31 results about "Layered learning" patented technology

The invention provides a micro-grid analog simulation training system based on the virtual reality technology. The micro-grid analog simulation training system comprises a virtual reality host and anoperation module, wherein the virtual reality host comprises a scene creating module, a mode selection module, a feedback module and a background module; the operation module is used for operating thebehavior of a virtual character, controlling the advancing of the virtual character, operating a device button and a switch, and operating the content in microgrid control software. For the system, by virtue of the virtual reality technology, the micro-grid training mode is changed, so that the training environment is vivid and controllable, the learning motivation of students is improved, the virtuality and reality combination and virtuality and reality interaction between virtual training and real training are achieved, and the gradual and layered learning is realized.

The invention discloses an image classification method based on a deep ridgelet neural network, and mainly solves problems that the prior art based on the neural network is long in image classification training time, and is not high in classification precision. The method comprises the implementation steps: 1, selecting 10% of data in an image library as a training sample, wherein the remaining data serves as test samples; 2, building a network structure of the deep ridgelet neural network, and enabling the training sample to serve as the input of the network; 3, carrying out the layered learning of parameters of each layer in the deep ridgelet neural network through a ridgelet auto-encoder; 4, enabling a parameter result of layered learning to serve as the initial values of parameters in the deep ridgelet neural network, carrying out the training of the parameters in the whole network through a gradient descending method, and obtaining a trained network; 5, inputting the test samples into the network, and obtaining a class label of each test sample. The method is high in classification precision, is high in training speed, and can be used for target detection and analysis and the detection of social activities.

The invention provides a training method and a device for determining a neural network of a molecular inverse synthesis route. The method comprises the following steps: for a plurality of molecules, when the inverse synthesis route of each molecule is determined, a hierarchical learning concept is adopted, the training process of the molecular inverse synthesis route which needs to be explored toa large depth is split into multiple layers for training, and the complete inverse synthesis reaction process is replaced by the multiple layers of molecular inverse synthesis routes; after the training of one layer of molecular reverse synthesis route is completed, representative molecules are selected as starting molecules of the next layer of molecular reverse synthesis route by means of a molecular screening mode, so that the exploration efficiency of the molecular reverse synthesis route can be effectively improved, and the accurate cost value information of the molecules can be extractedmore efficiently. By means of the layering mode, a large amount of calculation expenditure caused by determination of the molecular reverse synthesis route is greatly reduced, and on the basis that the accuracy of the molecular reverse synthesis route is guaranteed, the time for determining the molecular reverse synthesis route is shortened.

The invention discloses a high spectral image denoising method combining Bayes layered learning with space-spectrum combined priori; the method comprises the following steps: carrying out three dimensional slide block segmentation for a high spectral image according to a space spectrum correlation and non-local self-similarity thereof, and using relative distance priori based on fused features tonon-locally select a plurality of block data pieces to serve as synergy block data, wherein the selected block data is most similar to to-be-observed block data; using layered priori to build a Bayeslow-rank decomposition model, and realizing synergy block data learning and expression. The mode uses low-rank decomposition to carve statistics characteristics of synergy block data, and combines Dirichlet process mixing Gaussian distribution to express noise statistics characteristics; the method uses a variation Bayes method to solve the model, thus effectively reducing image noises. An existing method cannot simultaneously inhibit a plurality of noises in the high spectral image; the high spectral image denoising method combining Bayes layered learning with space-spectrum combined priori can solve said problems, is accurate in result, and can provide strong analysis basis for follow up applications.

Techniques are disclosed for building a dictionary of words from combinations of symbols generated based on input data. A neuro-linguistic behavior recognition system includes a neuro-linguistic module that generates a linguistic model that describes data input from a source (e.g., video data, SCADA data, etc.). To generate words for the linguistic model, a lexical analyzer component in the neuro-linguistic module receives a stream of symbols, each symbol generated based on an ordered stream of normalized vectors generated from input data. The lexical analyzer component determines words from combinations of the symbols based on a hierarchical learning model having one or more levels. Each level indicates a length of the words to be identified at that level. Statistics are evaluated for the words identified at each level. The lexical analyzer component identifies one or more of the words having statistical significance.

Systems and methods are provided for performing medical imaging analysis. Input medical imaging data is received for performing a particular one of a plurality of medical imaging analyses. An output that provides a result of the particular medical imaging analysis on the input medical imaging data is generated using a neural network trained to perform the plurality of medical imaging analyses. Theneural network is trained by learning one or more weights associated with the particular medical imaging analysis using one or more weights associated with a different one of the plurality of medicalimaging analyses. The generated output is outputted for performing the particular medical imaging analysis.

The invention discloses a time delay and energy consumption compromise model in an extended unmanned aerial vehicle network and a hierarchical learning algorithm. The time delay and energy consumptioncompromise model is characterized in that: an unmanned aerial vehicle network is divided into clusters, and each cluster comprises a large unmanned aerial vehicle serving as a cluster head, a relay unmanned aerial vehicle and a small unmanned aerial vehicle group serving as an extended unmanned aerial vehicle group; the relay unmanned aerial vehicles form a core network, the extended unmanned aerial vehicles form an extended network, idle unmanned aerial vehicles serve as the relay unmanned aerial vehicles, the relay unmanned aerial vehicles are used for assisting the extended unmanned aerialvehicles in transmitting information to the cluster heads, and the relay unmanned aerial vehicles are further used for adjusting a bandwidth strategy of an unmanned aerial vehicle network system to improve the bandwidth utilization rate and realizing compromise between time delay and energy consumption of unmanned aerial vehicle network communication according to a coupling relationship between power and bandwidth resources of unmanned aerial vehicle network communication. In combination with other structures or methods, the defect that the time delay and throughput performance and the resource utilization rate are not considered at the same time when the system delay problem of the unmanned aerial vehicle networks is optimized in the prior art is effectively avoided.

