1366 results about "Hidden layer" patented technology

Systems and methods for adaptive edge computing using artificial intelligence (AI) include monitoring real-time accuracy of a neural network by using a feedback loop configured to detect changes in inference accuracy and dynamically adjusting the structure of the neural network by adding or removing hidden layers based on monitored error rates and predetermined computational constraints. A Kalman gain computation determines neural network weight adjustments based on monitored error rates. Weight matrices undergo incremental updates derived from these adjustments. Incremental weight adjustments remain stored in memory to enable low-bandwidth model updates. The neural network stores inference results and refined weights in an inference result database. Pre-trained models periodically receive incremental updates based on stored adjustments. Predictive holistic inference logic (PHIL) applied to stored inference results improves the accuracy of the inference results.

The invention provides an intelligent incubation bin anti-interference control method and system based on error self-learning, and relates to the technical field of intelligent control, and the method comprises the steps: respectively calculating a temperature deviation value and a humidity deviation value according to an optimization parameter sequence of a temperature prediction error and an optimization parameter sequence of a humidity prediction error; performing normalized weighted summation on the temperature deviation value and the humidity deviation value, and performing dynamic scaling through a Gaussian kernel function to obtain a correction factor; dynamically adjusting the activation function slope of a neural network hidden layer according to the correction factor and a BP neural network, reconstructing the weight of the fuzzy rule base based on the numerical distribution characteristics of the correction factor, and generating the corrected weight of the fuzzy rule base; and based on the corrected fuzzy rule base weight, constructing a three-dimensional parameter adjustment curved surface, and dynamically adjusting proportion, integral and differential parameters of a PID controller through a curved surface gradient search algorithm to generate a control signal. According to the invention, the control accuracy is improved.

The invention discloses a quasi-brittle material damage field inversion method based on a physical information neural network. The method comprises the steps of data acquisition and processing; presetting initial damage field data, and taking the modulus of each unit in the initial damage field data as an independent to-be-inverted parameter; taking the position information of each node in the space as the input of a neural network, processing through a hidden layer of the neural network, and taking displacement data corresponding to the position information as network output data; constructing a loss function of a neural network according to the preprocessed displacement data, network output data and a mechanical law; and performing training optimization on the initial damage field data by using a loss function of a minimized neural network, optimizing hyper-parameters of the neural network by adjusting weights of control item loss, boundary item loss and data item loss until a preset convergence condition is met, and determining target damage field data. According to the method, the dependence of a neural network model on a data set is remarkably reduced, and the inversion result is ensured to have relatively high physical interpretability.

The invention relates to the technical field of air conditioning, in particular to a laboratory heating and ventilation load prediction and self-adaptive regulation and control method. According to the method, the infrared frame and the power sampling time mark are synchronized, the sensing flow is aligned and packaged, and the thermal diffusion evolution rate is generated. Lagging characteristics are determined in combination with power jump and temperature rise moments, and a heterogeneous dynamic coupling model is established. Extracting a physical evolution parameter as a mechanism operator, injecting the mechanism operator into a hidden layer of the prediction model, reconstructing a phase space, and calculating an air load increment in a lag window. And reverse mapping is executed based on the heat exchange characteristics to generate a feedforward instruction, and when the rate exceeds a threshold value, the weight is issued and dynamically corrected, so that closed-loop correction is completed. According to the method, deep coupling of feedforward prediction compensation and feedback residual adjustment is executed, and accurate regulation and control of the air volume and cooling and heating loads of the laboratory are achieved.

The invention discloses an intelligent blasting sequence control system for mixed loading explosives, which is provided with a data acquisition module, a data preprocessing module, a model construction module, a parameter optimization module, a multi-target control module and a data storage module to intelligently control the blasting sequence of the mixed loading explosives. Distance attenuation features, delay features, topographic features and blasting energy features of blasting data are constructed, the blasting prediction model is updated and iterated based on a neural network algorithm and historical blasting data information, and the performance of the blasting prediction model is evaluated. The number of hidden layers and the number of nodes in the blasting prediction model are updated and optimized through a control variable method, the blasting prediction model is optimized, when mixed explosive is adjusted and the blasting sequence is optimized, the optimal mixed explosive composition is searched through a search optimization algorithm, and the optimal mixed explosive composition is obtained through control signals in combination with real-time blasting data information. And the blasting sequence and delay are dynamically adjusted, so that the blasting of the mixed explosive is intelligently and accurately controlled in real time.

