26 results about "Narx neural network" patented technology

The invention relates to the technical field of automobile battery detection, and discloses a lithium ion battery state-of-charge estimation method and system based on an improved NARX neural network, and the method comprises the steps: collecting dynamic parameters of a lithium ion battery during operation; performing standardization processing on the collected dynamic parameters, eliminating noise interference through filtering, and calibrating nonlinear characteristics of the battery to obtain a standardized data set; constructing a hybrid model in which the NARX neural network and the LSTM neural network are connected in series; inputting the standardized data set into a hybrid model for training to obtain a trained hybrid model; and inputting dynamic parameters acquired in real time into the trained hybrid model, and outputting a state-of-charge estimation value of the lithium ion battery. According to the lithium ion battery state-of-charge estimation method and system based on the improved NARX neural network, the NARX neural network and the LSTM neural network are combined in series, so that the stability and precision of lithium ion battery state-of-charge estimation are improved.

The invention discloses a regulation and control quota distribution method and system fusing subjective and objective multi-dimensional features. The method comprises the following steps: acquiring electrical load data, historical response behavior data and subjective response intention information of a user; firstly, a Ward system is used for clustering, users are clustered according to active power, then a primary clustering center is used as an initial clustering center of secondary clustering, FCM clustering is carried out, and a typical load curve of the users is described based on a secondary clustering method; load prediction is carried out based on an NARX neural network, a predicted load curve is compared with a typical load curve, and adjustable potential is calculated; constructing a DR feature data set; and the DR feature data set is fused with an entropy weight method and an analytic hierarchy process to obtain a combined weight, a fuzzy relation matrix is constructed, a user comprehensive score is quantified, and a comprehensive response potential score of the user is formed.

The invention discloses a cable production equipment fault diagnosis method based on machine learning. The method comprises the steps that multi-source time sequence data are collected and preprocessed to generate a standardized data set; executing fractional calculus operation to obtain a fractional response sequence and dynamic memory weight distribution; constructing long-term memory features and forming an exogenous input vector sequence; performing joint training on exogenous input and real observation to obtain a convergent improved NARX neural network model; inputting the exogenous input and historical output lagging sequence into the model, outputting a state prediction value and mapping the state prediction value into a fault risk index; generating a fractional order residual energy index and an abnormal score sequence; and judging and outputting the fault state and the degradation trend grade of the cable production equipment. According to the invention, by introducing the fractional calculus algorithm and improving the NARX neural network model, high-precision fault identification and degradation trend intelligent prediction of cable production equipment under complex working conditions are realized.

The invention relates to an underwater electromagnetic detection noise suppression method based on an NARX neural network, and the method comprises the steps: collecting first magnetic field data through a first vector magnetic sensor, and collecting second magnetic field data through a second vector magnetic sensor; training a sea wave noise prediction model based on the first magnetic field data and the second magnetic field data, and repeatedly and synchronously intercepting the first magnetic field data and the second magnetic field data in a preset time range through a sliding window, inputting the intercepted first magnetic field data and the intercepted second magnetic field data into the sea wave noise prediction model for prediction to obtain a corresponding sea wave noise prediction value; and performing differential processing on the first magnetic field data in each sliding window and the sea wave noise prediction value in the corresponding preset time range, and splicing differential results to obtain secondary field information in the first magnetic field data. According to the invention, the accuracy of noise estimation is improved, and the background interference in the signal can be better stripped.

The invention relates to the technical field of coking industry, in particular to a coke oven gas collecting pipe pressure control method based on multivariable constraint optimization, which comprises the following steps: collecting, screening and preprocessing historical operation data of a coke oven gas collecting pipe system under different operation conditions; designing a multivariable nonlinear constraint optimization control algorithm; introducing a constraint reflecting the actual operation of a coke oven system into a nonlinear constraint optimization problem, and adding penalty terms for a control input constraint and a gas collector pressure constraint into an optimization objective function; and at the beginning of each control period, acquiring key parameters of the coke oven system in real time, and performing prediction calculation on the key parameters of the coke oven system through the NARX neural network model. The method has the advantages that the generated data set can be ensured to comprehensively cover the states of the coke oven gas collecting pipe system in various actual operation scenes, so that the subsequently trained NARX neural network model can fully learn dynamic behavior modes of the system under different working conditions.

