199 results about "Radial basis function" patented technology

The invention discloses a digital twin system architecture of a vertical shaft and a structural performance monitoring method. The method comprises the following steps: constructing a five-dimensional digital twin system framework suitable for a vertical shaft based on a shaft operation mechanism and performance monitoring requirements; establishing a shaft digital twinborn model with dynamic characteristics; a finite element proxy model is constructed through a virtual-real mapping technology in combination with a grid dimensionality reduction finite element analysis method, and rapid generation of the digital twinborn body is realized. According to the system, a three-dimensional operation platform is constructed based on a Unity 3D virtual engine, and efficient mapping and bidirectional interaction between twin and finite element simulation data are realized by adopting a radial basis function (RBF) proxy model. And real-time acquisition and online prediction are carried out on structural performance parameters in the shaft operation process. And dynamically updating the twinborn model according to a prediction result, and constructing a high-precision and light-weight digital twinborn evolution model, thereby realizing real-time observation of the stress change of the shaft and intelligent monitoring of the structural performance. The method can be widely applied to the fields of shaft safety assessment, maintenance decision making, intelligent mine construction and the like.

The invention belongs to the technical field of multi-body dynamics analysis, and discloses a wind driven generator transmission chain rigid-flexible coupling multi-body dynamics analysis method based on dynamic mode decomposition, and the method comprises the steps: firstly, enabling multi-degree-of-freedom time series data to be non-linearly embedded into a high-dimensional feature space through an encoder neural network; extracting a dominant mode by utilizing intrinsic orthogonal decomposition (POD), and constructing a low-dimensional feature space; parameterized dynamic mode decomposition and radial basis function regression are adopted, a mapping relation between system parameters and Koopman operators is established, and accurate prediction of dynamic characteristics under variable working conditions is achieved; and finally, reconstructing a physical response through a decoder, and optimizing model parameters in combination with an error driving mechanism. The problems that a traditional method is low in calculation efficiency, poor in nonlinear adaptability and difficult in multi-parameter coupling prediction are effectively solved, the efficiency and precision of transmission chain dynamic analysis are remarkably improved, and reliable technical support is provided for state monitoring and service life prediction of the wind turbine generator.

The invention discloses a dual-band InSar and GNSS fused high-precision three-dimensional deformation adaptive monitoring method, and the method comprises the following steps: constructing a dual-band observation equation, and decomposing the dual-band observation equation to obtain a dual-band InSAR resolving residual error; calculating the comprehensive weight of each GNSS observation point through an entropy weight method, selecting a plurality of GNSS observation points with high comprehensive weights as an optimal GNSS reference point combination, and converting the three-dimensional deformation observed by the optimal GNSS reference points into an LOS direction; expanding a two-dimensional radial basis function to a three-dimensional radial basis function, optimizing the number and distribution of the three-dimensional radial basis function according to the residual drop rate in the expansion process, and performing basis function encryption on the key area by combining the LOS direction result of the reference point; an original two-dimensional state equation in a Kalman filtering algorithm is expanded to be three-dimensional, meanwhile, a GNSS three-dimensional observation value serves as a strong constraint condition of the Kalman filtering algorithm, and a final three-dimensional deformation rate field is output through iteration and 3D-Va analysis.

The invention relates to the technical field of ship energy consumption prediction, and discloses a ship energy consumption interval prediction method and system based on a Gaussian quantile regression model.The method comprises the steps that firstly, a ship navigation historical data set is preprocessed, a model input feature set is screened, a Gaussian process quantile regression model with a radial basis function as a kernel is constructed, and hyper-parameters are optimized; and after the prediction performance of the subset evaluation point is verified through the model, an upper quantile prediction model and a lower quantile prediction model are respectively established according to a target confidence level, and finally a prediction interval is synchronously output and an evaluation report is generated. According to the method, through combination of Gaussian process processing nonlinear relation and quantile regression estimation condition distribution, high-precision point prediction is provided, meanwhile, prediction uncertainty can be quantized, an energy consumption prediction interval corresponding to a target confidence level is output, and more comprehensive and reliable information support is provided for ship energy efficiency management and operation decision making.

