43 results about "Hybrid kernel" patented technology

The invention relates to an electroencephalographic and electromyographic information automatic intention recognition and upper limb intelligent control method and system, which are used for rehabilitation treatment of the upper limb of a stroke patient, an electroencephalographic and surface electromyographic signal collector collects and processes the electroencephalographic and surface electromyographic signals of the patient in real time, a mixed kernel function formed by weighting a polynomial kernel function and an RBF kernel function weights is used to perform fitting and prediction, soas to more accurately identify and monitor the motion intention of the patient, and judge the corresponding degree of rehabilitation, according to which a corresponding rehabilitation training strategy is adopted. When the rehabilitation degree of the upper limb of the stroke patient is low, passive training control is adopted. When the rehabilitation degree of the upper limb of the stroke patient is high, active, assisted and resistive control modes are adopted. The hybrid kernel function support vector machine model provided by the invention has better learning ability and generalization performance, high prediction accuracy and good control performance, and the prediction result meets the index requirements of a rehabilitation robot for stroke patients.

The invention discloses an SAR image classification method for an SVM classifier by using a hybrid kernel function based on wavelet properties, belonging to the technical field of image processing and mainly solving the problem that effectiveness in image characteristic extraction is not sufficient. The method comprises the following steps: firstly, imputing training and testing sample images, and normalizing and marking the sample images; secondly, decomposing the normalized sample images, respectively extracting a plurality of characteristics from various decomposed sub-zones, and storing the characteristics in terms of a structural body of T1 x r; thirdly, according to the characteristics of the sub-zones, constructing the hybrid kernel function based on wavelet properties for the SVM classifier (see the formula on the lower right side, wherein, in the formula, Xi and Xj respectively indicate an ith sample image and a jth sample image, i and j are both less than or equal to 1, xik and xjk respectively indicate the kth chacteristics of the ith sample image and the jth sample, and Rhok is a convex combination coefficient; and fourthly, finishing the classification of the image characteristics by optimizing the convex combination coefficient in the hybrid kernel function. The method has the advantage of high image classification recognition rate and can be used for machine learning and mode identification.

The invention discloses a production method for an injection molding machine workpiece production line. A computer control system integrating a system decision, monitoring and learning functions is established for the characteristics of injection molding machine workpiece manufacturing systems of double production lines so as to realize automatic intelligent optimized production; a system control task is finished by a centralized controller and a stimulation optimizer commonly, monitoring of a production system and running of a control instruction are finished by PLC (programmable logic controller) and a production line state monitor; and a value iteration learning thought is realized by a reinforcement learning device through a support vector machine with a hybrid kernel function, so that the stimulation optimizing capability of a system stimulator can be improved by flexibly utilizing offline learning, the total running time of the control system is shortened, the running quality of the control system is improved, the production cycle of the manufacturing system is shortened, and the equipment utilization rate is increased.

The invention discloses a new method of gas path fault diagnosis of aeroengine based on hybrid particle swarm optimization twin support vector machine (SVM) algorithm. In view of the fact that the gas path fault is the most frequent in the whole aeroengine failure and the strong demand for the intelligent diagnosis method in the field, and TWSVM has the advantages of faster theoretical calculation and better response to the sample imbalance problem, and adopts the TWSVM algorithm to study aeroengine gas path fault diagnosis. In this paper, the hybrid kernel function is introduced to improve the performance of the kernel function, so that the TWSVM algorithm can better balance the generalization ability and good learning ability. In addition, the optimal parameters of TWSVM are optimized by using HPSO, and the fault classification model is obtained. The aeroengine gas path fault diagnosis is realized with high precision.

The invention discloses a support vector regression machine model selection method, and brings forward a novel support vector regression machine model selection method based on a hybrid kernel function and volume Kalman filtering, for solving the problem of model selection of a support vector regression machine. The hybrid kernel function is selected as a kernel function of the support vector machine, a combination coefficient of the hybrid kernel function is embedded into a superparametric state vector composed of a kernel function parameter and a regression parameter, accordingly, the problem of model selection is converted into a problem of state estimation of a nonlinear system, and then based on the high-performance volume Kalman filtering, superparametric estimation is carried out. A simulation experiment shows that the method brought forward by the invention, compared to a volume Kalman filtering support vector regression machine model selection method of a single kernel function and a genetic algorithm, has the following advantages: the generalization capability of a decision regression function obtained through the method is greater, and the prediction precision is higher.

