3results about How to "Increase the learning rate" patented technology

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.

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.