The invention discloses a marine environment forecasting method based on combination of machine learning and numerical forecasting, and particularly relates to the field of marine environment forecasting, which comprises the following steps: extracting marginal region features based on real-time observation data, constructing a forecasting field through a double-loss function, extracting extreme event features through space-time decoupling, and constructing a forecasting field through a double-loss function; and outputting a forecasting result of the target sea area. According to the marine environment forecasting method based on the combination of machine learning and numerical forecasting, the problem of initial field optimization failure caused by covariance matrix estimation deviation when marginal sea area data are sparse is relieved by embedding vorticity conservation equation residual calculation in a hidden layer; the dependence on the number of extreme event samples is reduced by realizing the targeted extraction of the extreme ocean event features; through a physical hard constraint layer technology of a residual error correction network, an initial field error of a data sparse region is corrected in combination with sea surface height and a flow velocity field constraint residual error feature map, and the problem of a large prediction error of an edge sea area is relieved.

The invention relates to the technical field of structural design, and discloses a topology and size joint optimization design method and system for a hierarchical composite structure, and the method comprises the following steps: dividing a structural domain into a plurality of hierarchical sub-domains, defining the size constraint range of each sub-domain, and distributing a multi-phase material for each sub-domain; grid discretization processing is carried out on the structural domain, a topological design variable is given to each discrete unit, acoustic unit modeling is carried out on the sound field domain, sound pressure boundary conditions are defined, and coupling boundary conditions are applied to the interface of the sound field domain and the structural domain; solving an acoustic-structure coupling equation based on a finite element method, calculating an objective function and constraint conditions, analyzing the sensitivity of the objective function to topological variables by adopting an adjoint method, and iteratively updating the topological design variables through an optimization algorithm until a convergence criterion is met; and on the basis of a topological optimization result, constructing a neural network agent model of an input layer, a hidden layer and an output layer, and carrying out global optimization on sub-domain size parameters by adopting a genetic algorithm so as to minimize a target function.

The invention discloses a cross-domain privacy protection method and system for advertisement recommendation, equipment and a medium, and the method specifically comprises the steps: carrying out the encryption matching of user behavior data and anonymized equipment data, and generating an initial cross-domain joint feature vector; fusing the initial cross-domain joint feature vector and the disturbance feature vector to form a target cross-domain joint feature vector; splitting a pre-trained advertisement recommendation model into a feature coding sub-module and a reasoning sub-module, deploying the feature coding sub-module to a user adjacent edge node, and retaining the reasoning sub-module in a user local device; and based on the target cross-domain joint feature vector, performing calculation of the feature coding sub-module and calculation of the reasoning sub-module, and uploading the encrypted hidden layer feature vector to a federated learning aggregation server for global model updating. According to the method, cross-domain data utilization and user privacy protection in an advertisement recommendation process are realized, and effective feature vectors are generated for personalized advertisement recommendation while data are guaranteed not to be out of a domain.

The invention discloses an unmanned platform electromechanical system fault early warning method based on a Transform-BiGRU cross attention mechanism, and belongs to the technical field of fault early warning. The method comprises the following steps: acquiring parameters, and performing time sequence alignment and key feature screening; secondly, designing a parallelization spatio-temporal feature extraction framework: on one hand, capturing a long time sequence dependency relationship through a multi-layer Transform encoder, on the other hand, realizing bidirectional context modeling of a sequence by adopting a bidirectional gating circulation unit, and introducing an interpretable global attention mechanism to perform feature weight dynamic allocation on a BiGRU hidden layer state; and a cross attention feature fusion module is constructed for a Transform and BIGRU fusion problem, and cooperative characterization of a time sequence dynamic feature and a space correlation feature is realized through interactive attention calculation of a spatial-temporal feature tensor. By means of the method, advanced early warning from diesel engine lubricating oil pressure abnormity to whole ship electric power system cascade faults is achieved.

The invention discloses an electromechanical composite transmission IGBT junction temperature prediction method based on a NODE reduced-order model. A thermal simulation model is constructed according to a real physical model mechanism, a material and a thermal transfer principle of an IGBT; in the constructed thermal simulation model, performing simulation calculation according to the monitored time sequence data to obtain the junction temperature of the IGBT corresponding to the time change, jointly establishing a data set for training and testing with the monitored data, establishing a NODE neural network order reduction model comprising an input layer, a hidden layer and an output layer, and training the NODE neural network order reduction model by using the data set; and inputting the IGBT related time sequence data into the constructed NODE neural network reduced-order model for prediction to obtain the time sequence data of the junction temperature. According to the method, the IGBT junction temperature can be predicted in real time, the calculation efficiency is high, the interpretability is high, the long-term dependence problem can be better solved, and the method is suitable for complex working conditions.

