56results about How to "Reduce model error" patented technology

The invention discloses a method for building a covered wire inner full-state turbofan engine vehicle-mounted real-time model. The method comprises the steps of firstly building a vehicle-mounted real-time dynamic model, indicating an applied force-included and above-idle-speed state, of a turbofan engine, then, building a starting simplified model of the turbofan engine, finally, utilizing a Kalman filter for estimating gas circuit performance characteristic parameters of the turbofan engine, and building a vehicle-mounted self-adaptation real-time model indicating the applied force-included and above-idle-speed state. The method can meet the requirement of a covered wire full-state general model of the turbofan engine for precision and vehicle-mounted real-time performance, the strong adaptive capacity for model mismatch caused by the engine individual difference and performance degradation is obtained, model foundation is provided for experimental verification of a modern control theory and a fault-tolerant control method, and the method plays a positive and promotion role in shortening the preparation time, reducing test risk and cost.

A gas sensor array concentration detection method based on fuzzy division and model integration belongs to the field of a gas sensor array signal processing technology. According to the method, time division of baseline drift data is carried out by a fuzzy clustering method, and a source dataset is divided into multiple sub-datasets with different drift degrees; then, regression models of different training datasets are established to obtain several sub-regression models; an optimal weight set of each sub-regression model is obtained within the training dataset, and center of clustering and optimal weight undergo fitting to obtain an optimal weight fitting function; and during the test phase, fitting weight is calculated on the basis of the optimal weight fitting function and clustering center time, and the sub-regression models integrate forecasted results of data to be tested so as to obtain the final gas concentration value. By the method, a pattern recognition model can be changed adaptively, changes of drifting can be traced, the influence of drifting on concentration detection performance is effectively reduced, and long-term accuracy of concentration measurement is guaranteed.

The present invention discloses a method for online identifying a measurement deviation fault for a traction substation instrument transformer. The method comprises the steps of: 1) modeling data selection and preprocessing: selecting data within I contiguous time points from instrument transformer valid measurement history data when a traction substrate is idle and all instrument transformer are operating properly, to form a matrix, and conducting standardized processing therefor; 2) establishing a primary element analysis model; 3) squared prediction error (SPE) calculation of the real-time sampling set of the instrument transformer; 4)online detection of the instrument transformer measurement deviation fault; (5) calculation of the SPE contribution values of the instrument transformers; and 6) identification of the instrument transformer subject to the measurement deviation fault. With the method according to the present invention, an instrument transformer subject to a measurement deviation fault can be online identified only by processing the measured valid data of all the instrument transformers in the traction substation, with no need of additionally arranging a waveform recording device. Therefore, the requirement on the hardware is low, the implementation cost is low, and the method is simple for promotion and application.

A multi-step iterative process for simulating complex agricultural situations where limited sets of data are available for such problems first predicts an outcome for each situation in a particular dataset, using initial assumptions of an applied primary model. The process then uses the errors across these situations to identify where opportunities exist among relevant predictive variables for the model to make changes to a response to such predictor variables to reduce the errors when averaged across all situations. The process then develops a correction model to identify adjustments based on combinations of the predictive variables, and applies the adjustments to the primary model to induce an altered outcome.

The invention discloses a MIMU gyroscope random drift forecasting method based on an ARMA and BPNN combination model. The method mainly includes the steps that 1, random drift data of a single-axle micro gyroscope is collected in the static state, the stationarity of an original data sequence of a gyroscope is judged by observing graphic characteristics of an autocorrelation parameter ACF and a partial correlation parameter PACF, an ADF unit root is checked, and stationary data is obtained after a trend term of the sequence is removed through differences; 2, the stationary sequence of the gyroscope is subjected to model order determination with the AIC minimum rule method, and an ARMA model of a gyroscope drift stationary sequence is established; 3, a random drift sequence of the gyroscope is modeled through the combination model, a training set and a test set are selected from error data of the ARMA model in the step 2, and a BP neural network forecasting model is established, and the structure of a BP neural network is set; 4, sample data of the established BP neural network is trained, and the forecasting result of gyroscope random drift is preserved.

