51 results about "Criterion function" patented technology

The invention relates to the technical field of equipment calibration, in particular to a multi-channel pressure sensor calibration method and system, and the method comprises the steps: constructing a flexible pressure response platform, carrying out the channel calibration according to the flexible pressure response platform, and carrying out the preliminary deviation modeling; generating a nonlinear response correction factor according to a result output by the preliminary deviation modeling, and performing local regression adjustment on the channel according to the nonlinear response correction factor; in response to completion of local regression adjustment of each channel, performing unified response surface fusion processing; and in response to completion of unified response surface fusion processing, constructing a stability comprehensive criterion function, and generating a calibration result according to the stability comprehensive criterion function. According to the method, the limitation that a traditional method depends on a linear model and static single-point calibration is overcome, and the problem of error accumulation caused by inter-channel interaction interference and delay response is solved.

The invention relates to the technical field of network security, and discloses a program-level code similarity analysis method and device based on semantic slicing, and the method comprises the steps: constructing a program-level target function call graph based on a target source code; matching the target function call graph with a standard function call graph to obtain a plurality of isomorphic sub-graphs; performing semantic slicing on each function node in the first sub-graph and the second sub-graph to obtain a first slicing result of the first sub-graph and a second slicing result of the second sub-graph; and evaluating the slice similarity between each function in the first slice result and each function in the second slice result, and determining the similarity of the target source code according to the slice similarity. According to the method, the user-defined function call graph for eliminating system function interference is constructed, an isomorphic sub-graph matching algorithm is combined, multi-dimensional features are comprehensively considered, the accuracy of similarity calculation is improved, effective support is provided for practical application scenes such as code reconstruction and security audit, and the guarantee of code quality and security is improved.

The invention belongs to the field of aircraft fault diagnosis and maintenance guarantee, and particularly relates to a flight parameter diagnosis rule dynamic compiling method. According to the method, codes required by the criterion, the blank-leaving criterion formula and the associated parameters are designed in advance according to the criterion function, the criterion formula and the associated parameters are filled after the criterion formula is analyzed, the to-be-compiled criterion file is generated, and finally the to-be-compiled file is compiled into the executable program capable of being linked to the main program through the compiling tool chain. And thus, the criterion is dynamically compiled into an executable program. The time for converting the criterion into the executable program is greatly shortened, the software development process is guaranteed to a great extent, and the guarantee efficiency is greatly improved.

The invention discloses a recursive augmented least square-based dynamic linearization error-assisted model-free adaptive control method and system, and a storage medium, relates to the field of control methods, and aims to solve the problem that the dynamic response performance of a controlled system cannot be effectively improved by an existing model-free adaptive control method. And dynamic linearization errors in a model-free adaptive control process cannot be effectively utilized to suppress disturbance. According to the method, an expansion full-format dynamic linearization data model of a controlled system is established by considering a dynamic linearization error in a control process, a controlled object is compensated based on the expansion full-format dynamic linearization data model, and an expansion pseudo gradient of the controlled system is estimated based on a recursive augmented least square method; and obtaining a model-free adaptive controller based on the expanded full-format dynamic linearization data model at the moment k based on the improved criterion function, inputting the compensated control input signal of the controlled object into the controlled object, calculating the dynamic linearization error difference of the system, and carrying out model-free adaptive control.

The invention discloses a power prediction method for a wind power generation system based on a stacked sparse auto-encoder network Hammerstein model, and aims to solve the problems that only nonlinear mapping is modeled and dynamic characteristics of the system are neglected and parameter coupling of the Hammerstein model leads to complex identification in an existing method, the Hammerstein model is constructed, an ARMAX model is utilized to describe a dynamic linear module, and the dynamic linear module is used to predict the power of the wind power generation system based on the stacked sparse auto-encoder network Hammerstein model. A static nonlinear module is described by stacking the sparse auto-encoder network; designing a zero-mean Gaussian signal input proxy model, realizing series module decoupling based on covariance function characteristics, and eliminating parameter coupling; a self-adaptive multi-strategy grey wolf optimization algorithm is adopted to determine the number of neurons in a network hidden layer, training is performed in combination with a sparse criterion function, and the feature extraction capability is improved. According to the method, synchronous capture of static nonlinear and dynamic linear characteristics is realized, the calculation complexity is reduced, the model identification precision, prediction precision and robustness are improved, and the method is suitable for accurate prediction of the power of a wind power system.