The invention discloses a mining area roadway stability intelligent classification and support scheme design method, and the classification method comprises the steps: carrying out the denoising through employing a denoising method based on statistical deviation, constructing a weight distribution frame based on a random forest algorithm, carrying out the feature importance evaluation of each feature variable through employing a decision tree integration method, and carrying out the design of a support scheme. Quantifying the influence proportion of each characteristic variable on the stability of the mining roadway, and generating a weight matrix containing the influence proportion of each characteristic; and constructing a mining area roadway stability intelligent classification model based on a support vector machine, adopting a radial basis function as a kernel function to map a feature variable to a high-dimensional space, adjusting a model classification boundary by iteratively optimizing penalty parameters, and training the model by using historical data to obtain an optimized mining area roadway stability intelligent classification model. Accurate evaluation and intelligent classification of the stability of the roadway in the roadway mining area are achieved, the supporting effect and the roadway safety are effectively improved, and technical support is provided for safe and efficient mining of the mine roadway.

The invention discloses a J-A model parameter identification method, system, equipment and medium based on RBF and an improved brownish bear algorithm, and belongs to the technical field of power system optimization, and the method comprises the steps: building a Jiles-Atherton hysteresis reverse model of a current transformer, determining a to-be-identified parameter vector, and building a model with a root-mean-square error between actually measured magnetic field intensity and simulated magnetic field intensity as a target function, training a radial basis function neural network model, expanding data through linear interpolation processing, obtaining a predicted magnetic induction intensity value, inputting an objective function and radial basis function prediction data into an improved brownish bear optimization algorithm, and iteratively optimizing model parameters through hierarchical population position updating and fitness evaluation until convergence conditions are met. And outputting an optimal parameter identification result. According to the method, high-precision and high-efficiency identification of hysteresis model parameters is realized, the generalization capability and robustness of the system are improved, and reliable technical support is provided for hysteresis characteristic analysis of a complex physical system.

The invention relates to a method and device for predicting online open course learner satisfaction and electronic equipment, and the method comprises the steps: predicting the online open course satisfaction of students through an MLP and RBF neural network model by using a virtual learning environment of a large-scale online teaching and learning platform and combining learning behavior data in a learning management system (LMS); the model comprises a data acquisition and processing module, a multilayer perceptron (MLP) module, a radial basis function (RBF) neural network module, a classification tree module and a control block, the data acquisition and processing module is used for generating a training and testing data set, and the MLP and RBF neural network model predicts the satisfaction degree of a learner. The MLP model carries out feature extraction through a multi-layer perceptron structure and different activation functions, the RBF model measures the distance between input data and a center by using a radial basis function to realize feature extraction, the classification tree is used for judging a prediction model to which a data point belongs, the control block integrates features from the MLP and RBF neural network models, and the RBF model is used for determining a prediction model to which the data point belongs. Experimental results show that the prediction accuracy of low-satisfaction-degree learners and high-satisfaction-degree learners can be improved at the same time through the combination scheme of the MLP and the RBF, the method can be applied to learner satisfaction degree prediction of various online open courses, an educational institution is helped to know the satisfaction degree condition of students in time, course design and teaching strategies are optimized, and the teaching efficiency is improved. And important support is provided for teaching reform and optimization in the field of online education.

The invention discloses a machine learning driven vehicle frame lightweight design method, which comprises the following steps: (1) performing vehicle frame parametric modeling in CATIA, constructing a vehicle frame modal analysis model and a stiffness analysis model in ABAQUS, and deducing a mathematical model of vehicle frame lightweight design; (2) performing Latin hypercube sampling and discrete Gaussian sampling on continuous and discrete parameters to generate a frame population, and establishing a database; (3) designing a variable type driven hybrid variation mechanism to obtain a candidate frame population; (4) constructing a radial basis function machine learning model, and deducing a comprehensive minimum statistical lower limit function to screen an optimal candidate frame; and (5) performing parametric modeling and simulation analysis on the optimal candidate frame, updating the frame population and the database, returning to the step (3) until the optimal frame meets the design requirement, and outputting the optimal frame. According to the method, adaptive variation strategies and screening functions are designed according to the vehicle frame parameter characteristics, and the vehicle frame lightweight design effect is good.

