51 results about "Entropy criterion" patented technology

The invention provides a method for rapidly and automatically identifying and removing ocular artifacts in an electroencephalogram signal and belongs to the technical field of biological information and the method is mainly applied to a process of acquiring and preprocessing the electroencephalogram signal. The method comprises the following specific steps of: carrying out discrete wavelet transformation on an acquired multi-channel electroencephalogram signal and an electro-oculogram signal to obtain multi-scale wavelet coefficients; using the wavelet coefficients connected in series as an input for analyzing an independent component, and rapidly acquiring the independent component by using a negative entropy criterion-based Fast ICA (Independent Component Analysis) algorithm; identifying the ocular artifacts through a cosine method, performing zero resetting on the independent component, and projecting the other components through ICA inverse transformation and returning to all electrodes of an original signal; and finally obtaining the electroencephalogram signal for removing the ocular artifacts through inversion of the wavelet transformation. By utilizing the method for rapidly and automatically identifying and removing the ocular artifacts in the electroencephalogram signal, the problems that an ICA method is poor in discrete effect and low in convergence rate when beingapplied to noisy electroencephalogram signals are solved, and the function of rapidly and automatically identifying and removing the ocular artifacts in the electroencephalogram signal is realized.

The invention provides a self-adaptive filtering method based on a maximum mixed cross-correlative entropy criterion. The method employs the maximum mixed cross-correlative entropy criterion. A cost function is composed of two different kernel bandwidths omega, and used for updating a weight vector w(n+1) so as to modulate a filter to obtain an ideal output signal y(n). The self-adaptive filtering method based on the maximum mixed cross-correlative entropy criterion provided by the invention is composed of two maximum mixed cross-correlative entropies with different kernel bandwidths. The method has the advantages of more rapid primary convergence rate and higher stability precision, and has the capability of rapidly tracing the system variation. The filter designed based on the method is easier to popularize and use in practical use.

The invention discloses a PCNN automatic segmentation method for a microscopic image of a traditional Chinese medicine. The PCNN automatic segmentation method comprises the steps of respectively establishing a CNN automatic binary image dividing algorithm which utilizes cross entropy segmentation criteria; establishing a traditional Chinese medicine microscopic image PCNN multi-value image automatic segmentation algorithm which utilizes maximizing mutual information as a segmentation object and utilizes mutual information entropy difference as a classification criteria, designing a vector pulse coupling neural network model, and realizing automatic segmentation on the microscopic color image of the traditional Chinese medicine through utilizing an index entropy criterion as a segmentation criteria; and establishing a traditional Chinese medicine microscopic image dividing algorithm in a multichannel or three-dimensional PCNN through utilizing a fuzzy index entropy as an optimized segmentation criterion. The PCNN automatic segmentation method has advantages of further improving objectivity, accuracy, repeatability and intelligent degree in quality evaluation of the traditional Chinese medicine, and providing a new approach for modernization of testing and analysis of the traditional Chinese medicine.

The invention discloses a segmentation method for an infrared thermal imaging image of forest fire. A method for searching two optimal thresholds through a genetic algorithm is adopted when a heating area and an abnormal heating area in the infrared thermal imaging image are respectively segmented in the prior art, so that the calculated amount is larger and the operating speed is rather low. According to the method, a minimum threshold and a maximum threshold are determined by utilizing the characteristics of the infrared thermal imaging image under a channel R and a channel G based on segmentation of the image through combining the genetic algorithm and a maximum entropy criterion, so that the range for searching the optimal single threshold through the genetic algorithm is shortened, the larger calculated amount which is generated when searching the optimal single threshold through the genetic algorithm is reduced, the speed for threshold segmentation of a target image is improved and the precision for threshold segmentation of the abnormal heating area is improved. The method can be widely applied to the field of security warning of the forest fire.

The invention discloses a limit learning machine method based on maximum center cross correlation entropy criterion, At first, that input train data of the system is processed by the limit learn machine model based on the maximum center cross-correlation entropy criterion, with a larger core width, the output of the model is obtained, the actual output of the system and the output of the model arecalculated by difference, and the error of the model is obtained. The mean or median of the error is used as the center of the kernel, the maximum center cross-correlation entropy criterion is used,the weight vector of the fixed point is iterated, and the output of the model is obtained by the weight vector after the iteration is converged many times. The method of the invention has good universality, is suitable for non-Gaussian system or signal processing process, and has the performance of satisfying both high precision and fast parameter selection, solves the problems of non-robustness to non-Gaussian noise under the minimum mean square error criterion and non-ideal precision under the maximum cross-correlation entropy criterion under non-zero mean noise, and the time-consuming problem of choosing the core width by cross-validation.

