32 results about "Gibbs sampling algorithm" patented technology

The present invention relates to a small sample reliability evaluation method based on a Bayesian theory. The method comprises the following steps: (1) performing system initialization, and inputtingpre-test information; (2) classifying the pre-test information; (3) analyzing an existing conversion method of similar system information, and using the D-S evidence theory and the F-HS based algorithm to perform analysis respectively; (4) using a hybrid pre-test distribution model; (5) determining a Bayesian reliability model of field test information; (6) integrating the field test information with the pre-test information through the Bayesian method to obtain an effective distribution model; (7) using a Gibbs sampling algorithm to obtain a sample value of a post-test distribution function;and (8) performing evaluation and estimation on reliability parameters to obtain estimated values of the reliability parameters. The small sample reliability evaluation method based on the Bayesian theory proposed by the present invention shows the superiority in both the effect of pre-information conversion and the accuracy of small sample reliability evaluation.

The invention discloses a multi-document automatic abstract generation method based on phrase subject modeling. Multiple sample documents are subjected to word segmentation to obtain phrases and frequency of occurrence of the phrases, and the documents are expressed in the form of a phrase bag; joint probability distribution of the documents is calculated on the basis of an LDA subject model, the LDA subject model is converted into a phrase subject model, then a Gibbs sampling algorithm is used for estimating implicit parameters in the phrase subject model according to Bayesian probability, and lastly probability distribution of the subject in words is obtained; the tested documents are subjected to word segmentation, the subject weight and word frequency weight of obtained sentences are calculated and obtained, the final weight of the sentences is obtained by means of weighting calculation, and abstract content is generated according to the final weight. The method is more standard and precise, the relationship between different words is taken into consideration, the subject weight of the sentences is introduced, and the generation result better conforms to the practical essay abstract writing conditions of people after the subject weight of the sentences is introduced.

The invention relates to a network safety log template extraction method and device. The method comprises steps: firstly, original safety logs are subjected to data cleaning, and log information after filtering of time and IP addresses is obtained; secondly, the log information which does not containing time and IP addresses are subjected to clustering, and logs with similar formats are classified to the same class; thirdly, for logs in the each class, template words describing formats in the logs are extracted, and the template of the log format is obtained. Concretely, a DBSCAN algorithm or an OPTICS algorithm is employed to cluster logs with similar formats, and template words describing formats in logs are extracted by utilization of an LDA Gibbs sampling algorithm. The device comprises a data cleaning unit, an information clustering unit and a template extraction unit. Priori knowledge is not needed, a template of a network safety log format can be obtained automatically, the system load can be reduced, and operational efficiency and accuracy are raised.

The invention provides a processing method for text clustering, a server and a system. The method comprises the steps that one theme is randomly distributed for each word in texts of a preprocessed text set from a theme set, the texts in the text set with distributed themes are distributed to multiple second servers, the initial mapping relation of each word in the texts distributed for the multiple second servers is established, clustering results of the texts in the text set are determined according to the updated theme of each word of the texts distributed for the second servers and fed back by the second servers, and the updated theme of each word is sampled and calculated by the second servers based on an improved Gibbs sampling algorithm according to the initial mapping relation of the words on the second servers. The data volume processed by the second servers and memory consumption can be effectively reduced and network bottlenecks can be avoided by determining the word mapping relation and using a matrix of a dense data structure and the improved Gibbs sampling algorithm.

The invention relates to a theme analysis method and system based on kernel principal component analysis and LDA, and the method is characterized in that the method comprises the following steps: 1) obtaining a literature corpus, and carrying out the preprocessing of all articles in the literature corpus; 2) according to the preprocessed literature corpus, establishing a KPCA-LDA theme model; 3) performing theme analysis on articles in the literature corpus by adopting the established KPCA-LDA theme model, and determining text representation of the articles in the literature corpus; and 4) carrying out training and parameter estimation on the KPCA-LDA theme model by adopting a Gibbs sampling algorithm, solving parameters of the KPCA-LDA theme model, and generating a plurality of themes represented by words. The method and system can be widely applied to the field of text mining.