The invention discloses a roadway anchor net cable support parameter optimization design method based on a neural network, and belongs to the technical field of crossing of mining engineering and artificial intelligence, and the method comprises the following steps: collecting related data of a roadway, and carrying out the preprocessing of the collected data; building a dynamic self-adaptive BP neural network architecture, wherein the dynamic self-adaptive BP neural network architecture comprises an input layer introducing three cross features, a hidden layer adopting a dynamic adjustment strategy and an output layer; an anchor cable model and an anchor rod model are constructed based on a dynamic adaptive BP neural network architecture, and adaptive adjustment is performed by adopting a dynamic learning rate combined with surrounding rock complexity during model training; and inputting the five key influence indexes acquired in real time on site and the three calculated cross characteristics into the trained anchor cable model and anchor rod model to generate predicted anchor cable parameters and anchor rod parameters for on-site support design. The design efficiency of the roadway support parameter scheme is improved, the consumption of resources such as manpower is reduced, and the cost is reduced.

The invention discloses a quantification method and reasoning method of a large language model and electronic equipment, and belongs to the technical field of large language models.The quantification method of the large language model comprises the steps that each linear layer to be quantized in the large language model is quantized; dividing a channel of the linear layer in the hidden layer dimension into a normal channel and an outlier channel; performing INT8 quantization on the first activation matrix corresponding to the normal channel in a word segmentation token dimension to obtain a second activation matrix, and performing INT4 quantization on the first weight matrix corresponding to the normal channel according to an output channel to obtain a second weight matrix; and determining an output result of the linear layer according to the second activation matrix, the second weight matrix, a third activation matrix corresponding to the outlier channel and a third weight matrix corresponding to the outlier channel.

The invention discloses a pulse neural network image classification method and system based on a space-time adaptive threshold, mainly solving the problems of poor nonlinear expression and limited time sequence and space feature processing ability in the prior art, and the scheme comprises the following steps: obtaining an image data set, and dividing the image data set into a training set and a test set; a spiking neural network main body structure comprising an input layer, a hidden layer and an output layer is selected, an existing neuron model is improved by introducing a space-time joint threshold adjustment mechanism, and improved neurons are placed in each neuron layer in the hidden layer to form a spiking neural network based on a space-time adaptive threshold. The training set is used to carry out iterative training; and inputting the test set into the trained pulse neural network to obtain an image classification result. According to the method, a space-time adaptive threshold mechanism is introduced, the threshold can be dynamically adjusted to adapt to time and space features, the processing capacity of the network on time sequence data and complex features and the classification accuracy of images are remarkably improved, and the method can be widely applied to dynamic visual tasks and event-driven scenes.

The invention discloses a large model training method, a question and answer method, related equipment and a computer program product. The method comprises the steps of obtaining question and answer training data; the problem samples are sent to a to-be-trained large model for reasoning, and prediction output of the large model is obtained; calculating an exploration reward based on hidden layer state characteristics generated in a big model reasoning process, wherein the exploration reward is used for encouraging the big model to process the problem sample by adopting an unknown reasoning path; calculating a result reward based on the predicted output and the answer tag; and updating parameters of the large model by adopting a reinforcement learning mode according to the exploration rewards and the result rewards. Exploration rewards are additionally added in the reinforcement learning process, a large model can be encouraged to explore an unknown reasoning path, local optimum is avoided, the probability that the large model discovers a better reasoning path when facing a complex problem is improved, the performance of the large model on a complex reasoning task is improved, and the reasoning efficiency of the large model is improved. And the accuracy of the answering result of the complex reasoning question is improved.

The invention relates to a trajectory prediction model robustness enhancement method based on dynamic subspace projection decomposition. Comprising the following steps: firstly, extracting hidden layer semantic features containing historical tracks and map topology through a multi-modal feature encoder; secondly, constructing a dynamic routing mechanism based on scene self-adaption, and calculating projection weights of input features on a plurality of expert subspaces; then, executing truncation projection operation based on orthogonal decomposition, retaining core semantics located in a low-dimensional space, and filtering out adversarial disturbance located in an orthogonal complementary space; and finally, introducing a feature consistency constraint training mechanism, taking the reconstructed features of the clean sample as anchor points, and compulsively aligning the purified features of the confrontation sample with the anchor points. Compared with the prior art, the method has the advantages that the robustness of the model in white box gradient attack, black box query attack and physical semantic deception scenes is remarkably improved through feature purification of a physical level and structured consistency constraint, and the prediction reliability of the automatic driving system is ensured.