The invention discloses a multi-threshold image segmentation method based on a crossover mutation artificial fish swarm algorithm and mainly aims to solve the problem that in the prior art, information loss of a segmented image is serious. The method comprises the implementation steps that 1, an image is input, and pixel grayscale values at all image pixel points are acquired; 2, c thresholds are selected to segment the image into c+1 classes; 3, n artificial fishes are generated, and each artificial fish is a 1xc-dimension vector and represents a group of threshold possible solutions; 4, a fitness function made according to the kapur maximum entropy criterion is regarded as a goal, and a maximum value of the fitness function is searched for; and 5, a group of thresholds corresponding to the fitness maximum value found through search are utilized to perform image segmentation, the pixel points with the grayscale values in the same interval are classified into one class, and the segmented image is output. Through the method, the optimizing precision of the artificial fish swarm algorithm in the optimizing process is effectively improved, the image segmentation effect is improved further in combination with multi-threshold image segmentation, and the method can be applied to computer vision analysis.

The invention discloses a photovoltaic array hot spot detection method based on a PSO optimized PCNN, belonging to the photovoltaic power generation field. Firstly, the R component in RGB color spaceis used to process the image, and then the region of photovoltaic array is extracted by shape feature recognition. Secondly, the image is transformed from RGB to HSV color space, and the hot spot region of S component image in HSV space is segmented by PCNN algorithm. For a PCNN algorithm, the maximum entropy criterion is used to determine the number of iterations, and particle swarm optimization(PSO) is used to optimize its parameters, which simplifies the operation process and improves the effect of segmentation.

The invention provides a broadband spectrum sensing algorithm based on a maximum cross-correlation entropy criterion robust sparsity, and solves the problem of broadband spectrum sensing in a non-Gaussian noise environment. According to the method, on one hand, the non-Gaussian noise is suppressed by using the robustness of the Maximum Correntropy Criterion (MCC), so that the adverse effect of thenon-Gaussian noise on the spectrum sensing algorithm is solved; on the other hand, Correntropy Induced Metric (CIM) is introduced to serve as a sparse penalty term of broadband spectrum estimation, sparse estimation is carried out through the sparsity of a spectrum, and the estimation speed of a spectrum vector is increased.

The invention provides a Granger causality discrimination method based on a quantitative minimum error entropy criterion. According to the method, the coefficient and the order of a regression model are determined by adopting the quantitative minimum error entropy criterion and a Bayesian information criterion, a causality discrimination index is obtained by calculating the error entropy and coefficient, and the causality between two time sequences is determined according to a causality judgment standard. Compared with a traditional Granger causality discrimination method based on a minimum mean square error criterion, the method is more accurate in estimating coefficients of the regression model, the obtained error entropy is smaller, and the causality discrimination index can be more accurately calculated. Due to the adoption of a quantization method, the calculation complexity of the method is remarkably reduced. The method integrates the error entropy and the coefficient when calculating the causality discrimination index, which makes the calculation of the causality discrimination index more accurate and robust. Therefore, the Granger causality discrimination method based on the quantitative minimum error entropy criterion provided by the invention is more easily promoted and used in practical applications.

The invention belongs to the radar foresight super-resolution imaging technical field, and discloses a radar foresight super-resolution imaging method based on the maximum entropy criterion; the method comprises the following steps: using dual-channel foresight scanning radar echo signals to modeling; introducing the entropy as target distribution prior information, and building a maximum posterior imaging model according to the Bayes criterion; employing an improved conjugate gradient method to solve the imaging model. The method combines with sum and difference channel processing so as to effectively alleviate single channel deconvolution problem inherent pathosis, and uses the entropy as the target prior information so as to prevent the echo prior information inaccurate problems, thus breaking the limits on the orientation resolution by the antenna aperture; the method uses the improved conjugate gradient method to carry out iteration solving for the foresight imaging result, is high in efficiency, thus realizing engineering high resolution foresight imaging.