The present application discloses a two-stage filtering maximum likelihood stochastic gradient identification method for MIMO systems, the method comprising: constructing intermediate variables for the MIMO system output error autoregressive sliding average model to determine the model intermediate variable expression; constructing a single output for the MIMO system output error autoregressive sliding average model according to the model intermediate variable expression to obtain a multi-input single-output subsystem model group; obtaining a filtering identification model through MIMO system identification model construction; constructing a criterion function for the filtering identification model to determine the identification model criterion function; obtaining the identification model initial configuration parameters through the identification model initial configuration; determining an information vector group, and determining parameter estimation data through parameter estimation comparison and judgment based on the information vector group. The present application solves the technical problems of insufficient parameter estimation accuracy, high model complexity and overfitting risk for multi-input multi-output systems through the above method.

A variable buffer status report configuration is transmitted from a wireless communication network node to a user equipment. The configuration may include one or more functions to compute a buffer status report indication indicating a metric related to a buffer of the user equipment or related to uplink traffic stored in the buffer of the user equipment, or calculates a table indicating metrics related to a buffer of the user equipment or related to uplink traffic stored in the buffer of the user equipment. The configuration may include a function to determine a precision variable that corresponds to a step size between different criteria or criterion functions that are used to determine the buffer status report indication. The size of the buffer status report indication may vary based on the indicated value. The user equipment may apply different reception configurations, or a function thereof, to determine a buffer status report indication based on different parameters, such as traffic type, traffic quality of service, buffer size, or amount of buffered uplink traffic.

The invention discloses an elastic prediction control method for a converter, and the method comprises the steps: constructing an output current increment prediction model which comprises an inherent dynamic item reflecting the mapping relation between the historical data of a converter system and the output current increment, and an attack item reflecting external abnormality; constructing a criterion function, calculating the gradient of the criterion function about the coefficient matrix, setting the gradient to be zero, and obtaining the estimation of the coefficient matrix at the current moment; taking the error between the reference current of the next period and the actual output current of the current period as the target output of an output current increment prediction model, rewriting the output current increment prediction model, and obtaining the control law of the voltage increment; and based on the control law of the voltage increment, substituting the coefficient matrix estimation at the current moment to obtain a voltage control increment, and selecting the switching state of the converter based on the voltage control increment. According to the method, converter prediction control without parameter dependence can be realized, false data attacks are resisted, the switching frequency is reduced, and the system robustness is improved.

The present invention belongs to the field of aircraft fault diagnosis and maintenance, and specifically relates to a method for dynamically compiling flight parameter diagnostic rules. The present invention pre-designs the code required for the criterion according to the criterion function, leaving blanks for the criterion formula and associated parameters. After the criterion formula is parsed, the criterion formula and associated parameters are filled in, and a criterion file to be compiled is generated. Finally, the criterion file to be compiled is compiled into an executable program that can be linked to the main program through a compilation tool chain, thereby realizing the dynamic compilation of the criterion into an executable program. This greatly shortens the time it takes to convert the criterion into an executable program, greatly speeds up the guarantee software development process, and significantly improves guarantee efficiency.