The invention discloses a front subframe lightweight design method and device based on RMNN, a medium and a program product, and the method comprises the steps: (1) constructing three simulation models of weight, stress and modal of a front subframe, and deducing a lightweight design model containing stress and modal constraints; (2) generating a population based on a uniform experimental design and a correlation criterion, performing simulation evaluation on the population, and constructing a database; (3) constructing a network architecture and training an RMNN; (4) generating a candidate frame set according to a random preferential strategy and the RMNN; and (5) establishing a radial basis function model, screening an optimal frame, performing simulation evaluation, updating the database and the population, returning to the step (3) until the optimization target reaches the standard, and outputting an optimal solution of the optimization parameter. According to the method, the feasible region is quickly searched through the RMNN, the problems that the high-dimensional design space search efficiency is low and the feasible region is difficult to recognize under multiple constraints are further solved in combination with the radial basis function model, and quick optimization can be carried out for the lightweight design of the front subframe.

The invention provides an urban storm surge forecasting method based on multi-source information coupling, and the method comprises the steps: taking multi-source typhoon driving element data as the input information of a preset typhoon structure reconstruction model, and outputting typhoon key physical parameters through the typhoon structure reconstruction model; based on a Kriging interpolation and radial basis function mixing method, carrying out fusion processing on the multi-source topographic data, and constructing topographic grid information; extracting tide harmonic constants of a specified number of partial tides from the multi-source tide factor data; based on the typhoon key physical parameters, the terrain grid information and the tide harmonic constants, determining input information of a predetermined urban storm surge forecasting model; and outputting a storm surge level abnormal value and a horizontal flow velocity component corresponding to the multi-source prediction associated data through the urban storm surge prediction model. According to the scheme, the precision of urban storm surge forecasting is improved.

The invention provides a mechanical equipment structure key pressure-bearing component real-time simulation method based on digital twinning, which takes a cubic press hinge beam as an example, and combines finite element analysis, Latin hypercube sampling, a K nearest neighbor algorithm, Gaussian interpolation and an RBF (Radial Basis Function) proxy model to realize stress-strain rapid prediction and three-dimensional visualization. According to the method, an efficient prediction model is established through structure database construction, dimension reduction processing, neighbor search and interpolation calculation, a simulation result is presented in real time by utilizing Python and Unity interaction, the design efficiency and accuracy are improved, and the method is suitable for structure optimization analysis under complex working conditions.

The invention discloses a gradient random porous structure design method based on a growth function and RBF (Radial Basis Function) interpolation, which relates to the technical field of geometric modeling and computer graphics, and comprises the following steps: collecting and preprocessing Voronoi seed point coordinates and implicit boundary parameters, extracting density field and gradient parameters, and calculating the density field and gradient parameters; mixing different pore forms based on a beta growth function to generate a transition region model, constructing a scalar field in combination with RBF interpolation, fusing the model and a density field to generate global gradient distribution, optimizing iteration to verify mechanical properties and correcting parameters, mapping the parameters into three-dimensional grid data, rendering a pore density cloud chart and a thermodynamic chart, and marking key parameters. By combining the beta growth function and the RBF interpolation, the problem of strong coupling of gradient control and seed point distribution and the problem of rigid transition of a supporting structure body are solved, flexible design of smooth gradual change and complex gradient of the pore form is achieved, mechanical properties are verified through optimization iteration, a three-dimensional visual model is generated, and the method is suitable for large-scale popularization and application. And the geometric continuity and controllability of the porous structure design are obviously improved.

The invention relates to the technical field of composite material digital detection, in particular to a composite material skin grinding amount calculation method based on clustering analysis and a symbolic distance field, which specifically comprises the following steps: acquiring three-dimensional measurement data of the surface of a composite material skin, and designing a three-dimensional measurement point feature descriptor; based on the feature descriptors, a clustering analysis algorithm is designed, and a grinding area three-dimensional measuring point class and a non-grinding area three-dimensional measuring point class are divided; aiming at the three-dimensional measuring points of the non-grinding area, carrying out implicit curved surface reconstruction based on a radial basis function; aiming at the three-dimensional measuring point of the grinding area, a grinding area symbol distance field is constructed, and the symbol distance is the grinding amount of the measuring point; according to the method, the problem that in the prior art, profile tolerance estimation cannot be obtained during aircraft CAD mathematical model is solved, the grinding allowance of the aircraft skin repairing area can be accurately analyzed, and aircraft maintenance personnel are guided to conduct grinding. Compared with a traditional method for judging the profile tolerance based on manual touch, the method has the advantages that interference of human subjective factors is eliminated, the grinding allowance is quantified, and the precision is higher.