The invention discloses an optimal data fusion method suitable for a ballistic missile INS/CNS/GNSS integrated navigation system. The method comprises the steps of constructing an INS/CNS/GNSS integrated navigation system model; respectively introducing an adaptive fading factor and a maximum correlation entropy criterion in a time updating stage and a measurement updating stage of a generalized high-order CKF to carry out local state estimation on an INS/CNS subsystem and an INS/GNSS subsystem; and fusing local estimation of the INS/CNS subsystem and the INS/GNSS subsystem according to a minimum variance principle and a volume criterion to obtain global optimal state estimation. According to the method, the influence of process modeling errors and non-Gaussian measurement noise on state estimation can be suppressed at the same time, the adaptability and robustness of ballistic missile INS/CNS/GNSS integrated navigation are improved, and a globally optimal state estimation value is obtained.

The invention discloses a joint estimation algorithm of delay and amplitude attenuation in a complex noise environment, and belongs to the technical field of radio positioning. The joint estimation algorithm comprises the following steps: 1) setting a search range of the delay and a search step length of the delay in the case of a known received signal length N; 2) using the sinc function as a transverse filter of a weight coefficient, converting the estimation of the delay into the estimation of a finite impulse response (FIR) filter coefficient, and establishing an expression with a delay signal on this basis; 3) figuring out a cost function of joint estimation of amplitude attenuation and delay by using the maximum correlation entropy criterion; 4) establishing an expression of directlyestimating the amplitude attenuation and delay in one step; and 5) performing the joint estimation of delay and amplitude coefficients by using spectral peak search. The invention provides a high-precision adaptive delay estimation algorithm in a complex communication environment in the presence of impulse noise and amplitude attenuation. The algorithm takes an amplitude attenuation factor of a signal as a fixed point of the filter weight coefficient, and directly realizes the joint estimation of the attenuation coefficient and the delay in one step, thereby having important practical significance.

The invention discloses a spacecraft attitude determination method based on central error entropy criterion unscented Kalman filtering, and the method comprises the steps: building a nonlinear system for spacecraft attitude determination according to spacecraft measurement data and a spacecraft attitude dynamic model; according to the state and the state covariance of the spacecraft at the previous moment, generating a plurality of Sigma points by using a preset sampling mode, establishing a time updating transfer formula, and obtaining a one-step prediction state estimated value and a one-step prediction state covariance of the spacecraft at the current moment; according to the one-step prediction state estimation value and the one-step prediction state covariance, generating a plurality of Sigma points by using a preset sampling mode, and obtaining a one-step prediction value, an auto-covariance and a cross-covariance of the measurement output quantity of the spacecraft; and establishing a linearized regression equation of the spacecraft state based on a center error entropy criterion, determining a cost function of center error entropy criterion filtering, and obtaining the state and the state covariance of the spacecraft at the current moment. According to the method, the attitude estimation precision and robustness during non-Gaussian noise processing can be improved.

The invention belongs to the technical field of adaptive signal processing, and provides an adaptive system state estimation method based on the extended kernel recursive maximum correlation entropy criterion. Estimate and track. The method includes: 1) Proposing a mathematical model suitable for describing nonlinear, slowly time-varying systems and a cost function based on the maximum correlation entropy criterion; 2) Iterating the system state in the regenerating kernel Hilbert space; 3 ) uses the kernel technique to iteratively solve the system state in the input space to realize the adaptive estimation of the system state. Experiments prove that the performance of the algorithm of the present invention is good, and it has good application prospects in real engineering applications.

The invention discloses a gas pipeline parameter estimation method based on an improved ARUKF, and the method comprises the steps: building a state space model of a natural gas pipeline according to the mass and momentum balance conditions of fluid dynamics in the pipeline; applying the fading factor and the maximum correlation entropy criterion to a cost function of a state equation and a cost function of a measurement equation, and after a filtering equation is established, using an improved ARUKF algorithm estimating and updating the pressure and the flow value of the gas pipeline until theset parameter estimation precision or the maximum number of iterations is reached.The problem of influence of abnormal values in measurement information is effectively solved, the detection and estimation precision of the pressure and flow of the gas pipeline is improved, and the frequency of false alarm in the gas pipeline network is reduced, so that the cost loss and harm of parameter estimation errors to the environment and personal health are reduced.

