4results about How to "Efficient learning process" patented technology

The application discloses a LIBS element quantitative analysis method based on double-branch feature fusion and electronic equipment, the method comprises the following steps: preprocessing the collected LIBS spectrum signal, including baseline correction and spectrum resampling; inputting the spectrum signal data after preprocessing into a double-branch feature extraction network, extracting local features through a CNN branch, and extracting global features through an MLP branch in parallel; performing adaptive weighted fusion on the local features and the global features through a gating fusion module to obtain fusion features; inputting the fusion features into a WMA-MLP model, the WMA-MLP model is based on MLP, integrates a multi-head self-attention mechanism and a residual module, is used for modeling the global dependency relationship between features, and outputs a final element quantitative analysis result. Through the parallelly arranged CNN and MLP branch feature extraction structures, more comprehensive spectrum feature representation can be obtained, and the accuracy and robustness of the spectrum analysis model are improved.

The application discloses a kind of brain-like reinforcement learning method and system based on layered experience playback, it is related to reinforcement learning and brain-like computing technical field.The method comprises the following steps: S1, collecting observation data and pre-processing;S2, initialize experience buffer pool and actor network, critic network and corresponding target network, parameter initialization;S3, initialize exploration noise and select action from actor network according to current state and execute, store the obtained experience sample to experience buffer pool;S4, obtain new sample from experience buffer pool, carry out short-term memory pool update;S5, use attention discrimination module to determine whether part of experience in short-term memory experience pool is transferred to long-term memory experience pool;S6, the parameters of actor network, critic network and corresponding target network are updated.The application uses the above method, improves the experience utilization rate of intelligent agent, improves the performance of reinforcement learning, and has wide application potential in many fields.

This invention discloses a multi-UAV task allocation and fusion method based on improved reinforcement learning, comprising: determining an initial parameter set for multi-UAV task allocation; fragmenting and decomposing multi-task objectives to determine cluster centers; using an adaptive clustering strategy based on center points to screen candidate clustering strategies and determine the optimal cluster set; determining a dynamic decay gradient strategy for the improved Q-learning network based on the action value function of basic reinforcement learning; determining a spiral cross-assignment mechanism for task redistribution based on a task redistribution mechanism with weighted coefficients; and determining a multi-UAV task allocation strategy based on the optimal cluster set of task points, the dynamic decay gradient strategy of the improved Q-learning network, and the task redistribution mechanism to complete the multi-UAV task allocation. This invention can effectively avoid unnecessary redundant allocation, improve the time consumption and range of task allocation, and increase the efficiency of task allocation.

The application relates to a dynamic gradient compression learning method of a federal learning system and belongs to the field of artificial intelligence. The method comprises the following steps: constructing a federal learning model, a server distributing different sampling communication bandwidths to clients, and the clients obtaining local models after training; considering resource constraints and gradient states of each client local model, calculating a pruning rate and a quantization level of a dynamic gradient compression strategy in this round; according to the pruning rate, each client performs a sparse operation on the gradient of the client to obtain a sparse gradient; according to the quantization level, each client performs a quantization and encoding operation on the sparse gradient to obtain compressed gradient data; each client uploads the compressed gradient data to the server, the server decompresses the gradient data to obtain decompressed gradient data; and the server aggregates the decompressed gradient data according to elements and updates a global model until the model converges. The application realizes efficient communication federal learning with better performance in a resource-limited scene.