13 results about "Generalized inverse" patented technology

The invention discloses a six-dimensional force / torque sensor decoupling system and method based on a width neural network, and the method comprises the steps: in a data collection and preprocessing unit, carrying out the null drift correction and normalization processing of a collected voltage signal through a weight type calibration platform and a data collection card, and forming a network model training data set; carrying out model training on the established six-dimensional force / torque sensor decoupling model of the width neural network, and solving a mapping matrix from a hidden layer to an output layer by adopting an importance score-based neuron pruning algorithm and a Moore-Pengos generalized inverse to obtain optimal model parameters; and finally, inputting the voltage signal subjected to data preprocessing to obtain a real-time output vector, and performing reverse normalization processing to obtain a decoupled six-dimensional force / torque vector. According to the method, precise decoupling of the six-dimensional force / torque sensor is achieved, and the inter-dimensional coupling error is remarkably reduced.

The invention discloses an unmanned aerial vehicle state estimation method, a readable storage medium and a navigation device, and belongs to the technical field of unmanned aerial vehicle navigation. The method comprises the following steps: establishing a linear state space model with multi-cluster measurement noise, modeling the measurement noise as multivariate Gaussian distribution, and introducing a measurement noise covariance matrix coefficient; joint prior updating is carried out based on posterior information of a previous moment, and prior estimation of a state, a state covariance, a measurement noise covariance matrix coefficient and a generalized inverse Gaussian (GIG) distribution parameter is obtained; performing pre-clustering on the measured values; online adaptive clustering is realized by using an EM algorithm, and unknown outdoor scene noise can be dynamically identified and divided; joint posterior updating is carried out, fixed point iteration is not needed, and therefore an updated measurement noise covariance matrix is obtained; updating the state and outputting the unmanned aerial vehicle state. According to the invention, through precise noise modeling, online clustering and adaptive adjustment of the measurement noise covariance matrix, the positioning precision and system robustness of the unmanned aerial vehicle are significantly improved.

The present invention provides a stable control method for the libration of an electrodynamic tethered satellite. Compared with existing control methods based on optimization theory, this control method has an analytical form, a small computational effort, and can handle the effects of control current saturation nonlinearity and unknown system disturbances. The method comprises: establishing orbital dynamics equations based on the six elements of the vernal equinox; establishing attitude dynamics equations based on in-plane and out-of-plane angles; obtaining control equations describing the libration of the tethered satellite through dimensionless transformation; designing an auxiliary dynamics system to compensate for the saturation nonlinearity of the control current; using a radial basis function neural network to approximate multiple disturbances, such as model perturbations and external interferences, and estimating the weight matrix and approximation error to compensate for the disturbances; designing an analytical control law for the current in the electrodynamic tether; correcting singular terms in the analytical control law using a dynamic scaling generalized inverse method; and controlling the libration process of the electrodynamic tethered satellite system using the analytical control law.

This invention relates to the field of radar target detection technology, and provides a robust method and apparatus for radar extended target detection under weighted generalized inverse Gaussian clutter. The invention uses a clutter prior information vector and a moment estimation order vector to estimate the clutter parameter vector; based on the training sample matrix, the data matrix to be detected, and the estimated values ​​of the clutter parameter vector, it estimates the inverse expectation of the texture component; based on the training sample matrix, the data matrix to be detected, the signal steering vector, and the estimated values ​​of the inverse expectation of the texture component, it constructs a test statistic; and based on the test statistic, it determines the target state. This invention solves the problems of radar extended target detection failing to meet real-time processing requirements and exhibiting poor robustness.

The invention provides a multi-star sensor attitude fusion calculation method and system, and the method comprises the steps: S1, calculating the projection of an optical axis of a star sensor j in a spacecraft body system according to an installation quaternion from a measurement coordinate system of the star sensor j to the spacecraft body system; s2, according to the quaternion from the inertial system of the star sensor j to the spacecraft body system, calculating the projection of the optical axis of the star sensor j under the inertial system; and S3, according to the vector sum, in combination with a Moore-Penrose generalized inverse theory of the matrix, calculating to obtain a spacecraft multi-star sensor fusion attitude conversion matrix. The invention aims to fully utilize the attitude information of all the star sensors carried by the spacecraft to carry out multi-star sensor attitude fusion calculation when the quantity of the star sensors carried by the spacecraft is more than 2, so as to realize high-precision spacecraft attitude measurement.

