267 results about "Radial basis function" patented technology

The invention belongs to the technical field of robot control, and particularly relates to a mechanical arm RBF network dynamic self-adaptive control method under the constraint of a time-varying mechanism, which comprises the following steps of: constructing a mechanical arm dynamic model, determining a system specified time convergence standard, combining a joint motion reference trajectory of a mechanical arm, defining a trajectory tracking error, and determining a mechanical arm dynamic model based on a dynamic nominal model. Constructing a stable robust control law of the nominal dynamical model under the specified time; and selecting a radial basis function, and designing an RBF network adaptive control law in a specified time to dynamically fit a comprehensive nonlinear disturbance term in the system. The problem that the convergence time of a traditional method is uncontrollable is solved, the RBF neural network is adopted to dynamically approach comprehensive disturbance, the network weight is updated online through the adaptive law of the time-varying gain, the anti-jamming capability is remarkably improved, a model driving method and a data driving method are combined, and the convergence time of the model driving method and the convergence time of the data driving method are greatly improved through a dynamic model decoupling and feedforward compensation strategy. And more accurate and low-delay trajectory tracking control is realized.

The invention relates to a causal effect dynamic estimation method and device and a medium. The method comprises the steps that an intervention perception convolution module is constructed through a longitudinal observation data set, weighting processing is conducted on time-varying covariables and intervention variables, and the intervention variables are embedded to generate multi-dimensional time sequence feature representation. A radial basis function is used for mapping intervention variables to generate high-dimensional feature representation, the high-dimensional feature representation is combined with time sequence features to construct a multi-layer intervention sensing module, and after feature dimension matching is adjusted, time sequence feature representation containing historical features and intervention information is output. And constructing a prediction model on the basis, predicting a result variable and a time-varying covariable of a next time step, minimizing loss by using an SAM optimization algorithm in combination with an observation value, introducing adversarial training to generate a sample, and iteratively optimizing model parameters. And performing anti-fact reasoning by using the model, generating anti-fact trajectories of each time step result variable and time-varying covariable, comparing a plurality of intervention schemes by combining different intervention variable sequences, outputting dynamic causal effect trajectory data, and realizing personalized causal effect estimation.

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 relates to the technical field of gas steel cylinder pressure control, in particular to a gas steel cylinder multi-stage pressure control method based on a self-adaptive threshold value, which can dynamically correct the influence of ambient temperature on a pressure reference value through a constructed pressure-temperature compensation function, eliminate measurement errors caused by temperature drift, and improve the accuracy of pressure control. A pressure fluctuation entropy value is calculated in real time based on a sliding window algorithm, a self-adaptive threshold value adjustment coefficient alpha is generated in combination with historical working condition database matching, a control threshold value is dynamically adjusted along with gas flow velocity fluctuation and equipment aging degree, and the pressure over-limit risk is reduced; according to the method, fuzzy neural network control is introduced in a critical adjustment stage through a multi-stage pressure adjustment strategy, an improved radial basis function dynamic updating mechanism can adapt to a pressure sudden change mode online, the control response speed is increased, and the steady-state error is controlled within + / -1.5%.

The invention provides a digital twinning application-oriented electrical equipment electromagnetic heat flow coupling rapid calculation method, and belongs to the technical field of electrical digital data processing.The method comprises the steps that firstly, a three-dimensional model of electrical equipment is acquired and preprocessed, and a full-order electromagnetic heat flow coupling calculation model is established and verified through a temperature rise test; generating an experimental point matrix by using a Latin hypercube sampling method, and constructing a current temperature power density relational data set; performing regional division on the power density field by applying a K-means clustering algorithm, and constructing an electromagnetic response surface model through a radial basis function; establishing a heat flow field order reduction model based on an intrinsic orthogonal decomposition technology, and extracting a dominant mode primary function; bidirectional coupling of an electromagnetic field and a heat flow field reduced-order model is achieved, an improved Lagrange multiplier method and a fixed point iteration method are adopted for processing the nonlinear coupling problem, finally, a software development kit supporting an open platform communication unified architecture protocol is packaged, and the electromagnetic heat flow coupling rapid calculation capacity needed by digital twinning application is achieved.

The invention discloses an ML-based rear subframe structure lightweight and modal optimization target design method, which comprises the following steps: (1) through three-dimensional modeling, statics analysis and modal analysis, constructing a mathematical model corresponding to the weight of a rear subframe and first-order inherent frequency simulation; (2) generating a population based on a Maximin criterion and a Latin hypercube, and establishing a radial basis function machine learning model; (3) generating candidate offspring individual vectors by adopting a Pbest-driven DPM evolutionary strategy based on an Eplison function; (4) constructing a minimum and maximum Pareto front lifting function to screen real offspring individual vectors; and (5) performing simulation evaluation on a real offspring individual vector, adaptively switching the reference vector type based on a reverse generation distance updating condition, returning to the step (3) until an optimization target meets a design requirement, and outputting an optimal optimization design parameter value. According to the method, the evolution direction is adaptively adjusted according to the population simulation result, and the optimization design effect on the two targets of lightweight and modal optimization is good.