The invention discloses an improved-Gibbs-sampling-based Beidou satellite selection method comprising the following steps: calculating an azimuth and an elevation angle of a Beidou visible satellite at a monitoring point to obtain state matrixes of n Beidou satellite, and using a GDOP value as an objective function of Beidou satellite selection; and on the basis of an optimized Gibbs sampling algorithm, searching for a Beidou satellite combination enabling the objective function value to be optimized to obtain an optimal solution. On the basis of the adaptive sweeping type improvement strategy, a phenomenon of falling into the local optimization of the algorithm is avoided and convergence to the optimal Beidou satellite selection scheme is realized well. According to the invention, the optimized Gibbs sampling algorithm is applied to the Beidou satellite selection process and the inherent advantages, including low complexity, fast convergence speed and good performance of the Gibbs sampling algorithm are utilized fully, so that the quality and efficiency of the Beidou satellite selection are improved effectively. The improved-Gibbs-sampling-based Beidou satellite selection method has the broad application prospects.

The invention discloses a topic modeling method based on a word co-occurrence network. The topic modeling method comprises the following steps: constructing the word co-occurrence network according toa given corpus or text set; constructing a new document set according to the obtained word co-occurrence network; and inputting the obtained new document set into a Gibbs sampling algorithm of a standard topic model LDA to obtain a document-topic matrix and a topic-word matrix corresponding to the new document set. The method does not need to depend on any external knowledge, avoids the energy ofcollecting additional knowledge, and improves the result of the topic model only through the information contained in the data set.

The invention discloses a knuckle recognition method based on an infinite Dirichlet process mixed model, which is carried out according to the following steps: step 1, on the basis of the local Markov assumption, the learning problem of conditional random measure is transformed into a random clustering learning problem; step 2. Use the infinite Dirichlet process mixture model to describe the probability density, and express the number of clusters as a random state; step 3, use the Gibbs sampling method to iteratively learn the density structure in the form of hierarchical probability; step 4, based on the Dirichlet process mixture model The collapsed Gibbs sampling algorithm DPMM uses the sample set for model training and learning, and uses a fixed threshold to recognize the knuckles of the hand image. The invention refines the description of the biological structure of the hand, the detection result is stable and reliable, and the calculation efficiency is high.

The invention provides a subtopic mining method. The method comprises the steps that (1) a subject value of each term of each document in a corpus is initialized; (2) based on the current subject values of all the terms of all the documents, the probability of each term in each article coming from all subtopics and the probability of each term coming from a background module are calculated, and then a subject value is redistributed for each term in each article through a Gibbs sampling algorithm based on the calculated probabilities, wherein the probability of each term coming from the background module is calculated according to term distribution vectors, subjected to statics in advance, in the background module, and the term distribution vectors in the background module are constant from beginning to end in the iteration process; and (3) if iteration stop conditions are met, LDA subtopics are obtained according to current subject value information, and if not, the step (2) is returned to. Through the method, the topic mining effect targeting a feature article set can be remarkably improved.

The present invention relates to a small sample reliability evaluation method based on a Bayesian theory. The method comprises the following steps: (1) performing system initialization, and inputtingpre-test information; (2) classifying the pre-test information; (3) analyzing an existing conversion method of similar system information, and using the D-S evidence theory and the F-HS based algorithm to perform analysis respectively; (4) using a hybrid pre-test distribution model; (5) determining a Bayesian reliability model of field test information; (6) integrating the field test information with the pre-test information through the Bayesian method to obtain an effective distribution model; (7) using a Gibbs sampling algorithm to obtain a sample value of a post-test distribution function;and (8) performing evaluation and estimation on reliability parameters to obtain estimated values of the reliability parameters. The small sample reliability evaluation method based on the Bayesian theory proposed by the present invention shows the superiority in both the effect of pre-information conversion and the accuracy of small sample reliability evaluation.