54 results about "Fuzzy rough sets" patented technology

The invention discloses a gas insulated substation (GIS) partial discharge online monitoring system and a fault mode identifying method thereof. The monitoring system comprises an ultrahigh frequency sensor, a signal preprocessing subsystem and a data processing subsystem, wherein the data processing subsystem comprises a monitoring and spectrogram display module, an off-limit alarm module and a mode identifying module; and a partial discharge signal acquired by the ultrahigh frequency sensor is transmitted into the data processing subsystem after being processed by the signal preprocessing subsystem so as to realize the monitoring and spectrum display of the partial discharge and the functions of off-limit alarm and fault mode identification. The system and the method have the beneficial effects that by adopting the GIS partial discharge online monitoring system and the fault mode identifying method thereof, the long-term online monitoring function on the partial discharge of the GIS is realized, and meanwhile, the fault type of the partial discharge can be identified well by utilizing the mode that a neural network identifying method based on a fuzzy rough set.

The invention discloses a short-term load predicting method based on a quick fuzzy rough set. The method comprises the following steps that firstly, electrical load data recorded by an electricity meter installed in a power grid are collected, and an initial attribute decision table is constructed; secondly, a fuzzy subordinate function of the condition attribute and the decision attribute is determined; thirdly, the attribute reduction is carried out according to the quick fuzzy rough set method, and the reduction condition attribute is obtained; fourthly, the reduction condition attribute serves as input data of a neural network to train normalized historical load data; fifthly, the neural network obtained through training is utilized for carrying out the short-term load prediction on an electric power system; sixthly, reverse normalization processing is carried out on the obtained normalization value of the maximum load of the prediction day, and a short-term electric power load prediction result is obtained and is the maximum load of the prediction day. The computing amount of the fuzzy rough set attribute reduction is small, the computing time is short, and the computing efficiency is improved.

The invention discloses an extraction method for influence factors of carbon exchange of an ecosystem and a system. The extraction method includes steps of 1), acquiring attribute data of carbon flux samples in a carbon flux data observation station; 2), inputting the attribute data, which are obtained in the step 1), of the carbon flux samples, selecting the optimal combination by the aid of proposed fuzzy rough and reduction integrated algorithm of shuffled frog leaping or quick fuzzy rough and reduction integrated algorithm based on importance, and finding an environmental factor set with the closest relationship with carbon flux; and 3), realizing modeling and stimulating for various environmental factors in the environmental factor set obtained from the step 2) by the aid of a neural network, and obtaining extraction rate of the environmental factors of the carbon flux. The extraction system comprises a data import module, a factor extraction module, an input module and an evaluation module. By the aid of the extraction method and the system, the range of correlation factors of the carbon flux in the researched ecosystem can be effectively reduced, research efficiency is improved, and inherent law among the environmental factors is found.

The invention relates to a method for extracting a classification rule based on a fuzzy-rough model. Since fuzzy boundaries of continuous attribute values are not considered in the conventional continuous attribute discretization method, a data mining rule is not refined or accurate enough and important data information is easy to lose during discretization. The method for extracting the classification rule comprises the following steps of: performing attribute fuzzification on continuous attributes in an information sheet by using a membership function in a fuzzy set; and extracting parameters such as approaching precision approximate measure, rough approaching precision approximate measure, approaching precision classification quality measure, approaching precision relative classification measure and the like by using a rough set in a fuzzy similarity relation so as to establish an approaching approximate-based fuzzy-rough set reduction algorithm to solve the classification rule. In the method, each continuous attribute is added into an attribute reduction set in a descending order according to importance until the reduction condition is met, and particularly, the attribute reduction can be quickly solved when multiple condition attributes are available.

The invention relates to a network security situation assessment method, and aims to provide a network security situation assessment method based on a fuzzy rough set. The network security situation assessment method based on the fuzzy rough set comprises the following steps: acquiring situation factors and situation levels to construct a decision table of the situation levels relative to the situation factors; and performing attribute reduction on the situation factors of decision rules in the decision table to obtain a reduced decision table, and making a decision on data needing to be judged in a current network through a KNN classifier by using the reduced decision table in order to obtain a situation level. Through adoption of the method, the problem that discretization of a real number continuous attribute is needed in a rough set method is solved. On the other hand, a rule aggregation way is adopted, thereby lowering the calculation complexity of a fuzzy rough set method. Meanwhile, a good decision result can be provided.

The invention provides a part supplier multi-target preferable selection method orienting bearing manufacturing enterprises. The degree of capacity of each expert is acquired from historical samples through a neural network training method by considering objective difference of the experience levels and the knowledge levels of different experts; the degree of capacity of the experts is introduced and then a multi-order fuzzy rough set integrated analytic hierarchy process with combination of an analytic hierarchy process in which fuzzy numbers replace accurate numbers and rough sets is provided so that combined processing of multiple experts for multiple part suppliers on the score result of a single indicator is realized; a multi-weight information integration model is established, and integration weights of the evaluation indicators are solved; and the part suppliers are ranked according to the indicator value of each evaluation indicator and the integration weights of the indicators of multiple part suppliers, and the optimal part supplier is obtained.