The invention discloses a turbo-shaft engine self-adaptive component-level simulation model construction method. The method comprises the following steps: step A, acquiring parameters of various worksections of a turbo-fan engine according to various component aerothermodynamics features of the turbo-shaft engine, and establishing a nonlinear component-level dynamic universal model about the turbo-shaft engine over-idling state; step B, designing a volume tracking filter, estimating an unmeasured performance feature parameter of a turbo-shaft engine gas circuit component in the obtained non-linear component-level dynamic general model, wherein the gas circuit component comprises a gas compressor, a gas turbine, and a power turbine; and step C, automatically correcting the flow and efficiency feature graph of each gas circuit component by using the estimated performance feature parameter, and applying the adjusted gas circuit component feature parameter to computing the component aerothermodynamics parameter to obtain modified model output data, thereby establishing the self-adaptive component-level simulation model of over-idling state. The model mismatch problem caused by turbo-shaft engine individual difference and performance degradation is solved through the method disclosed by the invention.

The invention discloses a method for testing and identifying dynamic characteristic coefficients of a sliding bearing. The method comprises the following steps of: (1) fixing a bearing to be identified on a test bed; (2) arranging a key phase mark which serves as a reference for subsequent rotational speed analysis and key-phase synchronous acquisition analysis on a shaft segment of a rotation shaft, wherein the shaft segment of the rotation shaft is the part, which is positioned between the bearing and a power input end of a rotation shaft, of the rotation shaft; (3) applying unbalanced force which serves exciting force to the shaft segment of the rotation shaft; (4) raising the speed of the rotation shaft up to the test speed, and respectively measuring a dynamic strain signal of the shaft segment of the rotation shaft and the vibration displacement of the rotation shaft relative to the bearing; (5) computing the dynamic load which is born by the bearing according to the dynamic strain signal of the shaft segment of the rotation shaft; (6) substituting the dynamic load of the bearing and the vibration displacement of the rotation shaft into a computational formula corresponding to the lubrication theory of the bearing to obtain two sets of solving equations; (7) changing the unbalanced force to obtain another two sets of solving equations; and (8) solving the four sets of solving equations to obtain the dynamic characteristic coefficients of the bearing. By the method, large-sized bearings can be tested, large-sized equipment such as a vibration exciter is not required, and the identification result is accurate.

The invention discloses a fitting method of a complex water level process, namely a layered transformation sieving and fitting method which organically integrates polynomial regression, stepwise regression, parameter mountain estimation, and the like and introduces progression transformation to systematically form a new method. Compared with the similar method, the fitting method has the core difference that the strong coupling function among the normal weak- influence factors is considered in the complex water level process; the fitting model error is further reduced by synthetically adoptingvarious methods, and the necessary progression transformation is introduced. The fitting method organically integrates the advantages of various theories and methods, is convenient to use and has thecommon applicability under similar complex fitting problems.

The invention relates to a BSL 3D DIC reconstruction method using a pericentric lens, and belongs to the fields of photomechanics and 3D DIC related technologies. The method comprises that an image before deformation of a sample, an image after deformation of the sample, an edge identification image and scaling board images are collected, and internal and external parameters of a camera are obtained via scaling; a reference world coordinate system in parallel with the back surface of a biprism is established, the transformation relation between the reference world coordinate system and a camera coordinate system is determined according to the external parameters; spatial positions of edges of the biprism lens and an original point mark are determined, and a space coordinate system is established; reconstruction operation and trial are carried out on the space coordinate of an angular point of a scaling board to determine the position of the space coordinate system; and according to paired pixel coordinates in the sample image, reconstruction operation is carried out on morphology before deformation of the sample and morphology after deformation of the sample to determine displacement generated by deformation of the tested sample. The method greatly reduces model error of the reconstruction method, and is low in operand, convenient to program and easy to realize automatic processing of large batch of data.

