711 results about "Radial basis function" patented technology

A method of building predictive statistical models provides a dedicated aggregation module for each transactional record source. Each aggregation module aggregates the transactional records using a neural network function to produce a scalar output which can then be input to a traditional modeling function, which may employ either logistic regression, neural network, or radial basis function techniques. The output of the aggregation modules can be saved, and updated aggregation values can be updated by processing new transaction records and combining the new transaction values with the previous output values using a blending function. Parameters of the neural network in the aggregation module may be calculated simultaneously with the parameters of the traditional modeling module.

The invention discloses a method for recognizing a road traffic sign for an unmanned vehicle, comprising the following steps of: (1) changing the RGB (Red, Green and Blue) pixel value of an image to strengthen a traffic sign feature color region, and cutting the image by using a threshold; (2) carrying out edge detection and connection on a gray level image to reconstruct an interested region; (3) extracting a labeled graph of the interested region as a shape feature of the interested region, classifying the shape of the region by using a nearest neighbor classification method, and removing a non-traffic sign region; and (4) graying and normalizing the image of the interested region of the traffic sign, carrying out dual-tree complex wavelet transform on the image to form a feature vector of the image, reducing the dimension of the feature vector by using a two-dimension independent component analysis method, and sending the feature vector into a support vector machine of a radial basis function to judge the type of the traffic sign of the interested region. By using the method, various types of traffic signs in a running environment of the unmanned vehicle can be stably and efficiently detected and recognized.

The invention discloses a face mage super-resolution method which fuses global features and local detail information. The invention can synthesize a high-resolution face image according to a low-resolution face image based on a sample image. Firstly, a local maintaining mapping algorithm and a radial basic function return algorithm are combined together to get a global high-resolution face image; then a neighborhood reconstruction method is adopted to synthesize a high-resolution face residual image block and consequently form a high-resolution face residual image by combination; finally, the high-resolution face residual image is overlapped to the high-resolution face image to obtain a final super-resolution effect. The technology provided by the invention can synthesize the clearer high-resolution face image, improve the recognition of the face image and have important application significances on video monitoring, face recognition and other aspects.

The invention relates to the technical field of computer application engineering, in particular to a method for using an improved neural network model based on particle swarm optimization for data prediction. The method includes the steps of firstly, expressing data samples; secondly, pre-processing data; thirdly, initiating the parameters of an RBF neural network; fourthly, using the binary particle swarm optimization to determine the number of neurons of a hidden layer and the center of the radial basis function of the hidden layer; fifthly, initiating the parameters of the local particle swarm optimization. By the method for using the improved neural network model based on particle swarm optimization for data prediction, the number of the neurons of the hidden layer of the RBF neural network model can be determined easily, RBF neural network performance is improved, and data prediction accuracy is increased. In addition, the improved neural network model based on particle swarm optimization is low in model complexity, high in robustness and good in expandability.

An architecture for minimizing calibration effort in an IEEE 802.11 device location measurement system. The calibration technique is based upon a regression function that produces adequately accurate location information as a function of signal strength regardless of gaps in the calibration data or minimally available data. The algorithm takes a set of signal strengths from known room locations in a building and generates a function giving (x,y) as a function of signal strength, which function may then be used for the estimation of new locations. Radial basis functions, which are simple to express and compute, are used for regression. The fact that the algorithm maps signal strength to continuous location makes it possible to skip rooms during calibration, yet still evaluate the location in those rooms.

A method for hole-filling in 3D models includes identifying vertices adjacent to hole boundaries in a mesh of points on a digital image and constructing a signed distance function based on vertices adjacent to hole boundaries. A Radial Basis Function is fit based on the constructed signed distance function and evaluated on a grid, which include the hole. The points on the hole surface are extracted and meshed to fill the hole.

The invention discloses an unmanned aerial vehicle (UAV)-unmanned ground vehicle (UGV) combined formation cooperative control method, comprising the following steps: step 1, establishing nonlinear dynamical models of unmanned vehicles in UAV-UGV combined formation; step 2, processing the nonlinear dynamical models of a UAV and a UGV via equivalent transformation, and taking acceleration as a common control target quantity, obtaining a unified control model taking acceleration as a control input in the combined formation; step 3, establishing a ground-air combined formation structure based on a virtual pilot to obtain a stable control signal for the UAV-UGV combined formation and obtain an error model of the combined formation, wherein the control signal is acceleration obtained in step 2 as a common control target quantity; and step 4, designing a UAV-UGV combined formation controller by adopting a RBF (Radial Basis Function) network algorithm according to the control model, the error model and the acceleration serving as a control signal and a control target quantity at the same time, so that the combined formation is stable and reliable.