The application provides a method and system for predicting main steam parameters before steam warming of a combined cycle unit, the method comprising the following steps: obtaining historical operation data of a target power generating unit, the historical operation data comprising a plurality of observable input parameters and output parameters closely related to a main steam system; constructing an NARX neural network model, training the neural network model based on the historical operation data, and obtaining a trained main steam parameter prediction model; deploying the main steam parameter prediction model on a general computing device; obtaining initial state parameters under a current working condition before starting the power generating unit, and inputting the initial state parameters into the main steam parameter prediction model; and outputting a predicted trajectory of main steam key parameters before the high-pressure bypass system is put into operation from the main steam parameter prediction model.

The invention discloses a neural network adaptive feedforward method and system for realizing cable constant tension control, and relates to the field of industrial control, and the method comprises the steps: S1, obtaining the latest historical tension data and real-time tension data of a cable; s2, a pre-trained NARX neural network model outputs a cable tension prediction value at a future moment; s3, taking the cable tension prediction value as a feedforward compensation amount; s4, inputting the system control error into a BP neural network to obtain a self-adaptively set PID control parameter; s5, calculating to obtain a feedback control quantity by adopting the self-adaptively set PID control parameters; s6, superposing the feed-forward compensation quantity and the feedback control quantity; according to the method, future tension changes are accurately predicted through the NARX neural network and serve as feed-forward signals, advanced suppression of disturbance is achieved, PID parameters are adjusted online through the BP neural network, and a controller can automatically adapt to changes of complex working conditions such as different sea conditions and loads.

The application discloses a kind of whole vehicle fuel sulfur content calculation methods, belong to fuel sulfur content calculation technical field.The whole vehicle fuel sulfur content calculation method includes the following steps: S1: information collection;S2: training NARX neural network model;S3: report whether SCR catalyst is sulfur poisoning failure;S4: repair to the poisoning SCR catalyst;If driver uses low sulfur content oil, time is very long, possibly all does not cause catalyst poisoning, so adopt the product of sulfur content and time, to express the degree of poisoning comprehensively, using NARX neural network to calculate: the poisoning degree of engine next time, is influenced by its last time poisoning degree, the feature of NARX neural network is to use the output value of last time, as the input of next time participates in calculation, improves the accuracy of whether catalyst is poisoned detection.

An intelligent automobile trajectory tracking control method based on adaptive switching MPC belongs to the technical field of intelligent automobile control, and comprises the following steps: establishing a vehicle mechanism dynamics model; establishing a vehicle dynamics model; designing an MPC controller based on the three types of models; the road curvature, the road adhesion coefficient and the longitudinal vehicle speed serve as fuzzy input, the transverse error serves as a variable universe scaling factor, and self-adaptive switching of the three types of models is achieved through variable universe fuzzy control. According to the method, the modeling precision under the complex working condition is improved through the NARX neural network, the tracking precision and the calculation real-time performance are balanced through the self-adaptive switching mechanism, simulation verification shows that the minimum transverse error peak value can reach 0.102 m, the control time is shortened to 4.56 s, and the requirements of an automatic driving system for high precision and high real-time performance of trajectory tracking are met.

The present invention belongs to the field of piezoelectric ceramic characteristic measurement, and discloses an output prediction modeling method for the force-electric-potential dynamic hysteresis of piezoelectric ceramics. First, install the data acquisition hardware system of the piezoelectric ceramic actuator, collect the experimental data of the input voltage, output force and output displacement of the piezoelectric ceramic actuator, establish the API-NARX neural network model of the piezoelectric ceramic actuator, use the processed experimental data as the input and output of the neural network model, select the initial parameters to train the neural network model, and call the trained neural network model for application in engineering. This method takes into account factors such as the force-electric coupling and dynamic hysteresis of the piezoelectric ceramic actuator, and is more accurate and effective than the traditional voltage-displacement modeling method. It can more accurately describe the force-electric-potential hysteresis characteristics of piezoelectric ceramics, and then realize the prediction of high-precision output displacement and output force. Moreover, this method has strong adaptability and can be applied to all systems containing piezoelectric ceramic actuators.

The invention provides a dynamic identification method and system for a GFM inverter dominant power distribution system and a related device. The method comprises the following steps: step 1, establishing a phasor domain mathematical model of a GFM inverter in a phasor domain; step 2, constructing a nonlinear least square optimization model based on the phasor domain mathematical model, obtaining a parameter estimation value corresponding to the GFM inverter, substituting the parameter estimation value into the phasor domain mathematical model, and solving to obtain an optimized output phasor; step 3, optimizing a system dynamic prediction model pre-constructed based on the NARX neural network by using the optimized output phasor obtained in the step 2 to obtain an optimized system dynamic prediction model; and step 4, based on the pre-constructed equivalent model of the GFM inverter in the phasor domain, utilizing the optimized system dynamic prediction model obtained in the step 3 to dynamically predict the dominant power distribution system of the GFM inverter, and through organic combination of physical optimization and data driving, high-precision and high-generalization system dynamic representation is realized.