The invention relates to a multi-target modeling method for a complex industrial process, which comprises the following steps of: acquiring input and output sampling data of a modeled process; determining a first target and a second target of a modeling problem; randomly generating an initial population, wherein each individual in the population expresses a first component part parameter of a T-S fuzzy recursive neural network by using an integer string consisting of four integers corresponding to a DNA basic group and the initial population is used as the current population; calculating a network second component part parameter corresponding to the network first component part represented by each individual by using a recursive least square method; calculating a fitness degree of each individual of the current population; executing selection, crossing and variation operations on the individuals in the current population to obtain a new generation population; and selecting a final population and taking the T-S fuzzy recursive neural network corresponding to any one of the individuals of the final population as a model of the modeled process. Compared with the traditional genetic algorithm, the modeling method provided by the invention can effectively improve the modeling precision.

The invention relates to an online modeling method of a flying robot dynamics model. The method comprises steps: a flying robot dynamics reference model is built in a flying robot dynamics module simulation computer; the flying robot flies and flying state information is sent in real time to the flying robot dynamics module simulation computer; and the flying robot dynamics module simulation computer updates in real time the flying state information contained in the dynamics reference model, the value of modeling parameters inside the dynamics reference model is obtained according to modeling noise, the value of the modeling parameter is in real time used in the flying robot dynamics reference model, and online modeling of the flying robot dynamics model is realized. According to the online modeling method of a flying robot dynamics model, true dynamics of the flying robot can be reflected in real time, the method is convenient and simple, computer programming and realization are facilitated, and verification of control algorithm on a simulation platform is facilitated.

The invention discloses a bearing load identification method based on strain signals of three cross sections of a rotating shaft. According to the invention, by testing the strain signals at the three cross sections at two ends of a bearing, the bearing load is identified. The invention has the advantages as follows: (1), compared with the traditional strain method, the method provided by the invention adopts a local modeling method, so that the problems of strain measurement error accumulation, transmission and divergence are avoided; and compared with the traditional integral modeling method, the bearing load identification method has the advantages that the structure of the shaft section nearby the bearing is simple, a mathematical model can be built up more accurately, and the modeling error is less; (2), the testing work load is small; strain testing points can be arranged nearby the bearing, and point distribution at the middle part of the rotating shaft, which requires opening the cylinder, is not required; and (3), the bearing load can be tested under a low-speed dynamic state, so that the actual load condition can be reflected more truly.

The invention discloses a design method for scheduling an unmanned aerial vehicle based on a robust servo gain, and belongs to the technical field of robust control of an unmanned aerial vehicle. Thedesign method comprises the following steps of S1, using an improved model linear processing method based on a function substitution method, improving the function substitution method for building anLPV system model, and building a longitudinal LPV system model of the unmanned aerial vehicle with he variable structure; S2, using a variable gain control method based on the structure matrix, introducing an additional state and a structure matrix, and simplifying the structure of the controller; and S3, acquiring a preliminary expression of the controller structure by using the variable gain control method based on the structure matrix in the step S2, and acquiring a final structure expression by simplifying the preliminary expression. By using the controller designed according to the methodprovided by the invention, the control on the linear variable parameter system is achieved, the defects of the high order number and the high-frequency band controller are overcome, the extremely high practical value is achieved, and the design method can be applied and popularized in the field of time-varying systems with uncertainty disturbance.

The invention discloses a design method of the resonance frequency of a circular microstrip antenna. The design method comprises the following steps: establishing mapping relationships between three relevant parameters, namely patch radius, dielectric substrate thickness and relative dielectric constant of the circular microstrip antenna, and the actually-measured resonance frequency by using a particle swarm neural network; by using a GPU technology, and in the unified computing device-framework programming environment, performing parallel accelerating computation on the training process of the particle swarm neural network, wherein the well-trained particle swarm neural network can be used for predicting the resonance frequencies of other circular microstrip antennae. By the design method, the defect that the computing time of the particle swarm neural network at a CPU end is long can be overcome, and the circular microstrip antenna resonance frequency modeling speed is increased and the circular microstrip antenna resonance frequency modeling accuracy can be improved.