The invention relates to the technical field of relay protection, in particular to an automatic checking method and system for a relay protection setting value of a power plant, and the method comprises the steps: obtaining relay protection data, including obtaining electrical parameters, equipment parameters and power topological structure information; constructing a topological graph model of the power network by using the power topological structure information, constructing an electrical model based on elements according to the electrical parameters and the equipment parameters, and combining the topological graph model with the electrical model by using node parameter association to obtain a power system integrated model; and performing constant value check calculation based on the power system integrated model, and generating a report according to a constant value check calculation result. Hidden layers are set for different fault types by adopting the multi-branch structure network, so that the neural network can perform specialized learning for different types of fault features, and the adaptability of the system to different fault scenes is enhanced.

The invention relates to the technical field of motor fault diagnosis, in particular to a fault diagnosis method of a drilling machine variable frequency driving motor based on IMSOA-MCNN-BIGRU, and the method comprises the steps: collecting the current data of an experiment platform motor, and adding interference to simulate the data of a real drilling machine driving motor; a population initialization strategy of an SOA algorithm is improved, opposite-reverse learning is introduced in an iteration process, and an adaptive worst resampling mechanism after stagnation monitoring is added; the size of a convolution kernel, the size of a hidden layer, an initial learning rate and L2 regularization intensity of the MCNN-BIGRU model are optimized based on IMSOA, and optimized parameters are endowed to the MCNN-BIGRU model again so as to construct an IMSOA-MCNN-BIGRU classification model; and inputting the current data into which the interference is added into the IMSOA-MCNN-BIGRU classification model to obtain a diagnosis result. According to the method, the MCNN and the BIGRU are combined and complemented, the characterization capability and the fault judgment precision of complex non-stationary signals are improved, the MCNN-BIGRU model is optimized by using the improved sea gull optimization algorithm, and the performance of the model is improved.

The invention discloses a six-dimensional force / torque sensor decoupling system and method based on a width neural network, and the method comprises the steps: in a data collection and preprocessing unit, carrying out the null drift correction and normalization processing of a collected voltage signal through a weight type calibration platform and a data collection card, and forming a network model training data set; carrying out model training on the established six-dimensional force / torque sensor decoupling model of the width neural network, and solving a mapping matrix from a hidden layer to an output layer by adopting an importance score-based neuron pruning algorithm and a Moore-Pengos generalized inverse to obtain optimal model parameters; and finally, inputting the voltage signal subjected to data preprocessing to obtain a real-time output vector, and performing reverse normalization processing to obtain a decoupled six-dimensional force / torque vector. According to the method, precise decoupling of the six-dimensional force / torque sensor is achieved, and the inter-dimensional coupling error is remarkably reduced.

A train-track-bridge coupling response prediction method based on a sparrow optimization algorithm and a long short-term memory network comprises the steps that data such as train speed, axle load, track vibration acceleration, bridge strain and environment temperature are collected in real time through a multi-source sensor, and a multivariable time series data set is constructed after wavelet denoising and standardized preprocessing; and designing an LSTM network architecture on this basis, introducing an attention mechanism to dynamically allocate feature weights of each time step so as to enhance the ability to capture key signals in the track irregularity mutation and bridge resonance interval, and adopting a sparrow optimization algorithm to globally search an optimal combination of a hidden layer neuron number, a learning rate and a time step length in order to solve the problem of LSTM hyper-parameter optimization. Through the dynamic adaptive step length strategy balance algorithm, the early-stage global exploration and later-stage local development capabilities are balanced, the local convergence defect of a traditional grid search or genetic algorithm is avoided, the calculation efficiency can be remarkably improved, errors can be reduced, and the prediction precision can be improved.

