4 results about "Neural network regression" patented technology

The application provides a SPR connection quality tracing method and system based on pruning light weight, which comprises the following steps: training a multi-output neural network regression model by using standardized SPR curves and standardized process parameters; applying local disturbance to the standardized process parameters, inputting the disturbed samples into the trained multi-output neural network regression model to obtain predicted SPR curves; training a simplified linear model based on the relationship between the disturbed samples and the predicted SPR curves to obtain a regression coefficient matrix and a bias term; training an inverse regression model by using the standardized SPR curves and the process parameters, the regression coefficient matrix and the bias term; and inputting an abnormal SPR curve detected in real time into the inverse regression model to obtain process parameters of the abnormal SPR curve. The application improves the prediction accuracy and reasoning speed of the model, increases the interpretability of the model, and ensures the accuracy and stability of the SPR connection process.

This invention discloses an anti-skid navigation control method for agricultural machinery based on historical driver data, comprising: S1, collecting operational data of the driver operating the agricultural machinery on slippery road sections, using the Pearson correlation coefficient method to screen key dependent variables affecting steering control, and constructing training samples; S2, constructing an attention-BP-LSTM neural network regression prediction model for desired wheel angle and steering wheel torque; S3, model training; S4, evaluating and verifying the model's prediction results; S5, deploying the model on the terminal controller of the agricultural machinery to acquire real-time operational data of the agricultural machinery. When slippage is detected, the model outputs the desired wheel angle and steering wheel torque, and, combined with the safety protection mechanism of output limiting and gradual control, automatically adjusts the steering mechanism of the agricultural machinery to control the agricultural machinery to escape slippage. This invention generates precise and real-time control commands through deep learning of the driver's historical sideslip correction data, solving the problems of traditional methods relying on physical models and lacking experience utilization.

This invention relates to a method and system for evaluating the online reading quality of students. The method includes: Q1. During online reading, collecting data on the user's viewing distance, blink frequency, and heart rate at the same moment, and performing normalization processing to obtain normalized data on the user's viewing distance, blink frequency, and heart rate at the same moment. Then, using an ELMAN neural network regression prediction algorithm based on the sardine optimization algorithm, the method predicts the user's viewing distance, blink frequency, and heart rate at the next moment, obtaining data on the user's viewing distance, blink frequency, and heart rate at the next moment. This invention not only evaluates the user's reading quality from multiple aspects, ensuring the accuracy of the reading quality assessment, but also assesses and reminds users of their reading quality in advance, thereby improving the user's enthusiasm and stability in reading.

The application discloses a hierarchical multi-entity multi-modal fusion-based online review usefulness prediction method, comprising the following steps: obtaining original data to be predicted, pre-processing and feature extraction are performed on multi-modal information to obtain text features, image features and data features; the text features, the image features and the data features are spliced into a first input sequence, which is input into a first-level Transformer encoder to generate review entities, user entities and merchant entities; the review entities, the user entities and the merchant entities are stacked to construct a second input sequence, which is input into a second-level Transformer encoder to generate global context features; and the global context features are input into a deep neural network regression predictor to output review usefulness prediction values. The two-stage Transformer design effectively improves the accuracy and robustness of review usefulness prediction.