33results about How to "Fast learning convergence" patented technology

The invention discloses a reinforcement learning controller of a bearingless permanent-magnet synchronous motor and a construction method of the reinforcement learning controller. Input ends of a suspension winding current control module and a torque winding current control module are connected with an actor-critic module, the actor-critic module comprises an actor neural network, a critic neural network, a reinforcement signal module and an instantaneous difference module, output ends of the critic neural network and the reinforcement signal module are connected with an input end of the instantaneous difference module, an output end of the instantaneous difference module is connected with input ends of the actor neural network and the critic neural network, a displacement error and a rotational speed error are common inputs of the actor neural network, the reinforcement signal module and the critic neural network, outputs of the critic neural network are a suspension winding given current and a torque winding given current under a d-q coordinate system, rotational speed and displacement feedback information is evaluated by an actor, the actor is guided to control the suspension winding current and the torque winding current, and stable tracking control of a bearingless permanent-magnet synchronous motor system is achieved.

The invention discloses a method for measuring coal humidity at a coal humidity control outlet, which includes data collection, data screening, data processing and modeling, model checking, and humidity detection. The invention discloses a method for measuring the coal humidity at the outlet of coal humidity adjustment. By constructing the model of the temperature value of the steam outlet of the dryer and the humidity value of the outlet coal, the humidity value of the outlet coal can be obtained in real time by directly measuring the temperature value of the steam outlet of the dryer. It is impossible to achieve accurate online measurement of the export coal humidity value, and there is a problem of time lag in laboratory measurement.

The invention discloses a method for dynamic collaborative dispatching of double yard bridges in a container yard, which includes step 1, using container yard operation simulation to simulate and learn the designed Q value table, and obtaining the Q value table after the simulation study; step 2, using The learned Q value table and action selection strategy dynamically generate the action instructions of the yard bridge, and the yard bridge selects tasks to carry out operations according to the action instructions. The method improves the operation efficiency of stacking and retrieving boxes in the storage yard, and reduces the interference of double yard bridges and the waiting time of vehicles.

The invention proposes a reinforcement learning variable-duration signal light control method based on the Internet of Things equipment, which mainly includes the following aspects: a reinforcement learning method based on the concept of "intensity" of the intersection is designed, and the data collected by the Internet of Things equipment is collected. Various real-time traffic information (such as vehicle position, speed, etc.) to control the phase selection of signal lights. At the same time, a most reasonable green light duration can be selected according to the number of vehicles in each lane. The invention can quickly converge to an excellent signal light control strategy under the condition of dynamic traffic changes, greatly shorten the learning time of the strategy and improve the control quality of the strategy.

The invention discloses a fault detection method for loose and missing key nuts of small parts bearings of railway freight cars, belonging to the technical field of freight train detection. The present invention aims to solve the problems of high cost and low efficiency in the current detection method relying on manual viewing of images, and the problem of low detection accuracy in the existing automatic image processing technology. The invention collects images and extracts the image of the bearing block key area, constructs a sample data set, uses the sample data set to train the deep learning network, and obtains a trained deep learning network; extracts the image of the bearing block key area from the real passing car image and uses the trained depth The learning network obtains the ternary segmentation image; using the ternary segmentation image for fault detection, if there is no nut in the bolt area, it means that the key nut is missing; if part of the bolt area is above the nut, it means that the key nut is loose; if the above If so, a fault alarm will be issued. It is mainly used for the detection of loose and missing faults of bearing retaining key nuts.