The present teaching relates to method, system, medium, and implementations for machine learning for time series via hierarchical learning. First, global model parameters of a base model are learned via deep learning for forecasting time series measurements of a plurality of time series. Based on the learned base model, target model parameters of a target model are obtained by customizing the base model, wherein the target model corresponds to a specific target time series from the plurality of time series for forecasting time series measurements of the specific target time series.

The invention provides a multi-AUV (Autonomous Underwater Vehicle) collaborative navigation method based on reinforcement learning. According to the method, a collaborative navigation process is divided into two main processes: firstly, a trajectory planning process: a trajectory of a slave AUV in a cluster is obtained through manual planning according to a task to be executed, and a trajectory of a master AUV is obtained through a trajectory planning method based on hierarchical Q learning; and then, a navigation calculation process: a proper nonlinear filtering algorithm is selected to carry out actual navigation calculation. Test verification shows that after the master AUV sails according to the trajectory planned by the method and then the slave AUV performs navigation calculation by using nonlinear filtering, the positioning error of the slave AUV can be obviously reduced.

The invention relates to an anti-interference channel selection method and system for a cognitive satellite-ground network, and the method comprises the steps: modeling an anti-interference decision problem as a Stackelberg game based on an obvious layering behavior between cognitive users and external malicious interference, building a lower-layer sub-game model of a graph game according to the characteristic that the interference between the cognitive users presents local influence, and carrying out the calculation of the anti-interference channel selection of the cognitive satellite-ground network. And finally, the corresponding sub-game model is converged based on the hierarchical learning algorithm of local information interaction and the Q learning algorithm, compared with other algorithms, the convergence performance is outstanding, and the system average throughput of the algorithm is close to the optimal NE solution, so that the problems that the algorithm adopted in the prior art is poor in convergence effect and inaccurate in channel selection result are solved, and the channel selection efficiency is improved. The current situations of frequency shortage and low spectrum utilization rate are relieved, and the spectrum resource utilization rate is improved.

The invention discloses a communication network architecture generation method and device, electronic equipment and a medium. By applying the technical scheme of the invention, the distributed unit DU and the centralized unit CU of the base station equipment can be utilized, and the distributed unit DU and the centralized unit CU form a communication network architecture together with the edge node. In this way, the model parameters of the device nodes in the communication network aggregation network can be aggregated subsequently to obtain aggregated model parameters, and the aggregated model parameters are used for hierarchical learning training to obtain a hierarchical federated learning model for service processing at a user equipment end or a base station end. And thus, the purpose of optimizing the service processing efficiency by using the communication network architecture is achieved.

The invention discloses a multi-extreme learning machine-based post-fault transient stability hierarchical evaluation method for a power system. The multi-extreme learning machine-based post-fault transient stability hierarchical evaluation method comprises the steps: extracting key features capable of representing the transient stability condition of the current power system from historical big data of the power system according to a maximum correlation minimum redundancy algorithm MRMR; carrying out hierarchical learning training on the ELMs by using the extracted key features of the transient stability condition of the power system; and then inputting the key feature group of the power system after the fault into the trained ELMs of each layer, and finally obtaining a final transient stability evaluation result by utilizing a decision evaluation criterion of each layer. The multi-extreme learning machine-based post-fault transient stability hierarchical evaluation method has high precision and meets the rapidity requirement of big data of the smart power grid.

The invention discloses a multi-region active power distribution system peak regulation optimization method considering power consumption demand elasticity. The method comprises the following steps: firstly, establishing a mathematical model of a photovoltaic and all-vanadium redox flow battery energy storage system and a multi-region flexible load scheduling unit in an electric power elastic environment; then, establishing a DTMDP model for the peak regulation optimization problem of a multi-region active power distribution system considering power consumption demand elasticity; and finally, solving the mathematical model in combination with reinforcement learning and an intelligent algorithm to obtain a multi-region scheduling optimization control strategy meeting peak regulation requirements. According to the invention, the hierarchical learning mechanism in the invention avoids the problem of curse of dimensionality of reinforcement learning to a certain extent, and promotes rapid solution of a scheduling strategy; the exploration capability of the algorithm is further enhanced through combination of reinforcement learning and an intelligent algorithm, and the optimal peak regulation strategy can be obtained; and potential dispatching information of the active power distribution system can be further obtained by considering electricity demand elasticity, and stable and safe operation of the system is promoted.

The invention provides a cascading neural network-based membrane pollution multi-index identification method, which is characterized in that the method considers the interference of irrelevant input variables on output indexes, can avoid the influence of the irrelevant variables on output based on a cascading structure, and adjusts the parameters of a cascading neural network through a hierarchical learning algorithm. The membrane pollution multi-index prediction is realized, the problem that the membrane pollution multi-index prediction precision cannot meet the requirement is solved, and the method has relatively high prediction precision and identification effect.

The invention discloses a method for constructing a hierarchical learning space, which comprises the following steps of: classifying subject skills, and identifying parallel or inclusion relations among the skills; Layering correspondingly according to different types, and calling a'plane learning space generation function 'to generate learning spaces of a plurality of planes one by one from the first layer according to the micro-skill relationship layer by layer; And calling the function combination of the'combined plane learning space component layer multi-dimensional learning space 'to generate a multi-dimensional layered stereo learning space. According to the method for constructing the hierarchical learning space provided by the invention, knowledge points related to subjects are constructed to form a learning space, and progressive learning is realized according to different mastery degrees of the knowledge points. And the mastered knowledge points can be objectively evaluated,the mastering degree of the knowledge points is known, and targeted learning is realized.