The embodiment of the application discloses a voice coil motor hysteresis modeling method and a related product, wherein the steps of the method comprise: determining a transfer function relationship between an output displacement and an input current, determining a phase frequency response and an amplitude frequency response relationship according to the transfer function relationship between the output displacement and the input current; and based on the phase frequency response and the amplitude frequency response relationship, fitting a voice coil motor hysteresis trajectory by using a neural network to obtain a voice coil motor hysteresis model. The scheme disclosed in the application can effectively describe the hysteresis phenomenon exhibited by the voice coil motor when the output millimeter-level displacement is output, and the description effect of the model basically remains unchanged with the change of the frequency, and the scheme has a more stable dynamic trajectory tracking effect than a pure NARX neural network model.

The invention discloses an ocean platform mooring cable tension real-time prediction method and application based on a neural network, and the method comprises the following steps: simulating the sea condition through numerical simulation, and generating a high-frequency time sequence data pair of the global displacement and tension of the top end of a mooring cable in an off-line manner, and taking the high-frequency time sequence data pair as the training basis of a model; global displacement data is converted to a local coordinate system with the top end of each cable chain as an original point to achieve motion normalization, and a feature data set which can be directly used for neural network training is constructed through downsampling and standardization processing; constructing a double-hidden-layer NARX neural network taking historical local displacement as input and current tension as output, and performing training and hyper-parameter optimization on the double-hidden-layer NARX neural network by using the generated normalized data set to obtain a prediction model; integrating the trained model into an anchor chain digital twin system, deploying the anchor chain digital twin system to an ocean platform with a motion sensor, predicting tension in real time, and performing early warning and analyzing the fatigue strength of the anchor chain according to a result.

The application discloses an improved NARX ship roll motion multi-step prediction method, which comprises the following steps: step 1, obtaining a plurality of roll angle time series data as training samples; step 2, determining a fixed step length of a multi-step prediction model; step 3, constructing an improved NARX neural network multi-step prediction model; step 4, training the improved NARX neural network multi-step prediction model through the training samples and the fixed step length, and obtaining a trained improved NARX neural network multi-step prediction model; and step 5, predicting the actually collected roll angle time through the trained improved NARX neural network multi-step prediction model, and taking any prediction value in the obtained fixed step number as a prediction result. The application can obtain better prediction effect in the case that the sensor sampling period is long and the system time lag is compensated, and can ensure that the ship equipment can be stably and safely operated.

The invention aims to provide a common rail fuel injection quantity estimation method based on a physical constraint autoregressive network, and belongs to the field of internal combustion engine fuel injection control. Physical parameter neural network training; pre-training the NARX neural network; oil injection stage judgment and physical output calculation; carrying out weighted fusion output; and carrying out integral training on the PI-NARX network. According to the method, time delay preprocessing is innovatively carried out on the training data labels, the time alignment problem of the system is improved, and the real-time estimation accuracy is improved. And a learnable fusion weight is introduced to realize adaptive optimal estimation. The self-adaptive weighted fusion mechanism ensures that the system can always give the most credible estimation result at present.

This invention relates to the field of unmanned aerial vehicle (UAV) swarm control technology, and more particularly to a method, system, medium, and software product for simulating and modeling phase trajectory of a UAV network system. The method includes system and task analysis, construction of interaction rule matrices, state analysis and phase vector space construction, approximate calibration of the stability domain, determination of the ultimate impulse, simulation of disturbance impulse injection, and data segmentation and transition zone marking, realizing a closed-loop process from modeling to disturbance assessment. Trajectory prediction is performed using a NARX neural network, combined with Gaussian white noise augmentation and multi-round simulation to achieve approximate calibration of the stability domain and determination of the ultimate impulse, and automatically outputs a structured trajectory dataset. This invention improves modeling accuracy, simulation efficiency, and system security.