The invention relates to three-dimensional (3D) image processing, in particular to rapid human face expression animation generation technique based on movement capture data, and discloses a human face expression animation generation method. The human face expression animation generation method includes the following steps: one, movement capture of facial expression of a performer; two, generation of a human face texture image of a facial image; three, human face deformation by using of a radial basis function; four, personalized human face model area division; five, personalized human face model subarea deformation and integration; and six, human face animation generation. The human face expression animation generation method effectively overcomes the shortcomings that manufacturing of 3D human face animation is high in cost, low in fidelity, tedious in operation and low in efficiency in the prior art.

The invention discloses an adaptive control system based on a radial basis function (RBF) neural network sliding mode control for a micro-electromechanical system (MEMS) gyroscope, and the system comprises a gyroscope and a control circuit, wherein the control circuit comprises a sliding mode controller and an RBF neural network; the difference of displacement of the three-axis gyroscope in the directions of three coordinate axes x, y and z and displacement of a reference model is taken as the input of the sliding mode controller. In the adaptive control system, an adaptive sliding mode control method is applied in controlling the gyroscope, so as to improve the stability and reliability of the system; and the RBF neural network is adopted to carry out adaptive learning on upper boundary of uncertain interference, thus reducing the influence of measurement error and external interference, effectively lowering the occurrence of buffeting, and achieving a better control effect.

The invention relates to a PID (Proportional Integral Derivative) control method for an elastic integral BP neural network based on RBF (Radial Basis Function) identification, which comprises the following steps: determining the structure of the BP neural network and determining an initial value; determining the structure of an RBF identification network; sampling; positively calculating the BP network and calculating the output of a PID control system; calculating the RBF identification network; revising the parameters of the identification network; and revising the weighting coefficient of the BP netural network. The invention has the advantages that the BP neural network is combined with the traditional PID control to form an intelligent neural network PID control system; no accurate mathematical model is required to be established; the change of the parameters of the controlled course, the parameters of the automatic setting control and the parameters of adapting to the controlled course can be automatically identified; and the method is an effective measure for solving the problems of difficult parameter setting, no real-time parameter adjustment and weak robustness of the traditional PID control system.

The invention discloses a fault location method based on a residual and a double-stage Elman neural network for a hydraulic servo system, comprising the following steps of: obtaining the input/output signals of the hydraulic servo system in a normal working state, an electronic amplifier fault state and a leakage fault state, training a fault observer by virtue of the input/output signal in the normal state, and obtaining a real-time residual signal by the fault observer at first, and then training a state follower in real time and on line to obtain a network connection weight corresponding to the real-time signal, and training an RBF (radial basis function) fault locator by using the time-domain characteristic value of the residual signal and the network connection weight as the training input samples of the RBF fault locator. Both of the fault observer and the state follower are realized by the improved Elman network. Whether the system has a fault or not at present can be judged by comparing the time-domain characteristic value with a fault threshold, and the type of the fault can be obtained by the fault locator. The fault location method disclosed by the invention realizes fault location for the hydraulic servo system, and has high location accuracy and engineering applicability.

A method for identifying a set of key frames from a video sequence including a time sequence of video frames, comprising: extracting a feature vector for each video frame in a set of video frames selected from the video sequence; defining a set of basis functions that can be used to represent the extracted feature vectors, wherein each basis function is associated with a different video frame in the set of video frames; representing the feature vectors for each video frame in the set of video frames as a sparse combination of the basis functions associated with the other video frames; and analyzing the sparse combinations of the basis functions for the set of video frames to select the set of key frames.

The invention discloses an enterprise power consumption load prediction method based on a k-means clustering radial basis function (RBF) neural network. The method includes steps: historical load data acquisition, meteorological data acquisition, date discrimination, neural network prediction, error calculation and correction, load curve drawing, and prediction data export. A prediction result is obtained by employing the neural network via the historical load data and meteorological factor input quantity, and correction is realized via an error correction module. Based on the requirement control technology of load prediction, with the combination of an industrial enterprise production plan and the condition of power consumption load usage, the system performs requirement control via a built-in requirement curve node determination method at a control point before the load prediction value reaches the maximum requirement, whether unnecessary loads are removed is determined, the current most appropriate energy-saving scheme is automatically selected, the maximum requirement is controlled in advance, over-load operation and even tripping operation can be effectively avoided, and safety and energy-saving production is guaranteed.

The invention relates to a method for rapidly detecting adulteration of olive oil, particularly relating to a method for detecting the adulteration of the olive oil by combing a near-infrared spectroscopy with a principal component analysis-radial basis function neural network method, and mainly being used for solved the technical problems that the suitable detection method does not exist at home and abroad, the detection time is too long and the detection process is cockamamie. The detection method of the invention comprises the following detecting steps: putting a sample in a 5mm-detection cell and carrying out spectrum acquisition by the near-infrared transmission spectroscopy, wherein the scanning range is 12000cm-1-3700cm-1, the resolution ratio is 4cm-1, and the number of times of the scanning is 32; taking the average value after each sample is repeatedly detected for 5 times; selecting the spectrum wave band within 12000 to 5390cm-1 to carry out pretreatments of baseline correction and vector standardization on the original spectrum; extracting the principal components for the pretreated spectrum data by a principal component analysis method; establishing a model of a radial basis function (RBF) neural network after the principal component is extracted; and acquiring the near infrared spectrum of a sample to be detected and carrying out forecasting by the established model. By using the detection method of the invention, the olive oil can visually distinguished from the adulterated olive oil.

