic field information in magnetoencephalogram signals. The method includes the steps of firstly, collecting and preprocessing magnetoencephalogram data; secondly, building a time-frequency atom database; thirdly, using a single-channel matching pursuit algorithm to build a linear combination; fourthly, forming a multi-channel matching pursuit algorithm; fifthly, determining iteration termination by the total residual energy of all channels so as to obtain atoms after signal decomposition; sixthly, removing the atoms representing artifact noise, and rebuilding the signals. The method has the advantages that the magnetoencephalogram signals are post-processed by the method, stimulation times can be reduced greatly, and test results are prevented from being affected by the fatigue, which is caused by long-time and repeated stimulation, of a scanned person; the training amount of a to-be-tested person is reduced, the requirements of the to-be-tested person are lowered, and the selection range of to-be-tested persons of clinical researches is expanded; data collecting time is reduced, research cost is lowered, and the clinical actual researches and popularization and application of event-related magnetic fields are benefited.

Theembodiment of that present application provide a neural network system, Among them, the neural network system, includes a clustering module for clustering a plurality of simulation results to determine a plurality of simulation clustering results, and determining a corresponding plurality of action sets according to the plurality of simulation clustering results, wherein each action set includes at least one action sample, and the simulation result is a simulation result of a target entity after the action sample is executed; The reinforcement learning module is configured to select an action set to be executed by the target entity from the plurality of action sets according to a state set of the target entity and output the selected action set to be executed by the target entity. The scheme provided by the embodiment adopts the reinforcement learning module of the action set training neural network system, and the order of magnitude of the action set is greatly reduced compared with the action sample, so that the training time of the neural network system can be shortened, the speed of finding the optimal solution in the training process can be increased, and the accuracy of the training result can be improved.

The invention discloses a platform patent value evaluation method based on an information ecological theory and an RF-GA-BP neural network. The platform patent value evaluation method comprises the following steps: analyzing network platform patent value influence factors based on the information ecological theory; obtaining a training sample and a test sample of the GA-BP neural network; performing dimension reduction processing on the platform patent value influence factors by adopting an RF-based feature selection method to obtain an optimal feature subset; designing a BP neural network topological structure, and setting related parameters; optimizing the weight and threshold of the BP neural network by adopting a GA algorithm; and taking the optimal feature subset selected by the RF asan input signal of a GA-BP neural network model, training and checking the GA-BP neural network, and finally constructing a platform patent value evaluation model based on an RF-GA-BP neural networkalgorithm.

The invention discloses a classification model training method, which is applied to the technical field of artificial intelligence and is used for solving the technical problems of high model training difficulty and inaccurate prediction result when an existing prediction model is used for predicting a low-risk probability event. The method provided by the invention comprises the following steps: acquiring risk training samples and non-risk samples; determining the number of base classifiers; determining a first training sample for training a first base classifier; binning the non-risk samples relative to the current base classifier according to the loss of each non-risk sample, and calculating the weight of each box under the current base classifier; according to the weight of each box and the number of the risk training samples; carrying out sampling from the corresponding boxes according to the determined number, and obtaining training samples of the current base classification; and training the first base classifier through the first training sample, and training the corresponding base classifiers through the training sample of the current base classification to obtain a trained classification model.

The invention discloses a swimming athlete training load prediction method based on PCA-PNN. The method comprises the steps: step 1, carrying out feature index selection and database construction; step 2, carrying out principal component analysis to construct a PNN network input quantity; and step 3, constructing a PNN swimming athlete training load prediction model. The invention provides the swimming athlete training load prediction method based on PCA-PNN; the method constructs the swimming athlete training load prediction model through the research of the training load impact fusion features of an athlete, so as to evaluate the training arrangement of one swimming athlete.

The invention discloses a neural network generation method and device and a computer readable storage medium, and the neural network generation method comprises the steps: obtaining an optimal micro-unit, constructing a first network through the optimal micro-unit, enabling the first network to have enough powerful performance, and meeting the actual application demands; and training the first network by using a preset training data set to obtain a second network, establishing third networks, respectively training each micro-unit of all the third networks by using the second network to obtain a first training data set, training each micro-unit of all the third networks by using the second training data set, and constructing the neural network model according to the micro-units of the trained third network, so that the functions of all the micro-units of the third network correspond to the functions of the second network, and compared with a traditional method of training the micro-units one by one, the training number can be reduced, the computing power demand can be effectively reduced, and then the cost of generating the neural network model is reduced.