The invention provides a road surface unevenness estimation method and device, a terminal and a medium, and the method comprises the steps: inputting an obtained random filtering white noise road surface into a vehicle quarter nonlinear suspension model to generate a dynamic response amount, and obtaining a sprung acceleration and an unsprung acceleration; inputting the sprung acceleration and the unsprung acceleration into an NARX neural network to predict the road surface unevenness to obtain a first estimated road surface unevenness value; obtaining a second estimated road surface unevenness value according to the sprung acceleration, the unsprung acceleration, the first estimated road surface unevenness value and an international road surface unevenness index; and fusing the first estimated road surface unevenness value and the second estimated road surface unevenness value by adopting a fusion algorithm to obtain a road surface unevenness estimated value. Through the fusion algorithm, the problem that the pavement unevenness estimation precision is reduced due to nonlinearity and uncertainty of the model can be effectively relieved, and the pavement unevenness estimation efficiency is improved.

The human lower limb motion intention recognition and exoskeleton robot angle prediction control method comprises the following steps: 1) collecting human surface electromyogram signals, and converting joint angle values into joint signals for recording; 2) pre-processing the collected signals; 3) carrying out noise reduction filtering on the pre-processed signals; 4) intercepting the noise-reduced electromyogram signals to obtain the action segments in the electromyogram signals, and extracting the characteristic values of the action segments; 5) converting the action segments into frequency domain signals, carrying out frequency domain analysis on the action segments, and obtaining the median frequency of the action segments; 6) taking the extracted characteristic values as the index for measuring the accuracy, constructing a regression model by using a BP neural network, and recognizing the lower limb motion intention through the regression model; and 7) constructing an NARX neural network to predict the joint angle. The present application enables the exoskeleton robot to simultaneously realize the estimation and prediction of the human lower limb motion intention and the lower limb joint angle.

The invention discloses a lightning arrester leakage current monitoring method and system, electronic equipment and a storage medium, and relates to the technical field of lightning arrester leakage current measurement, and the method comprises the steps: measuring the standard true value of the leakage current when a lightning arrester operates stably under the condition of eliminating environmental data interference; acquiring multiple groups of historical environment data of the lightning arrester and historical leakage current measured under the historical environment data, and calculating correlation between each group of historical environment data and the corresponding historical leakage current; taking the historical environment data of which the correlation is higher than a preset threshold value and the corresponding historical leakage current as input data, taking the standard true value of the leakage current as output data, and performing model training by adopting an NARX neural network to obtain a leakage current prediction model; the current environment data of the lightning arrester are input into the leakage current prediction model, the environment normalization reference value of the current leakage current is obtained, the influence of environment factors on leakage current monitoring is eliminated, and the accuracy of leakage current monitoring is improved.

The invention discloses a mechanism-data hybrid driven natural gas pipeline network transient simulation method and device, and relates to the technical field of natural gas pipeline networks, and the method comprises the steps: collecting sensor data of a natural gas pipeline network through an SCADA system, and carrying out the preprocessing through a VMD method, so as to obtain boundary conditions and correction data; acquiring natural gas pipeline network parameters and a topological structure, and inputting the natural gas pipeline network parameters and the topological structure together with the boundary conditions into the mechanism model for transient simulation; establishing a Kriging agent model based on the transient simulation result, and performing Kriging-differential evolution algorithm parameter identification to update the natural gas pipeline network parameters; taking an NARX neural network as a data driving model, inputting the boundary condition into the data driving model to obtain an error estimation value, and performing error compensation on a transient simulation result; according to the method, on the basis of a mechanism model, SCADA collection values are processed, a data driving model is constructed to compensate transient simulation errors, the simulation errors are reduced from multiple angles, and the simulation accuracy is improved while the interpretability is guaranteed.

The invention relates to the technical field of unmanned aerial vehicle cluster control, in particular to an unmanned aerial vehicle network system phase point trajectory simulation modeling method and system, a medium and a software product. The method comprises the steps of system analysis and task analysis, interaction rule matrix construction, state analysis and phase vector space construction, stability domain approximate calibration, limit impulse measurement, disturbance impulse injection simulation and data segmentation and transition area marking, and the closed-loop process from modeling to interference evaluation is achieved. Trajectory prediction is carried out through an NARX neural network, Gaussian white noise expansion and multi-round simulation are combined, stability domain approximate calibration and limit impulse judgment are achieved, and a structured trajectory data set is automatically output. According to the method, the modeling precision, the simulation efficiency and the system safety are improved.