The invention discloses a global ionosphere modeling method based on a spherical radial basis function, and the method comprises the steps: laying Reuter grid points at an interval of 10 degrees on an ionosphere thin layer, and converting the Reuter grid points into a daily fixed geomagnetic coordinate system for modeling. Cycle slip monitoring and abnormal value identification and elimination are carried out on obtained satellite observation data (O file), then a precise satellite orbit position is obtained through interpolation in combination with a precise ephemeris file (SP3), hardware deviation is corrected through differential code deviation (DCB file), an AbBPoisson spherical surface radial basis function is adopted and converted into a 15-order truncated AbBPoisson spherical surface radial basis function, and finally, a precise satellite orbit position is obtained. The bandwidth is set to be 0.25, L2 regularization optimization is used, parameters of a radial basis function are solved through a least square method, and therefore global ionosphere modeling is carried out. Different from a traditional spherical harmonic function method, the abbeposon spherical radial basis function controls the control range of a radial base point by adjusting the bandwidth, higher precision can be provided in a data-intensive area, and the adaptability is higher.

The invention belongs to the technical field of geological three-dimensional modeling, and provides a line-plane boundary constraint promotion modeling method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the modeling through employing a line-plane boundary constraint promotion model, and obtaining a three-dimensional geologic body model; the modeling method comprises the steps that a point set of a three-dimensional geologic body is obtained, optimization is conducted through a minimization energy function when radial basis function implicit modeling processing is conducted according to the point set, and a normal vector and an implicit function are obtained; the method comprises the following steps: introducing information of a planar geologic structure into an implicit modeling framework in an integral form, performing line-plane constraint on an implicit function, processing the line-plane constrained implicit function through a solution matrix to obtain an implicit function parameter, and determining a line-plane boundary constraint promotion model according to the implicit function and the implicit function parameter. The planar feature information of a geologic body can be effectively utilized, accurate constraint and continuous expression of a geological interface or a stratigraphic structure are achieved, and the spatial precision and reliability of three-dimensional geological modeling are improved.

The invention relates to a slope instability probability evaluation method based on burial depth constraint space random field simulation, and belongs to the technical field of slope engineering. The method comprises the following steps: acquiring shear strength parameters c and phi at different buried depths of a slope soil body in a research area; the obtained shear strength parameters are expanded according to Gaussian process regression in combination with a radial basis function, the reliability of the expanded parameters is determined through K-S inspection, and cross correlation between c and phi is obtained; determining statistical characteristics of the c and phi expansion data; under the burial depth constraint condition, slope model determinacy research is carried out, a safety coefficient is calculated, the model is compared with a traditional model, and the difference between the potential slip plane of the slope under the burial depth constraint and the potential slip plane of the traditional slope is obtained; the traditional Monte Carlo simulation MCS is optimized; according to deterministic analysis and a random field theory, carrying out uncertainty research on the side slope under the burial depth constraint, and calculating the instability probability of the depth evolution random field side slope model.

The invention discloses an electric arc surfacing repair trajectory planning method based on point cloud information. The method comprises the steps that defect point cloud data of a to-be-repaired area and intact point cloud data of an intact area adjacent to the to-be-repaired area are acquired; carrying out reconstruction fitting on the obtained defect point cloud data by adopting Poisson reconstruction to obtain a defect feature surface; fitting the obtained intact point cloud data by using a high-order function and a radial basis function to obtain an intact feature surface; and offsetting the intact feature surface for a specific distance along the defect depth direction to obtain an offset curved surface, and obtaining a path planning target area according to the offset curved surface. And the defect morphology is obtained on the premise of no original model, so that the applicability of the welding robot is enhanced.

