6results about How to "Increase the learning rate" patented technology

The application discloses a new energy station electric quantity prediction method and system based on a global-local double-layer prediction architecture, and aims at solving problems of insufficient monthly electric quantity prediction data of new energy stations, large medium and long term weather prediction errors, and low prediction precision of traditional experience methods, adopts a global-local double-layer model architecture, a XGBoost global prediction model is constructed by using all station data in the upper layer, common rules such as seasons, weather, installed capacity, shutdown plan and the like are mined, the global model output is taken as an enhanced feature, a XGBoost local model is trained in combination with single station data in the lower layer, and individual characteristics such as station equipment efficiency and operation and maintenance level are adapted, and a holiday adjustment factor is introduced to post-process and correct the prediction result. The application effectively solves the small sample modeling problem commonly existing in monthly electric quantity prediction, and significantly improves the monthly electric quantity prediction accuracy of wind power stations and photovoltaic stations.

The application provides a communication interference strategy generation method based on reinforcement learning, comprising the following steps: step one, constructing a system model of a communication party and a communication interference party; step two, based on the system model of the communication party and the communication interference party constructed in step one, learning an anti-interference scheme of the communication party by using a win-or-learn policy hill climbing algorithm, and designing a corresponding interference decision model; step three, using the interference decision model obtained in step two, learning an anti-interference strategy of a communication target and implementing interference according to a decision process of 'observation-adjustment-decision-action'. The application comprehensively considers interference basic principles and behavior changes of the communication target, combines a jam-to-noise ratio and anti-interference behaviors such as frequency change and transmission power increase of the communication target after being interfered, and uses the two as measurement indexes of interference effects, so that the purpose of real-time and rapid interference is achieved.

The invention relates to the technical field of civil engineering, and discloses a slope safety coefficient calculation method based on transfer learning and GRU, and the method comprises the following steps: S1, exploring a slope with a safety coefficient to be calculated, and obtaining the physical and mechanical parameters of the slope; s2, building and training a safety coefficient calculation model; s3, calculating a slope safety coefficient; and S4, carrying out transfer learning. According to the method, various physical and mechanical parameters of the side slope are used as input layers, and corresponding safety coefficients are output through two one-dimensional convolution layers, two GRU layers and two full-connection layers, so that the acquisition efficiency of the safety coefficients is improved, the parameters of each side slope and the safety coefficient of each side slope are used as one sample, and the sampling efficiency is improved. And training a slope safety coefficient calculation model through the sample, reducing the initial learning rate of the source domain model, slowing down the learning rate of the source domain model, increasing the learning rate of a full connection layer, performing multiple tests, and evaluating the accuracy of the model on target domain data.

This disclosure relates to batch normalization layers. The present invention provides methods, systems, and apparatus for processing input using a neural network system including a batch normalization layer, comprising a computer program encoded on a computer storage medium. One method includes: receiving a corresponding first layer output for each training example in the batch; calculating a plurality of normalization statistics for the batch based on the first layer outputs; normalizing each component of each first layer output using the normalization statistics to generate a corresponding normalized layer output for each training example in the batch; generating a corresponding batch normalized layer output for each training example from the normalized layer outputs; and providing the batch normalized layer outputs as input to a second neural network layer.

The present application relates to a kind of temperature and pressure integrated composite sensor fast self-adaptive decoupling method, belong to intelligent sensor field, solve the low speed slow problem of temperature pressure decoupling precision.Utilize acquisition model to obtain bridge voltage signal and corresponding temperature value, pressure value, respectively based on bridge voltage signal and corresponding temperature value, pressure value constructs first, second sample data set;Respectively establish temperature, pressure prediction BP neural network model;First, second sample data set is trained to temperature, pressure prediction BP neural network model;Loss function is calculated when each training iteration, and the learning rate of weight and bias is optimized using loss function, and the optimized learning rate is used in the process of reverse propagation to adjust weight bias;When the prediction accuracy meets the requirement training ends, respectively obtain trained temperature, pressure prediction BP neural network model as decoupling model;Real-time bridge voltage signal is input into decoupling model, and temperature pressure is decoupled in real time.Temperature pressure accurate real-time decoupling is realized.

The invention discloses a rule-based scene library setting automatic driving decision-making system and method and a vehicle, and belongs to the technical field of automatic driving, and the system comprises a sensor module which is used for collecting environment information around the vehicle; the data processing module is connected with the sensor module, and the data processing module is used for processing and analyzing the environment information collected by the sensor module so as to identify an obstacle and extract feature information of the obstacle; the decision module is used for making an avoidance decision according to the feature information extracted by the data processing module on the basis of a predetermined rule; and the execution module is used for controlling the vehicle to execute the avoidance decision generated by the decision module. According to the invention, various obstacles and moving states thereof can be accurately identified in a complex urban traffic scene, sudden occurrence of the obstacles can be rapidly judged and processed, and decision rules are continuously optimized through real-time learning and experience accumulation so as to adapt to different urban environments and obstacle arrangement.