The invention discloses a four-rotor aircraft data driving control method breaking through bandwidth constraint under hostile attack. The method specifically comprises the following steps: establishing a kinetic model of a four-rotor aircraft; converting the obtained kinetic model into an equivalent dynamic linearization equation by using a dynamic linearization method; designing a criterion function, and designing a data driving control method of the four-rotor aircraft; an event triggering mechanism is introduced, denial of service attack is considered, and then the final four-rotor aircraft safety event triggering data driving control method is obtained through arrangement of the obtained control method. According to the method, model-free adaptive control is applied to attitude control of the four-rotor aircraft, the advantages of PID are combined, malicious denial of service attacks are considered, an event triggering mechanism is introduced, the good flight quality of the aircraft is guaranteed, system bandwidth resources can be saved, and the working time of the aircraft is prolonged.

The embodiment of the invention provides an alternating current servo system control method, system and device based on an internal model principle and a medium, and belongs to the technical field of motor control. The method comprises the following steps: performing mechanism analysis, system identification and model order reduction processing on the alternating current servo system to obtain a four-order model corresponding to the alternating current servo system; on the basis of the fourth-order model, designing the corresponding optimal state feedback of the AC servo system under a linear quadratic regulator; determining a state observer of the inner ring object model, and determining an optimal observer parameter by using a first LQ criterion function according to the state observer; determining a first state space expression corresponding to the AC servo system according to the optimal state feedback and the optimal observer parameter; and determining a target control parameter corresponding to an optimal controller of the AC servo system according to the first state space expression, so that the AC servo system realizes optimal control according to the target control parameter.

The invention relates to the technical field of inverters, and discloses a VSG-based inverter parameter identification method and device, equipment and a storage medium, and the method comprises the steps: generating an initial population through an initial search space and a preset number Q of individuals, the initial search space reflecting the value range of model parameters of a VSG-based inverter model, and the value range of the model parameters of the VSG-based inverter model; the initial population comprises Q initial individuals, and each individual corresponds to a group of candidate model parameters; performing iterative updating on the model parameters through evolution operations such as variation, crossover and out-of-group competition, and finally determining target fitness according to a predetermined real response value of the inverter, an estimated response value of the inverter under the candidate model parameters in the target population and a preset criterion function. According to the method, the optimal individual corresponding to the optimal identification result of the model parameters is evaluated, the parameter identification accuracy is improved, and the problems of modeling and simulation of a high-proportion new energy power system can be solved while the power grid voltage support capability is improved.

The invention relates to the technical field of inverters, and discloses an inverter parameter identification method based on multi-point crossover and a related device, and the method comprises the steps: determining a first population corresponding to a model parameter of an inverter model under a current iteration number G; performing evolution processing on the first population by using a preset multi-point crossover rule to obtain a target population; determining target fitness according to a predetermined real response value of the inverter, an estimated response value of the inverter under the model parameter individuals in the target population and a preset criterion function; if the target fitness does not meet the preset convergence condition, G is made to be equal to G + 1, the step of determining the first population corresponding to the model parameters of the inverter model under the current iteration number is executed again, and G is the current iteration number; and if the target fitness meets a preset convergence condition, taking a model parameter corresponding to the optimal model parameter individual in the target population as an optimal identification result. Parameter identification is carried out through a multi-point crossing rule, so that a parameter identification result is more reliable and accurate.

The invention relates to a Copula function-based charging behavior analysis method, electronic equipment and a medium, and the method comprises the steps: determining two to-be-researched parameters of a charging behavior; respectively inputting the observation data of the parameters to be researched into a plurality of Copula functions, and calculating an AIC criterion function value corresponding to each Copula function; and taking the Copula function with the minimum AIC criterion function value as a final Copula function, and calculating the correlation of the to-be-researched parameters by adopting the final Copula function. According to the method, the probability distribution and the correlation of each parameter of different types of charging behaviors can be calculated through the Copula function.