The invention discloses a pneumatic valve intelligent control method based on deep reinforcement learning, and relates to the technical field of automatic control. The method comprises the following steps: step 1, establishing a dynamic model of the pneumatic valve according to real-time operation parameters in a multi-physical coupling behavior of the pneumatic valve; 2, defining a state space of the pneumatic valve control system according to the kinetic model; 3, inputting the state space into a pre-established deep reinforcement learning model, and outputting a real-time electromagnetic control current for the pneumatic valve; the deep reinforcement learning model is a double-deep Q network and comprises two same deep Q networks so as to relieve an over-estimation problem; each deep Q network is provided with a single hidden layer, and the weight and the bias of the hidden layer are correspondingly equal to the weight and the bias of the output layer respectively. According to the method, the problems of response lag, difficulty in parameter self-adaption and high energy consumption of a traditional control strategy under a complex working condition are solved, and the intelligent level of a control system is remarkably improved.

The invention provides an industrial process fault detection method based on a space-time causal diagram autoencoder, and the method comprises the steps: 1, carrying out the data preprocessing of the space-time process data of all process variables collected in the operation process of a target industrial process for the target industrial process; step 2, establishing a causal graph space-time auto-encoder CGSTAE; 3, executing a three-step causal graph structure learning algorithm to realize training of a causal graph space-time auto-encoder CGSTAE, wherein the training comprises three steps of pre-training, causal extraction and fine tuning; and step 4, obtaining a fault detection result based on hidden layer features of the causal graph space-time auto-encoder CGSTAE and residual data output by reconstruction. According to the method, effective process monitoring and fault detection are realized by constructing two statistical magnitudes in a feature space and a residual space. Compared with other methods, the fault detection method provided by the invention can improve the reliability and interpretability of industrial process monitoring.

The invention belongs to the technical field of X-ray welding seam defect recognition, particularly relates to an X-ray welding seam defect recognition method, system and equipment based on an AGP convolutional neural network, and aims to solve the problems that an existing convolutional neural network is low in convergence speed, prone to falling into local optimum and the like, and the recognition rate of X-ray welding seam defects is low. The method comprises the following steps: acquiring an image to be subjected to defect identification as an input image; preprocessing the input image to obtain a preprocessed image; inputting the preprocessed image into a trained AGP convolutional neural network to obtain a weld defect recognition result; the AGP convolutional neural network is constructed based on an input layer, a batch normalization layer, a hidden layer, a cavity layering attention module and a classification output layer. According to the invention, the recognition rate of the X-ray weld defect is improved.

The embodiment of the invention provides a tensor determination method and device based on expert parallelism and a storage medium, and relates to the technical field of artificial intelligence chips, and the method comprises the steps: dividing an input tensor into N sub-tensors in the hidden layer dimension of the input tensor; and distributing the N sub-tensors to expert sub-networks deployed by the N computing units for parallel computing. As each expert sub-network calculates the allocated sub-tensor through the corresponding first weight parameter, the first weight parameter of the expert sub-network and the allocated sub-tensor need to be matched in shape; on this basis, for the target dimension associated with the hidden layer dimension of the sub-tensor in the first weight parameter, the length of the target dimension is set as the length of the hidden layer dimension of the sub-tensor, so that the length of the first weight parameter in the target dimension is reduced, namely, the video memory space required to be occupied by the weight parameter of the expert sub-network is reduced; therefore, the storage pressure of the video memory is reduced.

The invention discloses a distributed photovoltaic power prediction method and system, belongs to the technical field of renewable energy prediction, and solves the prediction precision and efficiency bottlenecks of a traditional model under complex meteorological conditions through fusion of deep feature learning and an adaptive optimization mechanism. Firstly, a time sequence sample set is constructed based on a sliding window mechanism, and non-stationary fluctuation characteristics of a power sequence are dynamically captured; eliminating the dimensional difference between the input features and the tags through minimum-maximum normalization; constructing a four-level restricted Boltzmann machine stacking structure, and extracting time-space coupling characteristics of power data by layer-by-layer unsupervised pre-training; and designing an improved extreme learning machine dynamic analysis architecture, optimizing the number of neurons in a hidden layer in combination with grid search, and realizing global optimal balance between model complexity and prediction precision. According to the method, an effective dynamic mode and noise interference are distinguished by using hierarchical feature abstraction capability of the RBM, prediction robustness in strong fluctuation scenes such as cloudy and rainy scenes is remarkably improved through analytical solution and parameter adaptive adjustment of the ELM, and efficient technical support is provided for intelligent scheduling and energy storage optimization in a high-proportion photovoltaic grid-connected background.

