17 results about "Hierarchical neural network" patented technology

The invention discloses a wireless communication network intelligent optimization method and system based on neuron collaboration, and relates to the technical field of wireless communication. The method comprises the following steps: mapping a communication node into a bottom layer sensing neuron and constructing a neural state variable set; constructing a node neural situation function based on the variables; when the local threshold value is exceeded, excitation pulses are generated and uploaded to middle-layer convergence neurons; the middle layer carries out pulse space aggregation and extreme value search, and outputs a selection strategy and an adjustment strategy; and reporting to a top layer to execute whole network neural synchronization index analysis, and optimizing the wireless communication network. The technical problems of low spectrum resource utilization rate and unstable network performance caused by the fact that a traditional wireless communication network cannot realize high-efficiency spectrum allocation and dynamic topology reconstruction under user mobility change are solved, and the purposes of realizing local quick response and global collaborative optimization by constructing a layered neural network and improving the network performance are achieved. And the spectrum resource utilization rate is improved, and the network dynamic adaptive capability is enhanced, so that the user service quality is guaranteed.

The invention discloses a time-varying and time-lag multi-CSTR system adaptive control method and system based on a neural network, and the method comprises the steps: collecting key operation parameters of a multi-CSTR system in real time, constructing a time-varying and time-lag dynamic model through a recurrent neural network, and updating a time-lag parameter estimation value. And designing a hierarchical neural network structure comprising an upper global coupling model and a lower local compensation model, designing an adaptive algorithm based on a stability theory to adjust the weight of the neural network, and generating a control instruction to drive an execution mechanism. The system correspondingly comprises a data acquisition layer, a time-varying time-delay estimation module and the like. According to the scheme, the problems of time varying, time lag and coupling of the multi-CSTR system are solved, and stable operation of the system is guaranteed.

A system and method for robotic teleoperation enable a teleoperated robotic element to perform a sequence of actions based on intention estimation, eliminating the need for continuous human control. The system includes a robotic teleoperation input that receives motion inputs and gaze data from a human operator performing a robotic teleoperation task. A robotic teleoperation feature extractor analyzes and processes the motion inputs and the gaze data into sequential input data. A multi-window model assigns hierarchical prediction windows to the input data, generating windowed sequential data. A hierarchical neural network processes the windowed sequential data to determine low-level action intentions and high-level task intentions, and generates an intention estimation based on the low-level action intentions and the high-level task intentions. A hierarchical dependency model incorporates hierarchical dependent loss to refine the intention estimation.

The invention provides a diversified multi-hop question generation method and system based on knowledge combination sampling, and relates to the technical field of multi-hop questions.The generation method comprises the steps that context knowledge and target answers are obtained, and the target answers are obtained based on a hierarchical neural network architecture; obtaining conditional probability distribution data and an initial knowledge combination according to the context knowledge and the target answer; obtaining a plurality of diversified knowledge combinations according to the initial knowledge combination and the conditional probability distribution data through a preset sampling strategy; and generating a corresponding multi-hop question according to all the diversified knowledge combinations and the target answer through a preset multi-hop question generation model. The whole process does not need complex intermediate links and can be realized by relying on a mature neural network architecture, the manpower and hardware cost of technology landing is reduced, for example, when a multi-hop question bank is constructed in the education field, a teacher only needs to input a textbook text and knowledge points, multi-hop questions of different examination angles can be automatically generated, and the efficiency is improved. And the problem of data sparsity of the multi-hop question-answer data set is effectively relieved.

The invention designs a method for predicting the health state of an energy storage battery, and the method comprises the steps: S1, carrying out the collection and monitoring of the charging and discharging process of an operation battery, and collecting and storing the charging and discharging capacity data of each cycle of the battery; s2, preprocessing the collected discharge capacity data of the batteries, calculating battery health state data, constructing a time sequence sample by using a sliding window mechanism, and dividing the battery health state data of each battery into an input sequence and an output sequence; s3, constructing a hierarchical neural network architecture and training the hierarchical neural network architecture; s4, performing Bayesian optimization on the hierarchical neural network architecture based on an Optuna framework; and S5, predicting the energy storage battery by using the optimized neural network architecture to obtain the health state of the battery. Through the above design, the prediction precision of the battery health state can be improved, and the stability and robustness of overall prediction are enhanced.

A method of training a hierarchical neural network in hardware comprising a plurality of neurons having tree structure connections between respective ones of the plurality of neurons to compute plausible inferences based on input data. The method includes initiating a WTA neuron ensemble to connect to data input neurons and produce a winner-take-all, attaching data records match with top neurons and branching out at WTA junctions to form a hierarchy network tree, converting a stream of the input data into neuron bipolar signals, updating neuron weights with Hebbian and anti-Hebbian rules, and repeating the attaching, converting, and updating until the input data is exhausted.