The invention discloses a reflector surface shape active correction method and related equipment, and the method comprises the steps: constructing a parameterized finite element model of a reflector, the model comprising a plurality of active correction points; acquiring the initial displacement of each node of the mirror surface when the correction force is not applied to the reflector; sequentially applying a unit correction force to each active correction point, obtaining the displacement of each node of the mirror surface after each application of the unit correction force, calculating the difference between the displacement and the initial displacement, and constructing a sensitivity matrix reflecting the mapping relation between the correction force and the node displacement based on the difference; based on the sensitivity matrix, calculating sensitive characteristics of each node, distributing weights for each node according to the sensitive characteristics, and constructing a weight matrix based on the weights of each node; on the basis of the weight matrix, a target correction force acting on each active correction point is obtained through calculation by adopting a weighted generalized inverse method; and applying the target correction force to the finite element model or entity of the mirror to correct the surface shape of the mirror surface. According to the invention, more accurate and efficient active correction of the surface shape of the reflector can be realized.

The application provides a phenotype prediction method and system based on feature reduction and generalized inverse technology, which adaptively fuses linear and nonlinear effects, constructs a new model of adaptive fusion of linear and nonlinear effects of prediction factors for crop phenotype prediction, and evaluates the importance of different effects on the phenotype through the method of adaptive weight adjustment, so as to realize the function of comprehensively considering the influence of linear main effect and nonlinear relationship in crop phenotype prediction. When solving the model, the feature reduction is performed on the training data set, and the model is quickly and effectively solved by using the generalized inverse, so that the determination efficiency of the phenotype value of biological materials is improved. The learning set and the test set obtained by randomly dividing the experimental data set for multiple times are used for model learning, so that the system error of the model is reduced, and the stability of the phenotype prediction result is improved. The effectiveness of the application for phenotype prediction is verified based on DNA molecular marker genotype data and metabolite-based intermediate omics data.

The invention relates to the technical field of radar target detection, and provides a radar extended target robust detection method and device under weighted generalized inverse Gaussian clutter. The method comprises the following steps: estimating a clutter parameter vector by using a clutter prior information vector and a moment estimation order vector; estimating a reciprocal expectation of a texture component according to the training sample matrix, the to-be-detected data matrix and the estimated value of the clutter parameter vector; constructing test statistics according to the training sample matrix, the to-be-detected data matrix, a signal steering vector and the estimated value of the reciprocal expectation of the texture component; and determining a target state according to the test statistics. According to the method, the problems that the radar real-time processing requirement cannot be met and the detection robustness is relatively poor during radar extended target detection are solved.

The invention discloses a structural displacement response topological optimization design method and device, and relates to the technical field of structural dynamics. The method comprises the steps that a structural finite element model is established, boundary conditions and external load excitation are applied, an optimization model with displacement amplitude minimization as a target and volume fraction and anti-resonance frequency as constraints is constructed, after parameter initialization is completed, the sensitivity of the displacement amplitude and the volume fraction is calculated in an iterative mode, and the maximum displacement amplitude is obtained. Solving an anti-resonant frequency characteristic value sequence through a modal acceleration method, tracking a constrained anti-resonant frequency in combination with a modal confidence criterion, solving the sensitivity of the constrained anti-resonant frequency to a design variable by using Moore-Penrose generalized inverse, inputting all parameters and the sensitivity into an MMA optimization algorithm, judging the convergence, if the convergence is met, terminating iteration to obtain an optimal design variable vector, and if the convergence is met, ending iteration to obtain an optimal design variable vector; and structure displacement response topological optimization is realized. The problem that in the prior art, when displacement minimization topological optimization is carried out, an anti-resonance point is prone to convergence in advance to form a middle density gray area is solved.