The invention discloses a method for adaptive control over optical fiber winding tension so as to improve a tracking angle velocity of a fiber supply shaft and control precision of optical fiber tension in the optical fiber winding process. A tension control system dynamic self-adapting model based on dynamic compensation on a winding radius is established; first a motion track of a fiber collecting shaft is planned according to an S-shaped acceleration and deceleration curve of five stages; an angle velocity track of the fiber supply shaft is dynamically planned according to the established tension dynamic self-adapting model, and the planned fiber supply shaft angle velocity is compensated according to tension deviation signals fed back by a tension sensor; and finally a track subdivision technology is adopted to achieve smooth transition of an angle velocity track in the acceleration and deceleration process of the fiber supply shaft. According to the method, the tension control precision is high, the overshoot and tension wave range are small, and the method is easy to implement and suitable for optical fiber winding machines and winding devices of various types and has high application values.

Through the present invention, an error between projection and back projection in CT image generation is reduced. The CT image generation device according to the present invention is provided with: an X-ray scanner provided with an X-ray source and a detector; a storage module including a detector data block, a weighting data block, and a parameter data block; and a processor module including a main processor, a weighting calculation unit, a weighting vector multiplication unit, and a separable footprint computation unit. A scanner stores scanned data as detector data in the detector data block, the weighting calculation unit acquires a geometry parameter from the parameter data block and calculates a weighting vector, and the main processor controls a projection / back projection angle loop using the geometry parameter. The weighting vector multiplication unit multiplies the detector data by a weighting vector and acquires a corrected detector value, and the separable footprint computation unit executes a separable footprint projection / back projection algorithm using the corrected detector value.

The invention provides a stability control method for unfolding of a three-dimensional space tether system operating in a low earth elliptical orbit in an initial stage. A tether is released to a desired length and the swing of the tether is restrained only by adjusting the limited tether tension without relying on other thrusters. The method comprises the following steps that S1, a two-body spacetether system dynamic model considering tether mass is established; S2, under the conditions such as disturbance, uncertainty, underactuation, restricted tether swing angle and tether tension, basedon a nonlinear model predictive control (NMPC) algorithm, a pseudo-spectral algorithm is used for discretizing an original nonlinear model, and an open loop optimal problem in predictive control is transformed into a nonlinear programming problem for solving; and S3, according to the solution of the nonlinear programming problem, the control quantity of the next control period is determined to form the closed-loop control. Finally, the three-dimensional space tether system operating in the low earth elliptical orbit is stably released and unfolded in the initial stage.

The invention belongs to the technical field of dynamics modeling, and relates to an H-shaped motion platform modeling method based on Lagrangian dynamics. The method is based on an H-type motion platform coupling equivalent model, a Lagrangian dynamics and functional relationship is utilized to establish a coupling equivalent dynamics equation capable of obtaining three degrees of freedom, and the purposes of reducing modeling errors and conveniently designing a controller are achieved. According to the method, the beam deflection caused by desynchrony of the parallel double linear motors isfully considered, the influence of load movement on the beam is considered, the precision of the model is improved, and the problem that the control precision cannot be improved due to a modeling error of a traditional H-shaped platform non-coupling model can be effectively solved.

The invention discloses a surplus electric quantity estimation method for a power battery. A current detector and a digital-analog converter enable collected voltage and current data to pass through acollection card; the data are transmitted to a surplus electric quantity estimation module through a low pass filter; a fractional order model is built, so as to improve the accuracy of estimating terminal voltage; a fractional order model parameter and a nonlinear function on a surplus electric quantity of open circuit voltage are identified by using a current and voltage response of a battery pack; an adaptive extended kalman filter-based surplus electric quantity estimation strategy is provided for a fractional order battery model, so as to solve the problem that a noise variance of a system is unknown and improve the surplus electric quantity estimation accuracy. The model built by the method disclosed by the invention can effectively describe charging and discharging physical characteristics of the power battery and estimate the surplus electric quantity in real time, and is high in convergence, high in estimation accuracy and applicable to the surplus electric quantity estimation of the power battery of an electric automobile.