The invention discloses an SVDD radar target one-dimensional distance image identification method based on density weight and a hybrid kernel function and belongs to the radar target identification field. The K-type kernel function has strong generalization capability, extraction of global characteristics of training data is facilitated, complex index operation of a radial basic kernel function isavoided, and properties of small calculation complexity of a polynomial kernel function and high approximation precision of the radial basic kernel function are realized. The radial basic kernel function has quite good local characteristics, the K-type kernel function and the radial basic kernel function are combined to replace a kernel function of a traditional SVDD algorithm, moreover, a localdensity algorithm based on interception distance is employed to calculate local density between a support vector and training sample data in the high-dimensional kernel characteristic space, accordingto density distribution, the shape of a super-closed ball is adjusted, and identification performance of radar one-dimensional distance image single type targets is effectively improved.

A machine learning-based hybrid kernel function indoor positioning method disclosed by the present invention comprises the steps of firstly establishing a fingerprint map library, and taking the fingerprint map library as a training data set; then utilizing a weighted summation method to construct a hybrid kernel function, and using a support vector regression algorithm and a v-folding cross validation method in the machine learning algorithms to train to obtain an optimal weight coefficient and an optimal kernel parameter of the hybrid kernel function; and finally under the premises of the optimal weight coefficient and the optimal kernel parameter, carrying out the offline training learning on the training data set to obtain the fitting functions of an x coordinate and a y coordinate separately, and then utilizing the fitting functions to carry out the online learning on an RSSI value received by a target, thereby obtaining the position coordinate of the target. Compared with the conventional indoor positioning algorithms of a BP neural network algorithm, a k-nearest neighbor algorithm, a linear kernel function algorithm, a polynomial kernel function algorithm and a Gaussian kernel function algorithm, the positioning precision of the algorithm of the present invention is higher.

The invention discloses a random forest classification method used for coronary heart disease data classification and based on a kernel extreme learning machine and parallelization. Sampling with replacement is performed on a coronary heart disease sample set by using a Bootstrap method so that different coronary heart disease data training subsets and test subsets are generated to be used for base classifiers; the kernel function of the hybrid kernel form is used as the kernel function of the kernel extreme learning machine so that the influence of the kernel type on the performance of the classification model can be reduced; model training is performed on the kernel extreme learning machine by using the coronary heart disease data training subsets and performing testing is performed on the base classifiers by using the test subsets, cyclic judgment is performed by using the mode of sorting and particle swarm optimization and the optimized new base classifiers are regenerated and thebase classifiers having poor classification performance are eliminated and substituted so that the objective of enhancing the overall classification performance can be achieved; and the random forestmodel is formed and then the classification result is selected by using a relative majority voting method.

A system includes a multi-process application that runs on primary hosts and is checkpointed by a checkpointer comprised of a kernel-mode checkpointer module and one or more user-space interceptors providing at least one of barrier synchronization, checkpointing thread, resource flushing, and an application virtualization space. Checkpoints may be written to storage and the application restored from said stored checkpoint at a later time. Checkpointing may be incremental using Page Table Entry (PTE) pages and Virtual Memory Areas (VMA) information. Checkpointing is transparent to the application and requires no modification to the application, operating system, networking stack or libraries. In an alternate embodiment the kernel-mode checkpointer is built into the kernel.

The invention discloses an expressway traffic event detection method based on a hybrid kernel correlation vector machine, and the method comprises the steps of constructing an expressway traffic event detection initial variable set according to the change characteristics of upstream and downstream traffic flow parameters of a traffic event; learning minority class sample information by adopting a conditional generative adversarial network, training a generator to generate minority class supplementary samples, and balancing data distribution; screening key variables out through variable importance measurement of an XGBoost algorithm; establishing a combined kernel function based on a local Gaussian kernel and a global polynomial kernel; taking the key variables as input, and training a multi-kernel relevance vector machine model; and optimizing parameters through an improved fruit fly optimization algorithm to obtain an optimal model. According to the invention, the traffic incident detection rate is improved, the traffic incident occurring on the expressway is detected in time, time is won for road emergency rescue, casualties and property loss in the event are reduced, and meanwhile, technical support is provided for road traffic safety risk early warning.