The invention relates to the technical field of metallurgical industry simulation and intelligent control, and discloses an intelligent metallurgical process virtual simulation method and system based on digital twinning. The method comprises the following steps: acquiring multi-modal high-dimensional data in a metallurgical process and reducing dimensions to obtain a feature vector set of a hidden layer space; performing disturbance injection simulation by using the feature vector set of the hidden layer space to obtain a multi-path state sequence with time dependence; predicting the abnormal path by using a recurrent neural network to obtain a predicted state evolution trajectory; utilizing a preset inverse mapping function to obtain multi-path state representation in the physical space; screening to obtain a risk path set; key evolution nodes are extracted from the set to be processed, and a virtual simulation scene is obtained; and performing optimization simulation on preset process adjustment parameters according to the virtual simulation scene, and determining optimized parameter configuration. The method can solve the problem that it is difficult to construct a comprehensive virtual simulation scene which truly restores the physical production rule.

The invention discloses a phase generated carrier demodulation method based on an end-to-end neural network, belongs to the technical field of optical fiber interference sensing signal processing, and is used for a phase demodulation process of an optical fiber interference signal. According to the method, a data set with a physical label is generated through simulation, a deep neural network is trained to realize direct regression prediction of the phase, and dependence on multi-stage parameters and a complex demodulation process in a traditional phase generation carrier demodulation method is avoided. By constructing a regression network model comprising an input layer, a hidden layer and an output layer, high-precision effective demodulation can be performed on interference signals under different parameter conditions. The method has strong robustness, is suitable for various optical fiber sensing application scenes, and especially has good practical value in a dynamic environment.

A decoder may generate a reconstruction of a luma block based on a prediction of the luma block and a residual of the luma block. The decoder may generate, at one or more hidden layers, a score distribution. The decoder may generate the score distribution based on the reconstruction of the luma block and one or more coding parameters of the luma block. The decoder may generate, at an output layer and based on the score distribution, a prediction of a chroma block corresponding to the luma block. The decoder may determine a reconstruction of the chroma block based on the prediction of the chroma block and a residual of the chroma block.

The invention discloses a dynamic routing parameter efficient fine tuning method and system based on LoRA-MoE, and relates to the technical field of large model fine tuning. The method comprises the following steps: firstly, constructing a heterogeneous expert architecture-based LoRA module pool based on a multi-field data set; and secondly, coding the hidden layer features of the task through a dynamic gating network, realizing continuous differentiable expert activation, and improving the balance of expert allocation by adopting temperature attenuation and entropy regularization constraint. And then, dynamically selecting and carrying out weighted fusion on a plurality of LoRA parameter increments according to task semantics in a reasoning stage, so as to realize low-cost model adaptive updating. Finally, the module pool is continuously optimized through the confusion degree and manual evaluation feedback, low-efficiency modules are automatically eliminated, and a new LoRA module is generated to maintain task coverage. According to the method, the accuracy and generalization ability of the model in a complex scene can be remarkably improved on the premise of ensuring light weight, and rapid adaptation and dynamic optimization of a large model under a low-resource condition are realized.

The invention discloses a business risk management method and device, a computer program product and electronic equipment. The method relates to the field of artificial intelligence and big data, and comprises the following steps: obtaining business data, and encoding the business data to obtain a business vector; the business vector is input into a target model to obtain a risk prediction level, the target model comprises an input layer, a hidden layer, an attention mechanism layer and an output layer, the input layer is used for extracting a state vector from the business vector, the hidden layer is used for extracting hidden features from the state vector, the attention mechanism layer is used for calculating an attention weight, and the output layer is used for outputting the attention weight; the target feature is determined through the hidden feature and the attention weight, the attention weight is determined by the similarity between the business risk index and the hidden feature and the position code of the hidden feature, and the output layer outputs a risk prediction level based on the target feature; and determining a target risk management strategy through the risk prediction level. Through the method and the device, the problem of low business risk management efficiency in related technologies is solved.

We describe an artificial neural network comprising: an input layer of input neurons, one or more hidden layers of neurons in successive layers of neurons above the input layer, and at least one further, concept-identifying layer of neurons above the hidden layers. The neural network includes an activation memory coupled to an intermediate, hidden layer of neurons between the input concept-identifying layers to store a pattern of activation of the intermediate layer. The neural network further includes a system to determine an overlap between a plurality of the stored patterns of activation and to activate in the intermediate hidden layer an overlap pattern such that the concept-identifying layer of neurons is configured to identify features of the overlap patterns. We also describe related methods, processor control code, and computing systems for the neural network. Optionally further, higher level concept-identifying layers of neurons may be included.