The invention discloses a partition interpolation processing method and device for lake terrain simulation. The method comprises the following steps: firstly, extracting known elevation point data of a lake bottom and lake boundary data; secondly, processing the variance of the fluctuation degree of the elevation sampling points by adopting K-means clustering, carrying out regional division on the lakebed according to a clustering result, and respectively endowing power values for the elevation variation degrees of different regions; then determining the range of the power value of each region and the discrete step length of the power value, constructing an orthogonal test table to carry out a combination test of the power value of each region, and measuring the advantages and disadvantages of the power value combination by taking a lake elevation-storage capacity curve as a target; and finally, according to the optimal region power value combination, determining the number and coordinates of to-be-interpolated points at the lakebed by utilizing grid division, and achieving regional interpolation of an inverse distance weighted interpolation method by referring to the idea of a K-nearest neighbor algorithm. The power value of each region is analyzed and determined by using objective measurement data, the regional terrain is accurately interpolated, and the method and device are easy to popularize and use in lake terrain simulation.

The invention relates to the field of mobile communication technologies, in particular to a method of carrying out channel estimation through combining multiple OFDM (Orthogonal frequency Division Multiplexing) symbols. The method comprises the following steps of establishing a frequency domain receiving vector expression, utilizing MCE-BEM (Modified Complex Exponential-Basis Expansion Model) to carry out time domain modelling and deducing to obtain a frequency domain receiving expression, deducting a pilot frequency observation equation of each of M OFDM symbols, and utilizing an LS (Least Squares) rule to obtain a belt estimation parameter and a simultaneous equation so as to determine a time domain channel matrix. According to the method provided by the invention, M OFDM symbols form one symbol block to carry out channel estimation, firstly, the time domain modelling is carried out to obtain an expression of a time domain channel to complete deduction of the pilot frequency observation equation of each of M OFDM symbols, secondly, the belt estimation parameter is estimated by adopting an LS estimation manner, and lastly, the time domain channel matrix is obtained to complete estimation. Multiple symbols only carry out approximation of dimensionality of a base model at one time after rounding up to an integer, so that the modelling error is smaller, the number of parameters to be estimated is reduced, and the estimation performance is improved under the same pilot frequency number.

The invention discloses a method for transformer substation site selection by using oblique photography modeling. The method comprises the following steps: A, determining the flight height and the flight path of an aviation oblique photography device according to the geomorphological characteristics and the site selection area of a site selection region, and performing oblique photography by the aviation oblique photography device according to the set flight height and the flight path; B, carrying out noise reduction processing on the shot image; C, combining the images subjected to noise reduction processing to obtain a three-dimensional model; D, performing secondary correction on the three-dimensional model; and E, performing transformer substation site selection according to the three-dimensional model. For the method for transformer substation site selection by using oblique photography modeling, the defects in the prior art can be overcome, and the accuracy of modeling by using oblique photography is improved.

The invention provides a frame structure model correction method based on a simplified model. The method aims to achieve equal interlayer shear force of a simplified model and a frame structure. A frame structure novel simplified model capable of considering the influence of the beam-column joint corner is established. The invention provides an identification method for identifying simplified model parameters by adopting a monitored frame structure floor horizontal acceleration response, the simplified model parameters are converted into a state space, and a structure dynamic response prediction model of the frame structure under the action of dynamic loads such as earthquakes and wind is established based on the simplified model. The model can accurately predict the real response of the frame structure under the load effect. The method solves the problem that an accurate prediction frame structure dynamic response model is difficult to establish in a traditional frame structure modelcorrection method adopting a shear model.

The invention discloses an improved online modeling method for switched reluctance motor backward torque, and belongs to the technical field of switched reluctance motor modeling. The method comprises the following steps of: establishing a forward torque model and a backward torque model offline based on an ANFIS algorithm; applying the backward torque model to an SRM system for online current estimation; applying the forward torque model to the SRM system for online torque estimation; using the estimated current of the backward torque model as the input for the forward torque, comparing the output torque of the forward torque model with an expected value to obtain a torque estimation error, and online adjusting the parameters of the backward torque model. The method improves the precision of the established nonlinear backward torque model so as to gradually reduce the estimation error; improves the precision of the established model; avoids the complexity and the high cost caused by a current sensor; and greatly improves the control performance of the model-based switched reluctance motor.