The disclosure relates to a method and device for synthesizing singing audio frequencies, and belongs to the field of audio processing. The method comprises the following steps: acquiring a pre-storedaverage singing model; obtaining a target feature value text and a target acoustic parameter of target singing data; according to the target feature value text and the target acoustic parameter, determining a feature correspondence table of the feature value text of the target speaker and the acoustic parameters; on the basis of the target feature value text and the target acoustic parameters, training the average singing sound model to obtain a target singing sound model; and when a to-be-synthesized music score is received, extracting a feature value text of the to-be-synthesized music score, and on the basis of the feature value text of the to-be-synthesized music score, the target song model and the feature corresponding table, obtaining a target song audio frequency corresponding tothe to-be-synthesized music score. By adopting the method and the device, the singing synthesis efficiency can be improved.

The invention provides a static gait debugging method and system for a robot, an electronic device and a storage medium, and the method comprises the steps: a non-contact force simulation step: carrying out the non-contact force simulation debugging of a robot model through employing a pre-constructed initial motion node instruction of a static gait, and obtaining a first debugging instruction; a contact force simulation step: performing contact force simulation debugging on the robot model by using the first debugging instruction to obtain a second debugging instruction; and a real debugging step: carrying out real debugging on a real robot by using the second debugging instruction to obtain a target action node instruction of the static gait. According to the technical scheme, the technical problems that in the prior art, when gait debugging is conducted on the robot, the workload is large, the gait planning accuracy is low, and the debugging cost is high are solved.

The invention relates to the technical field of intelligent traffic, in particular to a traffic flow prediction method based on similar time sequence comparison, which comprises the following steps of: 1, acquiring traffic flow data of a certain region according to a public data website, and processing the traffic flow data; 2, mining a regional flow period; 3, pre-training an encoder; coding is carried out by using a deep ResNet network from a spatial angle, flow graph features are captured from the spatial angle, and due to the fact that regional flow distribution of the same city function is similar, features of similar regions are drawn close to each other by using a multi-instance contrast learning method, so that the features are far away from features of dissimilar regions; 4, the pre-trained encoder is put into the flow prediction model for fine adjustment; and 5, the model is stored. Compared with the conventional traffic flow prediction, the method has the characteristics of less parameter quantity and training cost, obvious modeling effect, good prediction result and the like.

The invention relates to a video classification method and device, equipment, a medium and a product, and relates to the technical field of computers, and the method comprises the steps: generating a first-class classification result of a first-class video based on a classification network, and generating a second-class classification result of a second-class video based on the classification network; wherein the first type of video belongs to a first video set, the second type of video belongs to a second video set, and the video number of the first video set is greater than that of the second video set; determining a first loss function between the first-class video tag and the first-class classification result, and determining a second loss function between the second-class video tag and the second-class classification result; and training a classification network based on the first loss function and the second loss function, and classifying the target second-class videos to be classified based on the trained classification network. In this way, the classification precision of the classification network for the second-class videos with the small sample size can be improved.

The invention discloses an edge detection filter optimization method based on deep learning, and the method comprises the steps: collecting a plurality of to-be-detected object images, and recording the to-be-detected object images as a training image set; selecting to-be-detected features in each image in a framing manner and labeling the to-be-detected features to obtain labeled images; taking the first training image as an input image; carrying out the convolution on the input image, calculating the gradient of each pixel point and inputting the gradient into a Sigmid function for activation processing, and acquiring an output result graph; recording the output result image as a new input image, and repeating; obtaining a normalized result graph by utilizing a softmax function, and calculating loss matrixes MLoss and LOSS values of the normalized result graph and the annotated image; performing back propagation by using the loss matrix MLoss to obtain corrected edge detection filters of each layer; taking the next training image as an input image, and continuing repeating by using the corrected edge detection filters until the LOSS value converges. The method is better in edge detection stability, high in robustness and small in calculated amount.