The present invention discloses a multi-wavelength phase unwrapping method and an object height measurement method and height measurement system. The multi-wavelength phase unwrapping method is used to obtain the optimal phase sequence of the coordinates at the surface of the object to be measured at the optical wavelength based on a multi-wavelength off-axis digital hologram of the object to be measured at the optical wavelength, using a height distribution model based on synthetic wavelength chain guidance and phase sequence search: the height distribution of any coordinate at the surface of the object to be measured at the optical wavelength in the hologram is used as an initial value, and a phase sequence is searched for at a certain interval within a preset range of the initial value. The phase sequences found by the search are used to form a criterion function. When the minimum value of the envelope of the criterion function is taken, the corresponding phase sequence is considered to be the optimal phase sequence. The present invention determines the optimal phase sequence by searching for the minimum value of the envelope of the criterion function, rather than by finding the minimum value of the criterion function. This greatly solves the problem of errors in the optimal phase sequence caused by noise damage to the criterion function.

The invention discloses a model-free self-adaptive discrete time fast terminal sliding mode control method and system based on a novel discrete approaching rate, and the method is compared with a conventional PI controller, a sliding mode controller and a model-free self-adaptive linear sliding mode controller. The response speed of the system is improved, and the method does not depend on a system model any more. Meanwhile, on the basis of input and output data of the permanent magnet synchronous motor system, a pseudo-partial derivative estimation algorithm is constructed according to a criterion function to obtain a time-varying parameter, a pseudo-partial derivative resetting mechanism is perfected, motor control based on data driving is achieved, and convergence of the pseudo-partial derivative estimation algorithm and stability of a control algorithm are analyzed; the control method is high in control precision and high in response speed, the robustness and the anti-interference performance of the system are improved, and then the control performance of the motor system under the complex working condition is improved.

The application discloses a kind of cylinder linear motor multi-objective optimization method, equipment and storage medium, the average thrust of the cylinder linear motor and thrust fluctuation are optimization target, the design parameter influencing it is analyzed, and the range of its value is determined;Sample library is constructed by optimal Latin hypercube experimental design;Kriging model is established based on the sample library;The expected maximum adding point criterion function and prediction function of model are parallel optimized using NSGA-II algorithm;If the model reaches the set convergence accuracy, the optimal design parameter is obtained by NSGA-II algorithm optimization;Otherwise, the real value is calculated and added to the sample library, and the optimization is repeated.The application combines NSGA-II algorithm and Kriging model, and parallel optimization is carried out on the expected improvement maximum criterion function and prediction function using NSGA-II algorithm, which can quickly improve the model accuracy and improve the optimization efficiency and robustness of the algorithm.

Unlike conventional or otherwise known decomposition methods like standard functional decomposition, time-affecting linear pathway (TALP) decomposition, or output-affecting linear pathways (OALPs) from the decomposition of TALPs, advanced output-affecting linear pathway (A-OALP) decomposition from algorithms separate the output variables of each execution pathway such that there is only one output variable per A-OALP, converting an algorithm into a set of process groups. A-OALPs extend parallelization by combining task-like parallelism with dynamic loop parallelism. The lightweight nature of A-OALPs allows for persistent thread and code management.

The present invention relates to the field of control methods and is directed to solving the problem that existing model-free adaptive control methods cannot effectively improve the dynamic response performance of a controlled system and cannot effectively utilize the dynamic linearization error in the model-free adaptive control process to suppress disturbances. The method of the present invention considers the dynamic linearization error in the control process to establish an extended full-format dynamic linearization data model of the controlled system, compensates the controlled object based on this, estimates the extended pseudo-gradient of the controlled system based on the recursive augmented least squares method, obtains a model-free adaptive controller based on the extended full-format dynamic linearization data model at time k based on an improved criterion function, inputs the compensated controlled object control input signal into the controlled object, calculates the system dynamic linearization error difference, and performs model-free adaptive control.