The application relates to the field of geomagnetic vector measurement, and discloses a magnetic target intelligent positioning method and system based on a layered neural network architecture. The method comprises the following steps: acquiring multi-point magnetic field data measured by a magnetic sensor array; calculating magnetic gradient tensor data according to the magnetic field data and the spatial position of the sensor; inputting the magnetic gradient tensor data into a pre-constructed layered neural network model; extracting local space-time features from the magnetic gradient tensor time series data of each measurement point through a sub-network layer; performing time series fusion and global context modeling on the feature sequences output by all sub-network branches through a global fusion network layer; and outputting the three-dimensional spatial position coordinates of the magnetic target according to the fused global features through an output layer. The implementation of the application is a deep integration of physical analysis methods and data-driven methods, can guide the mapping relationship between the magnetic gradient tensor and the position of the magnetic target of the deep learning model, and retains the strong nonlinear fitting capability, thereby enhancing the positioning accuracy of the magnetic target.

The invention relates to a self-adaptive agile control method for a high-speed maneuvering flight-oriented intelligent unmanned aerial vehicle with a body, and the method comprises the steps: building a four-rotor unmanned aerial vehicle kinetic model, and outputting the coupling characteristics of aerodynamic force and torque and the prediction information of a future motion state; constructing a reinforcement learning strategy of the Lyapunov stability constraint, and obtaining a compensation control strategy according to output information and tracking errors of the kinetic model; constructing a hierarchical neural network architecture, and generating a motion control instruction of the quad-rotor unmanned aerial vehicle based on a compensation control strategy and latest state information fed back by a sensor; non-linear interference in flight is estimated through a non-linear interference observer, the non-linear interference is fed back to an adaptive control law in the hierarchical neural network, and control input of the hierarchical neural network is optimized in a prediction window through a rolling horizon optimization algorithm; and generating a motion control instruction for intercepting the target unmanned aerial vehicle by using the compensated and optimized adaptive control law and the control input, and sending the motion control instruction to an execution mechanism of the four-rotor unmanned aerial vehicle.

This invention discloses an intelligent analysis and control system and method for the entire lifecycle of gas wells based on artificial intelligence, relating to the field of oil and gas field development technology. It constructs a differentiable physical enhancement model, in which the gas well physical equations are embedded as differentiable hierarchical neural networks. Using historical data for training, a digital twin model is constructed that conforms to physical laws and possesses data fitting capabilities, and the digital twin model is end-to-end differentiable. Based on the differentiable physical enhancement model, a safety rating function is designed, mapping each state-action pair to a continuous safety score. This invention, by constructing a differentiable physical enhancement model, integrates gas well physical laws with data-driven capabilities, ensuring that state prediction and strategy output conform to physical mechanisms, avoiding unauthorized operations, and improving control reliability. Through the safety rating function and safety rating strategy gradient algorithm, safety constraints are implemented in the strategy optimization process, mitigating the impact of unsafe actions and ensuring gas well production safety.

This invention discloses a hierarchical method for parsing and annotating single-cell chromatin accessibility data. The method includes: acquiring single-cell chromatin accessibility data; screening genomic region features based on cell type specificity to determine multiple feature sets; converting the genomic region features of each cell into a structured sequence; processing the structured sequence using a hierarchical neural network model, extracting local features within sub-units to obtain sub-unit representations, and integrating global features between sub-units to obtain cell-level representation vectors; and predicting the cell type of each cell using a classification model based on the cell-level representation vectors to generate cell type annotation results. This application effectively overcomes the high-dimensional sparsity and cell abundance imbalance problems of single-cell chromatin accessibility data, achieving stable identification and annotation of low-abundance cell types and improving the interpretability of data analysis.

The present invention relates to a device and method for estimating a virtual reality pose through a haptic device, wherein the device comprises: an input unit that receives input point data for estimating the pose of the haptic device through a plurality of input devices; a global feature extraction unit that inputs the input point data into a Multilayer Perceptron (MLP) model to extract point-specific features and extracts global features based on the point-specific features; and a pose estimation unit that inputs the global features into a hierarchical neural network to estimate the pose of the haptic device.

