7 results about "Stochastic matrix" patented technology

The invention discloses an unmanned aerial vehicle cluster patrol path decision-making method under resource constraint, and relates to the technical field of unmanned aerial vehicle path planning, and the method comprises the steps: carrying out the discretization of an actual to-be-patrolled physical position, and constructing an undirected topological graph; generating steady-state distribution of each patrol node according to topological constraints and importance degrees of the nodes; generating a plurality of transfer matrixes with the same steady-state distribution and different transfer characteristics according to a multi-stage entropy driving random matrix optimization algorithm; initializing the position of a navigator according to the transfer matrix and determining a selected path; according to the reference path of the navigator, the self-adaptive active positioning decision is realized under the positioning constraint to ensure the path tracking effect; according to path selection and tracking of the navigator, the follower and the navigator form a humanoid marshalling cluster through a reward function; and according to the multi-state transfer matrix and the humanoid marshalling, automatically switching to the next transfer matrix when a transfer frequency threshold value is reached, and realizing unmanned aerial vehicle cluster intelligent patrol path decision under resource constraint.

The invention provides an optimal scheduling method and system for a multi-region integrated energy system, and the method comprises the steps: enabling the integrated energy system of each region to be managed by a corresponding local agent, training a preset energy optimal scheduling model through the local agent based on a reinforcement learning idea, and obtaining a local optimization model; the local optimization model is updated according to the encryption parameter data packet of the neighbor intelligent agent and a double random matrix constructed based on the topological relation between the integrated energy systems, and the local optimization model of each local intelligent agent is updated after the local optimization model of each local intelligent agent is updated; and carrying out overall convergence evaluation on all the local optimization models, stopping cooperative training among the local intelligent agents when a preset optimization target is reached, and executing integrated energy system optimization scheduling of the corresponding region according to the local optimization model of each local intelligent agent. According to the method, the risk of data privacy leakage of the integrated energy system in each region can be effectively avoided, and the real-time performance and reliability of optimal scheduling of the multi-region integrated energy system can be improved.

The invention belongs to the technical field of information communication, particularly relates to a directed network environment variance reduction distributed self-adaptive gradient tracking algorithm and system, and aims to solve the following problems in the prior art: 1) an existing distributed self-adaptive random optimization algorithm uses double random matrixes as communication matrixes and cannot be applied to directed network topology; and 2) the existing distributed stochastic optimization algorithm is limited by the property of stochastic gradient in any strong connectivity network, and the optimization precision is influenced by steady-state errors. An efficient variance reduction distributed adaptive gradient tracking algorithm-VRDAGT algorithm is provided, the algorithm combines a gradient tracking technology, an adaptive step length technology and a variance reduction technology, and eliminates steady-state errors introduced by stochastic gradients under a push-pull framework; by using the row random matrix and the column random matrix, the VRDAGT algorithm gets rid of dependence on double random matrixes, so that the applicable network topology range of the algorithm is widened. Compared with an existing algorithm, the VRDAGT algorithm has higher optimization precision and a wider application range.

This invention discloses a residual routing constraint method and system for enhancing the training stability of neural networks, comprising the following steps: S1, based on the residual routing constraint in the neural network... l Input feature tensor of the layer x l S1. Generate the original values ​​of the pre-scaling matrix, the post-scaling matrix, and the residual routing matrix; S2. Apply non-negativity constraints to and to obtain the pre-scaling matrix. H pre and post-scaling matrix H post By constraining optimization to satisfy the double stochastic matrix condition, the residual routing matrix is ​​obtained. H res S3, Utilize H pre right x l Premixing is performed to obtain preprocessed characteristics. u l ;Will u l Enter to the number l Layer core calculation function F layer Calculations are performed to obtain the layer output features. y l ;Will H post Acting on y l The scaled output will be obtained. H res Acting on x l After obtaining the stable residual, the scaled output is summed with the stable residual to obtain the first... l Layer output features x l +1. This invention significantly improves the numerical stability of deep neural network training.

Disclosed are a stochastic matrix-vector multiply-accumulate operation system and an operation method thereof. The operation system includes an input bit-stream generator, a weight bit-stream generator and a memory array. The input bit-stream generator generates a pulse sequence representing a corresponding input stochastic bit-stream according to respective preset input values of a preset input vector, the weight bit-stream generator generates a pulse sequence representing a corresponding weight stochastic bit-stream according to respective preset weight values of a preset weight matrix, and the pulse sequence representing the input stochastic bit-stream and the pulse sequence representing the weight stochastic bit-stream are applied to a word line and a bit line of the memory array respectively. In addition, a result multiply-accumulating between the input stochastic bit-stream and the weight stochastic bit-stream of each memory device of the memory array is stored as a conductance value of the corresponding memory device.

A method for building a quantum computing circuit optimizes qubit routing in the circuit. A computer processor receives a plurality of qubits and an initial input circuit layer. Layers of quantum sub-circuits are extracted from the initial input circuit layer. Adjacency matrices are built for the layers of quantum sub-circuits. A cost function is determined for the extracted layers, based on the number of constraints violations determined by the doubly stochastic matrices. In addition, a final quantum circuit topology is selected based on the cost function of the extracted layers.

The present invention relates to a method for detecting all nodes of a brain functional network based on random matrix analysis, comprising: collecting electroencephalogram signals, constructing functional brain networks of different motor tasks, and obtaining a brain network data set, wherein the brain network data set is classified into categories with different motor tasks as labels; performing a difference analysis on the functional brain networks of the different motor tasks based on random theory, obtaining a functional brain network that meets a difference threshold, and screening the motor tasks; obtaining the functional brain network and node features of the screened motor tasks and inputting them into an R-GCN model to obtain a motor task prediction result. The present invention uses random matrix theory to analyze the eigenvalue statistical characteristics of a brain network based on electroencephalogram signals to achieve the accuracy of the result quantification theoretical prediction.