The invention relates to a BSL 3D DIC reconstruction method using a pericentric lens, and belongs to the fields of photomechanics and 3D DIC related technologies. The method comprises that an image before deformation of a sample, an image after deformation of the sample, an edge identification image and scaling board images are collected, and internal and external parameters of a camera are obtained via scaling; a reference world coordinate system in parallel with the back surface of a biprism is established, the transformation relation between the reference world coordinate system and a camera coordinate system is determined according to the external parameters; spatial positions of edges of the biprism lens and an original point mark are determined, and a space coordinate system is established; reconstruction operation and trial are carried out on the space coordinate of an angular point of a scaling board to determine the position of the space coordinate system; and according to paired pixel coordinates in the sample image, reconstruction operation is carried out on morphology before deformation of the sample and morphology after deformation of the sample to determine displacement generated by deformation of the tested sample. The method greatly reduces model error of the reconstruction method, and is low in operand, convenient to program and easy to realize automatic processing of large batch of data.

The invention relates to an automated guided vehicle control method and system. The method comprises the steps of obtaining the current speed difference of a to-be-controlled automatic guided vehicle;obtaining a magnetic bias value identification model; determining a magnetic bias value corresponding to the current speed difference according to the current speed difference by utilizing the magnetic bias value identification model; adopting a PID controller to determine the speed difference of the automatic guided vehicle to be controlled at the next moment according to the magnetic bias valueso as to complete control over the automatic guided vehicle to be controlled, wherein the PID controller is a PID controller after parameter searching based on a hybrid particle swarm optimization algorithm. According to the automatic guided vehicle control method and system provided by the invention, the magnetic bias value is determined by adopting the magnetic bias value identification model,so that the system modeling error can be reduced, and the model accuracy is improved. The PID controller subjected to parameter searching through the hybrid particle swarm optimization algorithm is adopted for control, so that the control efficiency, the control precision and the robustness are improved.

The invention discloses a grid fault diagnosis method based on a fuzzy integral multi-source information fusion theory. The method comprises: step one, electrical quantity and switching value fault information is obtained; step two, pretreatment is carried out on the fault information obtained at the step one, a wavelet analysis is carried out on the electrical quantity to obtain a wavelet singularity xj1 and a wavelet fault degree xj2; a Bayesian network diagnosis is carried out on the switching value and a fault degree xj3 of the switching value is obtained based on a Bayesian theory; and step three, on the basis of a fuzzy integral algorithm theory, fusion of the wavelet singularity xj1, the wavelet fault degree xj2, and the fault degree xj3 of the switching value that are obtained at the step two is carried out, thereby obtaining a final decision-making result. Therefore, occurrence of a model error that is caused by only using a switching value or an electrical quantity to diagnose a grid fault can be reduced.

The invention discloses a method for adaptive control over optical fiber winding tension so as to improve a tracking angle velocity of a fiber supply shaft and control precision of optical fiber tension in the optical fiber winding process. A tension control system dynamic self-adapting model based on dynamic compensation on a winding radius is established; first a motion track of a fiber collecting shaft is planned according to an S-shaped acceleration and deceleration curve of five stages; an angle velocity track of the fiber supply shaft is dynamically planned according to the established tension dynamic self-adapting model, and the planned fiber supply shaft angle velocity is compensated according to tension deviation signals fed back by a tension sensor; and finally a track subdivision technology is adopted to achieve smooth transition of an angle velocity track in the acceleration and deceleration process of the fiber supply shaft. According to the method, the tension control precision is high, the overshoot and tension wave range are small, and the method is easy to implement and suitable for optical fiber winding machines and winding devices of various types and has high application values.

The invention discloses an equivalent circuit model of a super capacitor, which relates to the technical field of battery management systems and comprises a super capacitor, an internal resistance switch control module, a nonlinear equivalent circuit model module, a terminal voltage module and a hysteresis voltage compensation module which are sequentially connected in series. The invention discloses a method for constructing an equivalent circuit model of a super capacitor. The method comprises the steps of S100, charging and discharging testing, S200, parameter identification and S300, actual working condition parameter verification. According to the method, the charging and discharging characteristics of the super capacitor can be accurately fitted, and the problem that the SOC estimation error is large due to low precision and poor real-time performance of an existing super capacitor model is solved.