The invention discloses a heat effect and stripe noise synchronous progressive correction method based on an RBF curved surface. The method comprises the following steps: acquiring a degraded image; performing gray processing on the degraded image to obtain a gray degraded image; performing guided filtering processing on the gray scale degraded image to obtain a filtered thermal radiation effect estimation image; constructing an RBF kernel matrix according to the thermal radiation effect estimation image and the RBF kernel function, and solving a weight coefficient in combination with a pixel gray vector of the thermal radiation effect estimation image; multiplying the solved weight coefficient by the RBF kernel matrix to generate an RBF thermal radiation effect curved surface; estimating stripe noise through a stripe operator based on the degraded image and the RBF thermal radiation effect curved surface; according to the RBF thermal radiation effect curved surface and the stripe noise, a potential clear image is restored through a synchronous progressive correction optimization algorithm. According to the invention, the thermal radiation effect and possibly accompanied stripe noise of the image can be simply and efficiently removed.

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.

Examples described herein generally relate to alerting metric baseline behavior change. The examples include performing at least one of a radial basis function (RBF) kernel procedure and an autoencoding procedure for a time-series data; determining whether one or more change points occur in a seasonal pattern of the time-series data based on at least one of the RBF kernel procedure and the autoencoding procedure; and transmitting, to a user, an alert indicating the one or more change points based on a determination that the one or more change points occur in the seasonal pattern of the time-series data.

The invention discloses a multi-disaster-type disaster internet-of-things time sequence adaptive anomaly detection method and system, and relates to the field of internet of things, and the method comprises the steps: S1, constructing an anomaly detection model; s2, acquiring a training data set; s3, training an anomaly detection model; s4, acquiring to-be-detected data; s5, analyzing the reconstruction structure of the to-be-detected data; s6, analyzing an abnormal score; the emergency disaster early warning system comprises an acquisition unit, a storage unit, a calculation unit and an early warning unit. A multi-scale time convolutional network and a self-adaptive spectrum feature module are fused, the characteristics of time sequence data in a time domain and a frequency domain are deeply mined, a gating memory mechanism is introduced, normal time-frequency features in the data can be accurately captured and enhanced, and the recognition capability is improved; by adding the radial basis function layer, the detection capability of the model on tiny anomalies is remarkably improved, so that the model can detect tiny abnormal changes more accurately.

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 provides a bearing small sample data expansion method and system based on a variational auto-encoder, and belongs to the field of deep learning and data enhancement. The problems that a traditional generation model has limitation in bearing small sample data, feature fuzziness and distortion are prone to occurring, and the data set quality is poor are solved. According to the method, a deep VAE framework is constructed, and a dimension reduction module, a data expansion module and a dimension raising module are used in a potential space; dimensionality reduction is performed on high-dimensional data by adopting a UMAP algorithm, so that the topological structure of the data is effectively reserved, and the extraction efficiency and quality of data features are improved; a Gaussian mixture model combining regularization and particle swarm optimization optimization is used for fitting distribution of scattered small sample data, new data with fusion features are expanded through sampling, and data diversity is increased; a radial basis function is used for nonlinear data dimension raising, new data can be ensured to be accurately mapped back to a high-dimensional space, meanwhile, the relation between features is reserved, and defect data with fusion features is reconstructed through a decoder.

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 relates to the field of dam effect quantity monitoring, in particular to a dam monitoring effect quantity prediction method based on mechanism and data dual drive, which comprises the following steps of: performing noise reduction preprocessing on monitoring data by adopting a wavelet noise reduction method; constructing a monitoring effect quantity long-term prediction model; constructing a short-time proximity precise prediction model; a Newton-Raphson optimization algorithm (NRBO) is used for optimizing network hyper-parameters of a physical constraint radial basis function (PIRBF) to construct an inversion agent model of finite element model parameters, a finite element calculation result is calibrated, and finally a hybrid model is constructed to realize long-term prediction of monitoring effect quantity. A deep learning model is constructed through an aurora optimization algorithm (PLO), a Transform architecture and a gated cycle unit network (GRU) to correct a long-term prediction model error, a deep learning model result is coupled to construct a short-term approaching prediction model, and short-term approaching accurate prediction of the effect quantity is realized.

The invention belongs to the technical field of life state monitoring and maintenance management of a pantograph carbon contact strip, and particularly discloses a pantograph carbon contact strip full-life cycle data management system, which comprises a multi-level gradient feature library, a pseudo edge filtering technology, multi-scale feature extraction and radial basis function classification, full-scale identification of defects such as cracks is realized, and the microcrack detection accuracy is improved; multi-source data collaborative acquisition and three-dimensional coordinate mapping are adopted, and self-adaptive sliding window analysis is combined, so that data integrity and timeliness under different working conditions are ensured; through impact energy parameter integration, pressure fluctuation index calculation and a thermal expansion correction coefficient, a partition life prediction model is established, and the wear prediction error of a key area is reduced; high-risk area automatic marking and maintenance work order generation are realized, the RFID tracing technology is combined, the maintenance efficiency is improved, and the service life is prolonged. According to the method, the defects of a traditional method in the aspects of defect detection, dynamic monitoring and service life prediction are effectively overcome.

