41 results about "Gaussian radial basis function" patented technology

The invention belongs to the field of wireless communication. In order to decrease the difficulty of measurement and the number of measurement and increase the accuracy of prediction, the technical scheme adopted by the invention is a support vector machine-based cabin interior path loss prediction method, which includes the following steps: distributed MISO (multi-input single-output) data is measured, and MISO is a multi-input single-output system; MISO path loss is calculated; the path loss PL data of each connection is adopted as training sample data, eight path loss values around a certain position as a center are adopted as eight-dimensional input variables xi in a training sample set (xi, yi), the path loss value of the position is adopted as an output variable yi, a support vector machine regression algorithm is adopted to process the training sample set data (xi, yi), and the Gaussian radial basis function is adopted as a kernel function in the regression model of the support vector machine regression algorithm. The support vector machine-based cabin interior path loss prediction method is mainly applied to cabin interior path loss prediction.

The invention relates to a method for predicting performance indexes of a drafting link in a carbon fiber precursor production process, in particular to a method for predicting performance indexes of a drafting link in a carbon fiber precursor production process based on a least square support vector machine (LS-SVM) optimized by a particle swarm optimization (PSO) algorithm improved by a power law. The method comprises the following steps of selecting a six-stage draft ratio of the drafting link in the carbon fiber precursor production process as feature information, performing linear function normalization, and establishing an input sample dataset; determining main performance indexes, including linear density, precursor strength and breaking elongation rate, influencing carbon fiber quality, performing logarithmic function normalization, and establishing an output sample dataset; and building an LS-SVM model according to the input and output sample datasets, adopting a Gauss radial basis function (RBF) as a kernel function of the LS-SVM, and selecting an optimal penalty factor C and a kernel function parameter sigma by using PSO. The PSO process is improved according to the power law, so that the optimization speed can be greatly increased and accurate prediction is realized.

The invention belongs to the crossed technical field of computer mode identification and nano material, and relates to a low-dimensional nano material identification method based on an SEM image. The method comprises the following steps of: (1) preprocessing a known nano material SEM image sample; (2) performing two-dimensional wavelet transformation on the preprocessed image to get sub-image matrixes on different frequency bands; (3) extracting characteristics of the sub-image matrixes on each frequency band, and taking a statistical value of each sub-image matrix as a characteristic value for representing surface texture of the nano material; (4) according to the characteristic value, taking a Gaussian radial basis function as a support vector machine kernel function to find an optimal hyperplane between any two classes, and creating a classification model for different classes of nano materials; (5) extracting a texture characteristic value of the known nano material SEM image sample, and identifying the unknown nano material by voting according to the classification model obtained in the step (4). The low-dimensional nano material identification method based on the SEM image represents and distinguishes different nano material structure types more accurately and effectively, and has the advantages of high accuracy, strong expansibility, high degree of automation and the like.

The invention provides a construction method and a prediction method of a woody plant leaf trait and photosynthetic characteristic model based on the DNA methylation level, and belongs to the technical field of biological analysis. The present invention selects important characteristic variables that reflect geographical location differences based on random forests, obtains seven leaf characteristic variables by screening, determines the optimal number of clusters, and uses the improved FCM clustering algorithm to obtain each group of clustered leaf samples; according to the correlation between variables The importance of the enzyme cut combination obtained from the gradient boosting tree is used to obtain the important enzyme cut combination in each group of clustered leaf samples; the DNA methylation level of the enzyme cut combination is used as a regression variable, and the LS is constructed based on the Gaussian radial basis function ‑SVM regression prediction model; input DNA methylation levels of important enzyme cleavage combinations to accurately predict leaf shape factor, leaf area and net photosynthetic rate. The method is used to predict the phenotypic characteristics and photosynthetic characteristics of woody plants, and simultaneously screen woody plant individuals with excellent traits.

A characterization method for optical free-form surfaces based on Gaussian radial basis functions is disclosed, by clarifying the expression of Gaussian radial basis functions, obtaining the data point set to be fitted to the optical free-form surface to be fitted, and normalizing the data to be fitted , Calculate the gradient vector according to the normalized data to be fitted, analyze and process the gradient vector, divide the sub-aperture, set the number of Gaussian radial basis function bases, obtain the number of Gaussian radial basis functions in each sub-aperture, and obtain the Gaussian radial basis function The total number of basis functions, the center point of the Gaussian radial basis function uniformly distributed in the sub-aperture, the optimal value range of the coefficient A, the final fitting effect, and the final total number of basis functions are specified, so that complex optical free-form surfaces can be processed High-precision characterization can meet the needs of modern optical system design, processing and testing. This method is simple to calculate, easy to implement, and has strong surface shape adaptability. It is suitable for any caliber and can realize high-precision characterization of optical free-form surfaces.

The invention discloses a state feedback control method of a soft robot based on a dielectric elastic body actuator, which uses virtual work simulation to establish a dynamic control model of a dielectric elastic body actuator, and then controls the unknown system function in the dynamic control model Constructing a Gaussian radial basis function neural network approximator, and by correspondingly designing its parameter update rate, the parameters of the Gaussian radial basis function neural network approximator are dynamically adjusted online to realize the online approximation to the unknown system function, Finally, a state feedback intelligent controller embedded with the Gaussian radial basis function neural network approximator is constructed in a state feedback control mode, which is used to dynamically adjust the control input of the dielectric elastomer actuator online so that the state tracking error converges to zero , so as to achieve the control goal of soft robots, to enhance the control accuracy and response speed of the control system, and to improve the adaptability and intelligence of soft robots based on dielectric elastomer actuators.