The invention provides an MMC (modular multi-level converter) sub-module open-circuit fault detection method based on a hybrid kernel support tensor machine, and the method comprises the steps: takingthe convex combination form of a radial kernel function and a polynomial kernel function as a hybrid kernel function; performing the sub-module open-circuit fault diagnosis of an MMC through a support tensor machine, and performing the training and testing of the collected data through combining with the AC side current and loop current data characteristics obtained during the normal operation and fault operation of the MMC; taking the AC current, a current envelope mean value and the change characteristics of a loop current among three phases as the fault judgment basis, and expressing a fault diagnosis result in a simple mode so as to timely detect the fault, thereby avoiding the accident potential troubles caused by the delayed detection to a system.

The invention discloses an air conditioning system sensor fault detection method and device and electronic equipment. The method comprises the following steps: determining system evaluation indexes, building a target function, and therefore a fitness function is determined; selecting a Gaussian radial basis kernel function and a polynomial kernel function to construct a vertical air conditioning system sensor fault detection hybrid kernel function matrix, and establishing a hybrid kernel function-based KPCA mathematical model for describing the operation state of the air conditioning system and sensor fault detection; and optimizing the hybrid kernel function parameters by using a multi-objective particle swarm algorithm to obtain fault detection optimization of the air conditioning systemsensor. According to the invention, the multi-target particle swarm optimization algorithm is introduced to solve the problem of fault detection optimization of the air conditioning system sensor, the hybrid algorithm is improved to solve the target problem, certain advantages are achieved, and fault detection optimization of the air conditioning system sensor can be achieved.

The invention belongs to the field of network flow prediction, and particularly relates to an SDN (Software Defined Network) flow control method based on a fuzzy C mean value and a mixed kernel least square support vector machine, which comprises the following steps of: converting non-stationary SDN flow data into a stationary time sequence component by adopting discrete wavelet transform; processing the stationary time sequence component to obtain amplitude signals of a high frequency band and a low frequency band; clustering the amplitude signals of the high and low frequency bands by adopting a fuzzy C-means algorithm; an optimized self-adaptive mixed kernel least square support vector machine prediction model is adopted to predict the clustered components respectively; reconstructing the prediction results of all the components to obtain a prediction result of the SDN network data traffic; according to the method, a fuzzy C-means algorithm is utilized, a membership mechanism is introduced, time sequence components are divided into a high-frequency low-amplitude component, an intermediate-frequency middle-amplitude component and a low-frequency high-amplitude component according to amplitude-frequency characteristics of the time sequence components, and accurate prediction is provided for subsequent classification prediction.

The invention belongs to the technical field of power systems. The invention discloses an electric power system short-term load prediction method based on hybrid kernel function adaptive fusion. Kernel functions are selected from the local kernel function library and the global kernel function library respectively, weight variables are allocated to form a mixed kernel function, the weight variables and parameters of the kernel functions are put together to be combined into a new parameter state vector, and a nonlinear parameter estimation model is established; and then estimating a parameter state by using high-order cubature Kalman filtering based on a parameter estimation model, so that a local kernel function and a global kernel function can be adaptively fused, and a trained neural network is used to predict a power load. According to the power system short-term load prediction method based on hybrid kernel function adaptive fusion, the optimal neural network hybrid kernel functionfusion coefficient is selected, and the load prediction precision is improved.

Methods for sorting haploid maize kernels in haploid induction crosses are provided. A method of sorting haploid kernels can include capturing near-infrared (NIR) spectra and applying a general multivariate statistical model to the acquired NIR spectra to discriminate haploid kernels from diploid-hybrid kernels mixed therein. NIR spectra can be collected on a single kernel using a high-throughput apparatus such that the amount of time required to analyze individual kernels can be significantly reduced in comparison to existing NIR technology and in comparison to traditional manual sorting.