The invention discloses a method for controlling a micro gyro based on a radial basis function (RBF) neural network sliding mode. Single-input single-output neural sliding mode control can be realized by using a switching function as the input of an RBF neural network, using a sliding mode controller as the output of the RBF network and using the learning function of the neural network; and a control effect can be achieved by integrating the advantages of sliding change structure control, an adaptive algorithm and the RBF neural network. The adaptive algorithm is used for adjusting the link weight of the RBF neural network in real time on line according to accessible conditions, so that a system finally achieves a sliding mode surface, completes tracking, and can adapt to sliding mode control strategies and timely correct and estimate all rigid errors, damping and the like; and the stability of a provided adaptive sliding mode controller exists according to the Lyapunov stability theorem, the system has good robustness, and digital simulation of the three-dimensional micro gyro proves that the method for controlling the micro gyro is valid.

The invention discloses a real-time prediction method of mine gas concentration in short and medium terms based on radial basis function neural network integration. The method comprises the following steps of: taking mine gas concentration data as a chaotic time series to construct a plurality of prediction sub-models of radial basis function (RBF) neural networks, and taking a weighted mean of synchronous prediction results of all prediction sub-models as an integrated prediction value to realize prediction model initializtion of RBF neural network integration; then realizing prediction of the gas concentration in the range of from a short term to a medium term through setting an integrated capacity parameter (the integrated capacity parameter is also equal to an RBF network prediction step-length); and obtaining a new prediction sub-model by utilizing an incremental training mode aiming at the characteristics that gas concentration information is continuously collected, and realizing updating of the RBF neural network integration according to a first in first out queue sequence so as to improve real-time prediction precision of the gas concentration, therefore, a proper compromise can be obtained between prediction range and prediction precision requirements, and the technical requirement on a mine gas information management system is satisfied.

The invention provides an intelligent identification method for a pipeline defect on the basis of an RBF (Radical Basis Function) neural network, comprising the following steps of: (1) obtaining a pipeline defect flux leakage signal and a pipeline defect outline as detection data; (2) building an RBF neural network; (3) training and testing the neural network; and (4) predicting the pipeline defect outline by the tested neural network. The pipeline defect outline comprises the length, the width and the depth of the pipeline. With the intelligent identification method, finite tests are carried out, thus a pipeline defect outline prediction model is built. A computer simulation test is carried out for scientific prediction, and the pipeline defect outline can be accurately and quickly predicted.

The invention discloses a robust neural network control system for a micro-electro-mechanical system (MEMS) gyroscope based on sliding mode compensation and a control method of the control system. The control system comprises a given trajectory generation module, a sliding mode surface definition module, a neural network controller, a weight adaptive mechanism module, a sliding mode compensator, an MEMS gyroscope system, a proportional-differential control module, a first adder and a second adder. The control method of the control system comprises the following steps of: establishing an MEMS gyroscope kinetic model based on a sliding mode surface, designing a controller structure, and designing an updating algorithm of a radial basis function (RBF) network weight, so that the trajectory of the MEMS gyroscope is tacked. By the control method, the influence of the unknown dynamic characteristic of the MEMS gyroscope and noise interference can be compensated on line, the vibration trajectory of the MEMS gyroscope completely follows a reference trajectory, and the anti-interference robustness and reliability of the system are improved; the updating algorithm of the network weight is designed on the basis of a Lyapunov stability theory, so that the stability of a closed-loop system is ensured; and a powerful basis is provided for expanding the application range of the MEMS gyroscope.

The invention discloses a patient movement demand-based assistance lower limb rehabilitation robot self-adaptation control method. By collecting the joint angle and joint angle speed signal of the lower limb of a patient in real time, the expected track self-adaptation tracking control is realized by a robustness variable-structure control method; then, by using a man-machine dynamics system model, the rehabilitation degree and the active movement ability of the patient are studied in real time by using a RBF (Radial Basis Function) neural network; the forward feed assistance of a lower limb rehabilitation robot is further estimated; next, the real-time assistance of the robot is subjected to self-adaptation attenuation according to the track tracking errors; the continuous self-adaptation patient rehabilitation demand-based assistance control is realized; finally, the tracks subjected to the patient rehabilitation demand-based assistance self-adaptation control correction are input into a lower limb rehabilitation robot joint movement controller; the on-line movement is performed; and the continuous and seamless patient rehabilitation demand-based assistance lower limb rehabilitation robot self-adaptation control is realized.