The invention provides a deep learning channel estimation method based on spatial perception interpolation, and belongs to the technical field of wireless communication and artificial intelligence fusion. The method comprises the following steps: firstly, constructing a pilot frequency index set according to sparse distribution of pilot frequency points, and mapping the pilot frequency index set to a two-dimensional subcarrier-symbol grid; then generating a centrosymmetric and edge-attenuated space weighting matrix alpha (x, y), and adjusting interpolation weight distribution by introducing a position-related Gaussian weighting function; complex field interpolation is carried out on the pilot frequency observation value in combination with a space weighting radial basis function, and a complete channel initial estimation matrix is constructed; and then decomposing the interpolation result into a real part, an imaginary part and a spatial weighting coefficient, constructing a three-channel tensor as input, sending the three-channel tensor into a convolutional neural network for refined estimation, and outputting a final complex channel estimation result. In a preferred embodiment, the convolutional neural network adopts an SRCNN structure, and inputs a real part, an imaginary part and a spatial weighting matrix alpha (x, y) including an interpolation channel to enhance the spatial perception ability of the model. Simulation results show that under a VehA standard channel model, the method is always superior to a traditional LS method under the sparse pilot frequency condition, the performance of the method is close to that of an MMSE method, higher estimation precision and higher robustness are shown, and the method is suitable for a channel recovery task in a high-speed dynamic wireless communication system.

The invention discloses a front subframe structure MaOP method and device based on TPT, a medium and a program product, and the method comprises the steps: (1) constructing a MaOP design model capable of optimizing the weight, modal, strength and rigidity at the same time based on front subframe longitudinal beam and load analysis; (2) generating an elite population based on a diversity criterion and Latin hypercube, obtaining target values of the elite population, establishing a data set, and constructing a radial basis function model; (3) designing co-evolution operation driven by TPT to generate a candidate frame set; (4) an individual potential evaluation criterion is constructed based on the advantage and disadvantage target sets, and an optimal candidate frame is screened; and (5) obtaining each target value of the optimal candidate frame, updating the data set and the radial basis function model, returning to the step (3) until all optimization targets meet requirements, and outputting an optimal parameter value. According to the method, a TPT-driven coevolution mechanism is adopted, radial basis function model prediction is combined, and the multi-performance index optimization process for the front auxiliary frame can be effectively balanced.

The invention discloses a mixed time-varying reliability analysis method for a solid rocket engine shell structure. Uncertain parameters and distribution are determined according to complex characteristics such as high-temperature and high-pressure time-varying load borne by a solid rocket engine shell structure, geometric sudden change in a star hole charging combustion stage, a throat ablation non-stationary interval process and a time-varying correction coefficient random process, and the random and interval processes are processed in a unified mode through equivalent time-varying uncertainty conversion. Input dimension explosion is avoided, specifically, a random process vector and an interval process vector are converted into an equivalent random vector, so that a time-varying limit state function is equivalently converted into a static function, the input dimension of the RBF combination model is effectively prevented from being increased, the advantages of multiple basis functions are fused through the radial basis function RBF combination model, the calculation cost is reduced, and the calculation efficiency is improved. The modeling precision of the strong nonlinear limit state function of the solid rocket engine shell is improved, the nonlinear mixed time-varying reliability analysis problem is efficiently solved, and the calculation precision is high.

The invention discloses an optimization method and device based on a neural network model, a medium and a program product, and the method comprises the steps: (1) carrying out the modeling of an automobile rear subframe based on SFE-Concept, constructing a first-order modal maximization mathematical model according to an adaptive penalty function, generating an optimization population based on Latin hypercube, and carrying out the optimization of a first-order modal maximization mathematical model; performing first-order modal and rigidity simulation analysis on the optimized population in an Isight multidisciplinary optimization design platform; (2) generating an optimal candidate sub-population and a successful design variable vector through differential evolution based on a cubic kernel radial basis function machine learning model; (3) training the Dropout neural network model to obtain evolution parameters; (4) updating evolution parameters based on the Dropout neural network model; and (5) updating and optimizing the population based on the evolution parameters, if a convergence condition is met, outputting an optimal rear subframe, otherwise, returning to the step (2). According to the method, the evolution parameters are adaptively adjusted according to the Dropout neural network model, and the adaptability to the modal optimization problem of the rear subframe of the automobile is high.