The invention discloses a low-coherence interference demodulation method based on modal decomposition and a radial basis function neural network, and the method comprises the steps: carrying out the empirical mode decomposition of a filtered low-coherence interference signal, extracting the effective time-frequency domain features of each IMF and the mathematical statistics time domain features of the low-coherence interference signal, and forming a low-coherence interference signal feature data set; constructing and training a radial basis neural network, establishing a nonlinear model between low-coherence interference signal features and pressure, setting an input layer to correspond to a fusion feature vector, setting a hidden layer to adopt a radial basis function as an activation function, realizing nonlinear mapping by using a Gaussian kernel function, and outputting a layer to correspond to a pressure value; the network is trained through the feature data set, a mean square error is used as a loss function, a gradient descent method is adopted to optimize the weight and threshold of the network, and iterative training is carried out until the error converges; and inputting a low-coherence interference signal acquired by an optical fiber Fabry-Perot pressure demodulation device, and demodulating the signal through the trained radial basis function neural network model to obtain a corresponding pressure value.

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 discloses a short-term load prediction method, device and equipment for a virtual power plant, and relates to the field of load prediction. According to the method, firstly, a multi-objective optimization function covering prediction precision and model complexity is constructed, then, a composite method of a radial basis function (RBF) neural network and an improved non-dominated sorting genetic algorithm (NSGA-II) is set, and by combining the strong nonlinear mapping capability of the RBF neural network and the multi-objective optimization advantage of the NSGA-II algorithm, the prediction accuracy and the model complexity are improved. The multi-variable and multi-target problems in the virtual power plant load prediction are effectively processed, and the prediction precision and the calculation efficiency of the short-term load prediction of the virtual power plant are improved.

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 provides a terrain elevation map generation method and system based on radial basis function fitting, and the method comprises the steps: receiving historical point cloud information and odometer information, generating a local point cloud map, and carrying out the downsampling; dynamically generating a center point of the RBF based on the down-sampled local point cloud map and the ground fitting area; calculating a kernel matrix according to the down-sampled local point cloud map and the central point, and iteratively calculating the weight of the central point through GPU parallel acceleration to generate a terrain manifold; and calculating and outputting an elevation map through GPU parallel acceleration according to the terrain manifold. According to the method, the fitting speed is remarkably increased, the real-time performance of elevation map generation is ensured, and the map generation speed is greatly increased; the elevation information of the dynamic obstacle is updated while the elevation information of the static obstacle is reserved, and perception information is provided for movement, navigation and obstacle avoidance of the intelligent wheelchair; and an elevation map, a gradient map and a normal vector map with any resolution can be output.

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 provides a steering-by-wire control method and system based on particle swarm-sliding mode control and a fuzzy radial basis function, and the method comprises the steps: carrying out the adaptive approximation of an uncertain item and unknown disturbance of a state-space equation based on a radial basis function neural network in combination with an adaptive law, and obtaining the real-time estimation values of the uncertain item and the unknown disturbance; a sliding mode surface is obtained based on the wheel rotation angle error, the sliding mode surface is corrected and compensated based on the real-time estimation value, parameters of the sliding mode surface are optimized in combination with a particle swarm algorithm, and an optimized sliding mode surface is obtained; on the basis of a fuzzy logic controller, the optimized sliding mode surface and the change rate of the optimized sliding mode surface are converted into membership degrees of a fuzzy set for fuzzy logic reasoning, and the fuzzy set of control behaviors is obtained; obtaining a control signal based on the fuzzy set of control behaviors; and completing steering-by-wire control of the vehicle based on the control signal. According to the technical scheme, the response speed and the anti-interference capability of the steer-by-wire system can be improved.

The invention discloses an SF6 switch equipment fault diagnosis method and system based on decomposition components, and the method comprises the steps: firstly obtaining decomposition product data of SF6 switch equipment, and carrying out the standardization processing; and then, determining a fault type by using a pre-constructed SF6 switch equipment fault diagnosis model in combination with the standardized data. According to the model, a radial basis function is used as a kernel function, a support vector machine is used as a base classifier, and feature extraction and dimension reduction are carried out on standardized data through weighted principal component analysis. Meanwhile, parameters of the support vector machine are optimized by adopting an improved adaptive sparrow search algorithm. Through the WPCA-ASSA-SVM method, the accuracy and efficiency of SF6 switch equipment fault diagnosis are remarkably improved, a new solution and thought are provided for latent fault diagnosis based on decomposition component analysis, and development of the SF6 switch equipment fault diagnosis technology is promoted.