The invention provides a carbon emission factor intelligent prediction method and system based on an NARX neural network, and belongs to the field of power system carbon emission prediction. According to the method, an NARX neural network model with historical node carbon emission factors as endogenous autoregression input and historical node load power as exogenous input is adopted, and prediction is carried out in combination with power grid real-time operation data and recent historical data, so that time sequence characteristics of power system data and internal relations among the factors can be effectively mined; the defects that a traditional physical model is difficult to track real-time changes of electric carbon factors and cannot fully consider the influence of market factors and an existing neural network model is weak in generalization ability and insufficient in utilization of time sequence information are overcome, and meanwhile excessive dependence on complete power grid operation parameters is reduced. Therefore, the accuracy, timeliness and adaptability of power grid node electric carbon factor prediction are improved.

The application discloses a kind of based on data driving power battery fault early warning and safety risk assessment method, comprising: data acquisition;The data collected are handled;The data after processing are divided, and the correlation analysis of power battery parameter is carried out, and data set is made;Data set is divided into training set and test set, so that NARX neural network structure model is trained on training set, so that NARX neural network structure model is tested on test set, according to the performance of test set, the hyperparameter adjustment of NARX neural network structure model is carried out, and voltage fault early warning model is established;Temperature fault early warning model is established according to probe temperature;According to voltage fault early warning model and temperature fault early warning model, establish power battery safety risk assessment system.The power battery fault early warning and safety risk assessment method disclosed in the application has the advantages of small amount of calculation, fast calculation speed, early fault early warning, can be more accurately early warning and evaluation to power battery and the like.

The invention discloses an aquaculture water quality prediction method based on an IMSSA-NARX (International Mass Spectroscopy Amplification-Networking ARX Collecting aquaculture water quality parameters, establishing a data set, and completing preprocessing; multi-bit control parameters are introduced into a chaotic equation of an original Tent, so that chaotic mapping of the improved Tent is realized; through combination of a dogvessel alveolar group algorithm and improved Tent chaotic mapping, an improved dogvessel alveolar algorithm IMSSA is realized so as to improve global exploration and local development capability of the dogvessel alveolar algorithm. Training a nonlinear autoregressive neural network NARX model by using an IMSSA algorithm to optimize the weight and bias of the NARX neural network, so as to establish an aquaculture water quality prediction model based on the IMSSA-NARX; and completing an aquaculture water quality prediction experiment based on IMS-NARX, and comparing the method with a conventional method. Compared with the prior art, the model provided by the invention has the highest prediction accuracy, and the IMSSA-NARX network model has good performance in the aspect of aquaculture water quality condition prediction.

The invention discloses a self-adaptive control system and method for plasma ozone products in a complex environment, and belongs to the technical field of ozone generator control. According to the method, multi-dimensional time sequence data of ozone concentration, temperature, humidity and pulse excitation frequency in changing environmental conditions are collected and input into an NARX neural network model with a first-order lagging output characteristic, and the dynamic change trend of the ozone concentration is predicted. And embedding the prediction model into a model prediction control framework, solving an optimal control sequence in real time in a rolling prediction time domain by using a gradient descent algorithm, and adaptively adjusting the excitation frequency. According to the method, observable disturbance is incorporated into a prediction model, the method has the capability of quickly responding to multivariable coupling disturbance, overshoot and steady-state errors caused by environment temperature and humidity changes in ozone concentration control can be inhibited, and the robustness and precision of a control system are remarkably improved. The method is suitable for scenes requiring high-precision ozone control under variable working conditions, and can be popularized to the field of intelligent control of other plasma products.

The present application relates to nuclear power plant fault diagnosis prediction technical field, especially a kind of long-period vibration prediction and regulating system based on NARX modeling, the system includes long-period vibration instability diagnosis model, long-period vibration instability diagnosis model is used to identify the long-period vibration of vibration displacement, determine whether long-period vibration occurs in steam turbine;Vibration displacement prediction NARX neural network model extracts steam turbine bearing pad vibration displacement and operating parameter, and vibration displacement NARX model is obtained by training;Vibration displacement NARX neural network adjustment prediction model is used to determine the best oil temperature and safe oil temperature range of steam turbine not long-period vibration;Oil temperature regulation hysteresis NARX neural network model is driven by the difference between the best oil temperature and current oil temperature, and the output is adjusted oil cooling water flow value.The system realizes accurate and rapid regulation of oil temperature through oil temperature regulation hysteresis NARX neural network model, and makes up for the shortcomings of poor control accuracy caused by hysteresis.