The invention relates to an air conditioner power prediction and control method based on a comprehensive intelligent zero-carbon power plant cloud platform. The method comprises the steps that S1, historical operation data and real-time operation data of an air conditioner and a temperature and humidity sensor are collected; s2, on the basis of the actual layout of the air conditioner and the structure of the room, a multi-level neural network model is constructed and used for predicting the power of the air conditioner; s3, training the neural network model by adopting historical operation data; s4, establishing an optimization algorithm objective function of the optimal air conditioner control strategy; s5, determining an optimal air conditioner control strategy through an optimization algorithm; s6, deploying the neural network model and an air conditioner control strategy optimization algorithm on a comprehensive intelligent zero-carbon power plant cloud platform; and S7, after the comprehensive intelligent zero-carbon power plant cloud platform receives a demand response instruction issued by the power grid, an optimal control instruction is obtained through an embedded algorithm, and the instruction is sent to each air conditioner device. Compared with the prior art, the method has the advantages of low calculation cost, more accurate prediction effect and the like.

The invention provides a network security insurance underwriting evaluation method, device and equipment fusing multi-source heterogeneous data and a medium, and relates to the field of network security insurance, and the method comprises the steps: obtaining multi-source data of an insured person, the multi-source data comprises risk factor data and insurance factor data, the risk factor data comprises external rating data and internal control data of the insured person information system; analyzing and processing the risk factor data, constructing a risk factor characteristic factor set, analyzing and processing the insurance factor data, and constructing an insurance factor characteristic factor set; based on the risk element feature factor set and the insurance element feature factor set, constructing a network security insurance underwriting evaluation model of a multi-modal feature hierarchical neural network architecture by simulating risk decision tree logic; and inputting the risk factor data and the insurance factor data into the network security insurance underwriting evaluation model, and outputting an underwriting conclusion. According to the invention, the comprehensiveness and accuracy of the underwriting evaluation conclusion are improved by analyzing the multi-source data generated in the evaluation process.

The present invention relates to a virtual reality pose estimation apparatus and method using a haptic device. The apparatus comprises: an input unit for receiving, through a plurality of input devices, input point data for the estimation of a pose of a haptic device; a global feature extraction unit which inputs the input point data into a multilayer perceptron (MLP) model so as to extract point-wise features, and which extracts a global feature on the basis of the point-wise features; and a pose estimation unit for estimating the pose of the haptic device by inputting the global feature into a hierarchical neural network.

The invention discloses a shaftless rim type pipeline generator intelligent body, which comprises a shaftless rim power generation module and an intelligent control module, the shaftless rim power generation module is used for executing a power generation instruction, the intelligent control module is used for sending a power generation control instruction, and the shaftless rim power generation module and the intelligent control module are fixedly connected and realize data interaction and instruction transmission; the sensing sub-module is used for collecting multi-dimensional data; the analysis sub-module processes the collected data and outputs an analysis result; the hierarchical neural network learning sub-module is internally provided with a neural network, receives an analysis result and outputs an optimal result; the decision sub-module outputs and analyzes a result based on a neural network; and the security assurance sub-module is used for providing multi-dimensional protection. According to the shaftless structure, the power generation efficiency and reliability are improved, the shaftless rim type rotor design is adopted, friction and water flow blocking of a traditional center shaft are eliminated, water flow resistance is reduced, and the power generation efficiency is improved; the self-lubricating bearing prolongs the maintenance period, the operation stability is obviously improved, the power generation efficiency is improved, and the annual average maintenance cost is reduced.

The invention discloses a method and a system for predicting crystal plasticity constitutive parameters of a single crystal based on nanoindentation data driving, and belongs to the technical field of crossing of material computational mechanics and artificial intelligence. The method comprises the following steps: generating a nanoindentation simulation data set and a uniaxial tension simulation data set by using a crystal plasticity finite element, and constructing a hierarchical mapping relation of nanoindentation curve-stress-strain curve-crystal plasticity constitutive parameter by using a multilayer sensor. According to the method, a staged hierarchical neural network prediction framework based on single crystal nanoindentation data driving is constructed, and efficient and accurate prediction of crystal plasticity constitutive parameters is realized.

The invention discloses a distributed regulation and control power generation system based on a shaftless flange type pipeline generator intelligent agent, which comprises an intelligent agent, a hierarchical neural network system and a distributed regulation and control platform, and is characterized in that the intelligent agent is the shaftless flange type pipeline generator intelligent agent; a plurality of intelligent agents are arranged and are connected to the water conveying pipeline through a pipeline manifold, the plurality of intelligent agents are interconnected through a local bus to form an equipment cluster, and the equipment cluster is connected to the distributed regulation and control platform through a standardized communication interface; the intelligent body comprises a shaftless rim power generation module and an intelligent control module which are fixedly connected and achieve data interaction and instruction transmission. According to the invention, a three-layer neural network system is arranged, the individual layer neural network ensures autonomous and reliable operation of a single device, the regional layer realizes intra-regional collaborative optimization, and the global layer realizes cross-regional resource configuration; the three mechanisms of transfer learning, federal learning and knowledge distillation not only shorten the new equipment adaptation period, but also guarantee data security and decision-making efficiency.