The invention discloses an intelligent vehicle path tracking method based on a maximum correlation entropy criterion, and belongs to the field of trajectory tracking. The method comprises the following steps: constructing a vehicle dynamics model; converting the vehicle dynamics model into a system state model; performing discrete linearization processing on the system state model to construct anoutput model for forming a prediction time domain; constructing a path tracking model for solving the control increment delta uk based on a maximum correlation entropy criterion and a semi-square method; and solving the path tracking model to obtain a control increment based on the vehicle centroid speed v and the front wheel steering angle sigma f. According to the scheme, the path tracking modelis established by adopting the measurement of the maximum correlation entropy criterion, and the model can effectively suppress or eliminate the influence from noise or off-site points so as to realize stable path tracking of the vehicle.

The invention discloses a seismic wavelet signal extraction method based on the maximum joint entropy; a seismic wavelet estimate method in the exploration geophysics and the maximum joint entropy criterion in information theory learning are combined, and the maximum joint entropy criterion can replace the original least squares criterion, thus allowing an object function can better solve non-gauss noises. Compared with the conventional least squares criterion, the seismic wavelet signal extraction method based on the maximum joint entropy is better in robustness, higher in precision, and can better process the geology exploration data.

The invention discloses an ISAR image minimum entropy phase correction method and a high-precision phase compensation method. The existing method proposes a method of combining a Doppler center tracking method with an image entropy criterion, but the problem of too much calculation exists. The scheme disclosed in the invention provides the ISAR image minimum entropy phase correction method and thehigh-precision phase compensation method, an image evaluation function can be further optimized, an explicit expression is used for description, and the amount of computation can be further reduced.The problems that when the above steps in the prior art are used to optimize the image entropy method, the amount of computation is too large in actual application, and application to real-time ISAR imaging is difficult can be solved.

The invention provides a multi-convex combination adaptive filtering method based on maximum correlation entropy, the weight vector is updated by the maximum correlation entropy criterion, The L asynchronous adaptive filters are combined to modulate the output results in convex combinations, and the one of the L adaptive filters with different step sizes which has the largest step size ensures theconvergence rate of the multi-convex adaptive filters at the beginning of the filter, and then The other L-1 filters adjust the weight ratio according to the mixing coefficient at different time periods, thereby controlling the convergence speed and the steady state amount.. The invention not only improves the convergence speed, but also maintains a low misalignment amount, can more flexibly adjust the proportion of multiple step sizes, and improves the re-convergence ability and the tracking ability.

The invention, which belongs to the field of signal processing, discloses a relative-entropy-criterion-based external radiation source selection method of a passive MIMO radar and particularly relatesto a method for selection of a passive MIMO radar transmitting station so as to solve a problem of external radiation source selection in the field of radar signal processing technology. A matched filter outputs a plan of opportunistic irradiation source selection of a passive MIMO radar by using a relative entropy as a selection standard under two kinds of hypothesis testing. Therefore, an EI selection combination having an optimal or suboptimum detection performance can be obtained in a range allowed by resources, so that a problem of too high complexity in a receiver in the prior is solved.

The invention relates to a method for determining a relational imprint between two images comprising the following steps - the implementation of a first image and of a second image, - a phase of calculating vectors of similarity between tiles belonging respectively to the first and second images, the similarity vectors forming a field of imprint vectors, the field of imprint vectors comprising atleast one haphazard region disordered in the sense of an entropy criterion, - a phase of recording in the guise of relational imprint of a representation of the calculated field of imprint vectors. The invention also relates to a method for authenticating a candidate image with respect to an authentic image implementing the method for determining a relational imprint according to the invention.

The invention discloses a spacecraft attitude determination method based on central error entropy criterion volume Kalman filtering, and the method comprises the steps: building a nonlinear system according to spacecraft measurement data and an attitude dynamic model; according to the state and the state covariance of the spacecraft at the previous moment, using a Cholesky decomposition method and a preset volume point, solving a volume sampling point, carrying out state transfer, and obtaining a one-step prediction state estimated value and a one-step prediction state covariance of the spacecraft at the current moment are obtained; according to the one-step prediction state estimation value and the one-step prediction state covariance, using a Cholesky decomposition method and a preset volume point, solving a volume sampling point, transmitting measurement information, and obtaining a one-step prediction value, covariance and cross covariance of spacecraft measurement output quantity; and establishing a linearized regression equation of the spacecraft state based on a center error entropy criterion, determining a cost function of center error entropy criterion filtering, and obtaining the current spacecraft state and the state covariance. According to the method, the attitude estimation precision during non-Gaussian noise processing can be improved.