The invention discloses a battery health state prediction method based on variational feature extraction and composite kernel function optimization Gaussian process regression. The method comprises the following steps: firstly, collecting battery cycle data, extracting features by utilizing VAE, screening out an optimal feature set by adopting PCC, and jointly dividing the optimal feature set and SOH data into a training set and a test set; in the training process, the optimal modal number K of the VMD is determined through the envelope entropy, a composite kernel function CKFGPR model fusing a linear kernel, a radial basis kernel and a periodic kernel is constructed, and hyper-parameters are optimized through maximum likelihood estimation. In the test stage, the features are decomposed into K intrinsic mode components by using VMD, the K intrinsic mode components are respectively input into the CKFGPR model for prediction and reconstruction, and finally the CKFGPR model is input again to realize SOH prediction. According to the method, the feature robustness is enhanced through cooperative application of the VAE and the VMD, the fitting capability of the GPR to the battery degradation behavior is improved in combination with the composite kernel function, and the SOH prediction precision is effectively improved.

The invention discloses a parameterization design method for a speed brake mechanism applied to a wide-area aircraft. The parameterization design method comprises the steps that the maximum resistance increment and the target resistance increment of the speed brake mechanism are determined; determining constraint conditions of reference design parameters and dynamic design parameters, and constructing a resistance increment mathematical model based on a radial basis function; the maximum resistance increment is substituted into the mathematical model, the extension length of the auxiliary board and the opening angle of the main board are made to be maximum values of constraint conditions, an optimization equation is built, and an optimal reference design parameter combination meeting the maximum resistance increment requirement is obtained; the target resistance increment is substituted into the mathematical model, an optimization equation is constructed by taking the reference design parameters as optimal reference design parameters, and an optimal dynamic design parameter combination meeting the target resistance increment requirement is obtained; and performing simulation verification on the optimal design parameter combination until the design is verified to be qualified, and outputting final design parameters. Wide-range adjustment of resistance can be achieved, accurate control over resistance errors is achieved, and the wide-range aircraft resistance increasing requirement is met.

The invention discloses a CVM-based airfoil profile multi-objective optimization method, equipment, a medium and a program product. The method comprises the following steps: (1) respectively constructing an airfoil profile lift coefficient simulation model, an airfoil profile resistance coefficient simulation model and an airfoil profile rigidity simulation model in SU2 and Ansys; (2) establishing a population and a database by using Latin hypercube sampling and Pearson product-moment correlation coefficients; (3) constructing a global radial basis function prediction model; (4) generating candidate offspring individuals for each sub-problem by using elite-guided co-evolution operation, and constructing a sub-population; (5) evolving the sub-population in the characteristic space after covariance matrix mapping; and (6) based on a Chebyshev fitness function, screening an airfoil node parameter vector solution set, updating a population and a database, and turning to the step (3) until a design cycle is reached. According to the method, the comprehensive performance of the airfoil structure is improved through elite-guided co-evolution and characteristic space efficient evolution after covariance matrix mapping.

The present application relates to the technical field of aquaculture, and particularly relates to an improved PSO-based water quality parameter prediction method and system for aquaculture, which comprises collecting water quality parameters at different positions and depths in a breeding pond; training an improved radial basis function (RBF) neural network using training set data; and optimizing the parameters of the improved RBF neural network model using an improved particle swarm optimization (PSO) algorithm. The present application introduces a mixed Gaussian function and an abnormal S-shaped function into the radial basis function of the traditional RBF neural network, thereby solving the problem of weak capability of the model in nonlinear data modeling. Furthermore, the present application improves the inertia factor and the learning factor in the traditional PSO algorithm, thereby solving the problems of slow parameter convergence speed and poor global search capability in the RBF neural network.