A variable buffer status report configuration is transmitted from a wireless communication network node to a user equipment. The configuration may include one or more functions to compute a buffer status report indication indicating a metric of the user equipment related to the buffer or to uplink traffic stored in the buffer, or to compute a table used to indicate the metric. The configuration may include a function to determine a precision variable that corresponds to a step size between different criteria or criterion functions used to determine the buffer status report indication. The buffer status report indication may vary in size based on the indicated value. The user equipment may apply different received configurations, or a function thereof, to determine a buffer status report indication based on different parameters, such as traffic type, traffic quality of service, buffer size, or buffered uplink traffic.

The application discloses a kind of aircraft global time-varying aerodynamic parameter identification method, comprising: S1. establishing aircraft rigid body dynamics model, and obtaining time-varying parameter to be identified;S2. the time-varying parameter to be identified is written as polynomial form about time;S3. state sensitivity, covariance sensitivity, gain sensitivity, filter state sensitivity and covariance sensitivity are estimated;S4. according to aerodynamic parameter characteristics or time period, carry out local subarea;S5. in current interval, obtain state estimated value, estimated covariance matrix, Kalman gain, state filter value, covariance matrix by parameter value in combination with Kalman filter;S6. according to the above obtained criterion function, when criterion function is not minimum value, parameter is updated using Newton-Raphson algorithm;S7. using moving average filter to smooth all parameters, obtain a smooth global aerodynamic data curve.The application avoids the problem that fitting is not accurate when parameter variation amplitude is too large.

The invention relates to the technical field of charging piles, and discloses a fan rotating speed control method for a power module, electronic equipment and a storage medium, and the method comprises the steps: constructing a dynamic linearization data model related to the air inlet temperature and the fan rotating speed of the power module; based on the dynamic linearization data model, a pre-built first criterion function is combined, and an estimation function of the air inlet temperature is obtained; and obtaining an estimated value of the pseudo partial derivative, and substituting the estimated value of the pseudo partial derivative into the estimation function of the air inlet temperature for calculation to obtain an output value of the fan rotating speed. According to the method, self-adaptive control over the rotating speed of the draught fan is achieved by combining estimation of the pseudo partial derivative with the estimation function of the air inlet temperature, the rotating speed of the draught fan of the power module can be adjusted in real time, the noise pollution is reduced while the heat dissipation requirement is met, and the model-free self-adaptive control mode is adopted; the rotating speed of the fan can be controlled without establishing a complex process model, external interference factors can be effectively eliminated, and the control precision is improved.

The invention discloses an English syllable u feature wavelet coefficient extraction method based on a spline interpolation wavelet neural network, and the method comprises the steps: firstly carrying out the normalization and threshold preprocessing of an audio signal, and effectively removing the interference of environment noise; secondly, constructing a three-layer neural network feedback matrix based on a six-order spline wavelet function, and generating a feedback matrix through transposition operation and inverse matrix calculation of a global matrix psi; then, inverse discrete Fourier transform is utilized to construct a criterion function containing frequency domain errors; and finally, dynamically adjusting the weight of an output layer through iterative training, and terminating training when the modulus value of the criterion function is smaller than a training error iteration ending condition, so as to obtain a wavelet coefficient set representing the characteristics of the longhairy sound u. According to the method, the frequency domain localization characteristic of the six-order spline wavelet is innovatively combined with the adaptive learning of the neural network, the anti-noise performance is improved while the feature extraction precision is ensured, and the problem of individual pronunciation difference is solved.

The invention discloses a construction layer Q estimation method and system based on a depth consistency criterion function. According to the method, seismic data are divided into a plurality of structural layers according to interpretation horizon, and seismic wave attenuation in each layer is represented by an equivalent quality factor Q value; a scanning Q value is set, and a reflection coefficient and corresponding reconstructed seismic data are obtained through unsteady-state inversion; characterizing the consistency of the reconstructed seismic data in the layer by using a depth consistency criterion function layer by layer, and selecting a Q value corresponding to the highest consistency as the Q value of the layer; and performing layer-by-layer circulation processing to finally obtain a structural layer Q model and corresponding reconstructed Q value compensation seismic data. According to the invention, a stable construction layer Q model estimation technology integrated with construction information can be provided, and the resolution and reliability of seismic data are remarkably improved.