The application discloses a structural displacement response topology optimization design method and device, and relates to the technical field of structural dynamics. The method comprises the following steps: a finite element model of a structure is established, boundary conditions and external load excitation are applied, an optimization model with the minimum displacement amplitude as the target and the volume fraction and the anti-resonance frequency as the constraints is constructed, after the parameter initialization is completed, the sensitivities of the displacement amplitude and the volume fraction are iteratively calculated, the anti-resonance frequency eigenvalue sequence is solved by using the modal acceleration method, the constrained anti-resonance frequency is tracked in combination with the modal assurance criterion, the sensitivities of the anti-resonance frequency to the design variables are solved by using the Moore-Penrose generalized inverse, all the parameters and the sensitivities are input into the MMA optimization algorithm, the convergence is judged, and if the convergence is satisfied, the iteration is terminated to obtain the optimal design variable vector, and the structural displacement response topology optimization is realized. The method solves the problem that the existing technology is prone to falling into the anti-resonance point and converging in advance to form an intermediate density gray area when displacement minimization topology optimization is performed.

The application provides a high-resolution target highlight extraction method in a reverberation background, belongs to the field of underwater acoustic signal processing, and particularly relates to the field of active sonar recognition research. In order to improve the performance of highlight extraction, the difference between the time-frequency domain reverberation and target echo correlation is used to model the two as a low-rank matrix and a non-low-rank matrix. In the Bayesian framework, the low-rank matrix is modeled, and the Laplace and generalized inverse Gaussian mixture distribution prior is given to the non-low-rank part to enhance the robustness of matrix recovery. After the Bayesian inference is completed through MCMC sampling, the time-frequency matrix formed by the target echo after reverberation suppression is subjected to RLSTFRFT processing to improve the time-frequency resolution, and the performance of highlight extraction is comprehensively improved. The simulation and experimental results verify the effectiveness of the method, and the performance is obviously improved compared with the existing method. The achievement realizes the performance improvement of highlight extraction, and provides a technical foundation for underwater target recognition.

The invention discloses an indoor line series fault arc detection method based on multi-feature fusion, and the method comprises the steps: collecting a current signal at a main line of an indoor line, carrying out the normalization preprocessing of the current signal, and extracting a time domain, a frequency domain and multiple types of features of the time domain and the frequency domain, so as to comprehensively reflect the non-stationary features of the current signal. For samples under different working conditions, redundant features are removed by using an improved feature selection algorithm in combination with Pearson correlation analysis, and a final feature set with strong discrimination capability is constructed. Furthermore, an extreme learning machine model is introduced, rapid modeling and efficient classification are achieved based on a random initialization weight and a generalized inverse solution method, and high detection precision and real-time performance can be kept under complex working conditions and noise interference. The method has the advantages of being comprehensive in feature expression, efficient in model training, high in detection accuracy and high in robustness, the missing report rate and the false report rate of the indoor line series fault arc can be effectively reduced, and the safety and the reliability of an electrical system are improved.

The present application relates to the field of ship structure safety evaluation, and discloses a large-area distributed random ice load frequency domain identification mathematical model ill-posed elimination method, first, by establishing a finite element model containing the hull plate, the rib and the longitudinal structure, the ice load is converted into an equivalent concentrated force model, and multiple point strain response measuring points are arranged on the surface of the structure, then, frequency response data is obtained by applying a simple harmonic excitation to form a frequency response function matrix, and the high ill-posed frequency is optimized by a multiple extension search algorithm, the sub-matrix with the smallest condition number is screened out, the frequency domain identification model is reconstructed, finally, based on the optimized frequency response function sub-matrix, combined with the structure response spectrum and the inverse problem theory, the generalized inverse method is used to solve the ice load power spectral density function. By optimizing the frequency response function matrix through the multiple extension search algorithm and the generalized inverse method, the stability and identification accuracy of the model are significantly improved, and the ill-posed problem caused by the high ill-posed frequency in the existing method is solved.