The invention discloses a crane main beam non-probability reliability assessment method based on a convex set model, and the method comprises the steps: collecting the working condition and key section multi-source data of a crane, and carrying out the time alignment, missing measurement compensation and time-frequency feature extraction; selecting a parameter vector influencing the safety of the main beam, and constructing a combined convex set uncertain domain consisting of an interval box, an ellipsoid set and polyhedron linear constraints; based on a finite element and radial basis function substitution model, main beam key section response mapping is established, the proportion of equivalent stress and deflection relative to an allowable value is obtained, and an overall limit criterion is formed; executing worst case search in the uncertain domain, and calculating an opposite number of a maximum value of a limit criterion as a non-probability reliability index; and reliability indexes are further calculated and summarized at the section layer and the main beam layer, and the reliable state grading of the main beam is realized according to a preset threshold value. The method does not need to depend on probability distribution hypothesis, and rapid assessment of the safety of the crane girder can be realized under small sample and multi-source uncertain conditions.

The invention belongs to the technical field of magnetic suspension control, and provides a multi-point cross-coupling suspension control method for a sliding-mode-driven RBF network, and the method comprises the steps: constructing a sliding-mode surface and a radial basis function neural network according to the suspension gap error of a single-point electromagnet, and obtaining a sliding-mode-driven RBF neural network control method for the single-point electromagnet; independent sliding mode driving RBF neural network control is respectively carried out at four electromagnets, gap and speed dual cross coupling terms are designed according to errors among points in a four-point suspension frame, error terms are superposed in control input, and finally a sliding mode surface driving RBF controller combined with cross coupling control is formed. According to the method, the tracking performance of the target gap and the synchronization performance between the suspension points are improved, stable suspension of the suspension frame is achieved, and it is verified that the method still has a remarkable control effect under the operation conditions that the track is not smooth and faults occur.

The invention provides a non-stationary signal fault diagnosis method and a storage medium, and the method comprises the steps: carrying out the discretization of a low-pass fractional order filter, and obtaining a digital filter; optimizing the parameters of the digital filter according to the objective function by using a QPSO algorithm; filtering the non-stationary signal by using the optimized digital filter; extracting characteristic parameters from the filtered non-stationary signals; and performing fault type identification by using a fault diagnosis model based on the characteristic parameters. According to the method, discretization processing is carried out on the low-pass fractional order filter, and parameters of the low-pass fractional order filter are optimized, so that the filtering effect on non-stationary signals is remarkably improved. By means of the optimized digital filter, fault features, such as amplitude entropy, fractional order spectrum kurtosis and dominant frequency components, in non-stationary signals can be extracted more accurately. The support vector machine is adopted as a fault diagnosis model, and the radial basis function is selected as a kernel function, so that the accuracy and robustness of fault recognition are further improved.

The invention provides a numerical feature embedding method based on a scale perception radial basis function and a situation awareness method for the health degree of power equipment, and the method comprises the steps: obtaining a logarithm of to-be-coded numerical data x, and obtaining a base number D and an index L; performing RBF (Radial Basis Function) expansion on the base number D to obtain the expression of the base number D; carrying out soft sub-bucket distribution on the index L to obtain a distance from the index L to each soft sub-bucket, taking the distance as a coefficient of the soft sub-bucket, and weighting each soft sub-bucket according to the coefficient of the soft sub-bucket to obtain representation of the index L; and converting the representation of the index L into two numbers by adopting a gated linear layer network, and scaling the representation of the base number D to obtain the coded representation of the numerical data x.

The application belongs to the technical field of load identification of structural health monitoring, and provides a concentrated load positioning method based on principal stress constraint and strain gradient trajectory identification, which comprises the following steps: arranging strain sensors in a monitoring area of a planar structure plate and collecting strain data of measuring points, constructing a strain field inversion function based on a radial basis function and establishing an error optimization function containing a fitting error term and a smooth constraint, introducing a principal stress direction constraint and a gradient projection smooth constraint to form a comprehensive error function and solving to obtain an optimal strain field distribution, then calculating strain gradient vectors of each grid node and tracking a gradient trajectory through a gradient descent method, and finally performing an iterative clustering analysis based on distance statistics on a trajectory end point to output a cluster center as a concentrated load application position identification result. The application can improve the concentrated load positioning accuracy, enhance the anti-noise capability, and realize fast, stable and large-sample-free load position identification.