The invention belongs to the technical field of ship motion, and discloses a ship LAAV motion model parameter identification method based on a dynamic immune genetic algorithm, which comprises the following steps of: constructing an identification model fitness criterion function by using ship real sea test data, setting a hyper-parameter adaptive adjustment mechanism, and performing several-generation selection, intersection, variation and immune operation on an initial population to identify ship LAAV motion model parameters. And finally determining model parameters. According to experimental verification, unknown parameters in a ship LAAV motion model are optimized by utilizing a DIGA framework in combination with sea test data, and an optimal solution is substituted into the LAAV model to generate AVP (Attribute, Velocity, Position) data in a simulation manner. The test result shows that the DIGA method has the advantages of high precision and high convergence speed in the LAAV model parameter identification problem, and the similarity between the generated AVP data and the sea test data is more than 90%.

The application discloses a kind of based on model-free adaptive multi-farm machine formation path navigation-tracking control system and control method, belong to agricultural machinery navigation technical field.Main steps are:1, using sensor, real-time acquisition agricultural machinery position information, pre-look lateral deviation and area deviation.2, based on deviation information and control input constructs dynamic linear equation, constructs criterion function, minimizes tracking error in control increment, deduces control law, combines pseudo partial derivative online estimation algorithm, realizes model-free adaptive trajectory tracking.3, based on the position information of leader agricultural machinery of design, real-time control follower motion control operation, realizes agricultural machinery vehicle path tracking precision.4, based on the model-free adaptive control scheme of design pre-look deviation.The application can adapt to complex farmland environment, not dependent on accurate model and have strong adaptive ability, realizes high-precision, strong robustness collaborative operation, provides technical support for modern agricultural intelligent development.

The invention relates to an aircraft product simulation method supporting multi-mode calculation, which comprehensively considers multiple factors such as function universality, compatibility and the like of an aircraft product model extension platform, and adopts echelon packaging and real-time scheduling technologies from the perspective of protecting an aircraft product control model. The first-stage package is a dynamic link library of two standard functions and five interaction components, and the second-stage package is an aircraft product integrated dynamic library containing a unique identification code. The access and replacement of different types of aircraft product component entities / models are supported, the aircraft product model extension calls the core function, calling of each component model can be realized, the requirements of real-time calculation of information such as anti-interference capability simulation motion trails, motion speeds and motion attitudes of different types of aircraft products are met, and the real-time calculation of the anti-interference capability simulation motion trails is realized. And analysis and distribution of open-loop data are supported, multi-mode aircraft product simulation is supported, and aircraft product model debugging time and cost can be effectively reduced.

The invention provides a method, a device and equipment for predicting the compressive strength of medium and high strength concrete based on a GA-BP neural network model, and a storage medium, and relates to the technical field of concrete strength prediction. The method comprises the following steps: acquiring a plurality of groups of concrete test samples containing cement, slag, fly ash, water, a water reducing agent, stone and sand and corresponding compressive strength measured values; a GA-BP neural network model is constructed, the node number M of an input layer of the GA-BP neural network model is consistent with the concrete component parameter type, a quadratic form error criterion function is used as a target function, a weight and threshold optimal solution is obtained through genetic algorithm optimization, and model construction is completed; performing normalization processing on the test sample, and inputting the test sample into the model for training; and normalizing to-be-predicted concrete component parameters, and inputting the normalized to-be-predicted concrete component parameters into the trained model to output a compressive strength predicted value. According to the method, accurate and stable prediction of the strength of the concrete with the complex mix proportion is achieved, and the problem that a traditional grey water ratio empirical formula is large in error is solved.