The present application provides a multi-sensor target recognition attribute reduction method and apparatus. The method includes the following steps that: sensor data are preprocessed, so that a plurality of target attribute data are obtained; for each target attribute datum, a K-means clustering method is used to determine a target clustering number corresponding to the target attribute data according to a preset rule on the basis of a preset category number parameter of target recognition, and data fuzzy processing is performed on a clustering result corresponding to the target clustering number; and a rough set algorithm is used to perform attribute reduction on a fuzzy processing result, so that an attribute reduction result is obtained. According to the multi-sensor target recognition attribute reduction method and apparatus of the present invention, a characteristic that the same attribute data of the same type of targets of the sensor data are just slightly different from each other, and the same attribute data of different types of targets of the sensor data are largely different from each other is considered; the K-means clustering method is adopted to perform clustering; the data fuzzy processing is performed on the clustering result; continuous data are discretized; and therefore, the limitations of a rough set in attribute reduction can be eliminated, and the recognition rate of fuzzy rough set-based target recognition can be improved.

The invention relates to the technical field of a space strategy supporting system in a geographic information system, in particular to a method for establishing a location selecting model of a mountain photovoltaic power station. The method comprises the following steps of conducting digital formalized description on a location factor and a corresponding index of the mountain photovoltaic power station according to the characteristics of the mountain photovoltaic power station; regarding a multidimensional location selecting index knowledge base, deriving a smallest location selecting property subset through a fuzzy rough set to replace an original property set on the basis that the location selecting classification capacity is not reduced, and obtaining a location selecting strategy list; extracting the corresponding property characteristic in the location selecting strategy list from a GIS, and establishing a membership degree function to evaluate and classify the station establishing potential of a specific geographical position. The method for establishing the location selecting model of the mountain photovoltaic power station can conduct quantitative relation derivation according to the situation that a plurality of micro and macro factors influence power generation amount of the power station to obtain a main influence factor. The space property relationship is analyzed based on a GIS data base, and direct strategy support to the location selecting of the mountain photovoltaic power station is provided.

The invention relates to an integrated classifier and a classification method thereof, and the classifier and the method are used for solving the problems of low speed and precision as well as biasing characteristics and nondeterministic polynomial of attribute subsets in the field of spatial raster data monitoring and classification. The integrated classifier and the classification method adopt an attribute division mode, combine training data subsets with a parallel computing technique, and can be applied to high-latitude raster data; and as the integrated classifier and the classification method adopt a fuzzy rough sets theory as a standard for parallel division of high-altitude attributes, each subset has independent characteristics, and the integrity of a strategy is maintained. Therefore, the integrated classifier and the classification method are applicable to discrete and continuous heterogeneous data and can be applied to the fields of remote sensing and geographical information systems.

The invention refers to the theory of fuzzy rough set and provides a mining method for fuzzy rough monotonic data based on inclusion degree. The method includes: realigning decision properties and condition attributes according to values; dividing realigned collections into intervals; setting decision rules according to membership function and inclusion degree of each interval; deciding relationships between the decision properties and the condition attributes to build fuzzy included monotonic depending relational models; mining preliminary relationships between the decision properties and the condition attributes via the relational models, setting decision filtering rules, and determining condition attribute reduction data collection and optimal data. Existing attribute reduction algorithm usually aims at limited data collection, the mining method for fuzzy rough monotonic data based on inclusion degree is capable of aiming at massive irregular data, and the larger the data volume is, the more obvious the superiority of the algorithm is.

The invention refers to the theory of fuzzy rough set and provides a mining method for fuzzy rough monotonic data based on interval average. The method includes: realigning decision properties and condition attributes; dividing realigned collections into intervals; deciding monotone according to each interval average; determining membership function values of the condition attributes; determining number of divisions according to circular division of the intervals to obtain function range of interference factors; setting filtering rules to filter unsuitable data so as to obtain reduction data collection and optimal data.

The invention discloses a short-term load prediction method based on a C-mean clustering fuzzy rough set. The method considers various types of influence factors influencing the short-term load prediction, collects the historical load data and the data of the load influence factors, performs the attribute reduction on the influence factors influencing the short-term load by using a fuzzy rough set, obtains a reduced environment attribute set influencing the short-term load, takes the attribute of the set as the input data and the short-term load as the output data to train a support vector machine model, and then uses the trained model for predicting the short-term load, so that the short-term load prediction method becomes faster and more accurate. According to the method, the problem that the membership function is selected in the fuzzy rough set due to the artificial subjective consciousness is solved, and the problem that the prediction speed and the prediction performance of the support vector machine are reduced due to the fact that an influence factor set is too redundant, is also solved.

The invention discloses a big data method based on granularity calculation in a cloud environment. The method comprises the following steps: (1) establishing a variable-precision fuzzy rough set modeloriented to hybrid data analysis; an extended ziarko variable-precision rough set thought is combined with a fuzzy rough set theory; wherein the innovation point of the variable-precision fuzzy roughset model is a determination rule of upper and lower approximate sets, information table elements are considered in the approximation of the upper and lower sets to evaluate the inclusion degree of the decision approximate set, and the elements are included in the approximate set with high enough inclusion degree; (2) a data roughness measurement method based on random entropy is provided, and aneffective roughness measurement technology is convenient to research; and (3) designing a mass data parallel attribute reduction acceleration algorithm based on granular calculation, fully combiningbig data analysis and processing with a cloud calculation platform, and adopting a model-data parallel research method to solve the problem of mass data and high-dimensional complex data attribute reduction.

The invention relates to the technical field of engineering construction site selection suitable area prediction, the invention particularly relates to a method for predicting a suitable area for engineering construction site selection, which comprises the steps of dividing influence index factors, dividing correlation analysis of each influence index factor, selecting mesh unit size, calculatingweighted information amount and determining information amount threshold value. The weighted information quantity calculating step comprises calculating the information quantity of each influence index factor; calculating a weighted information amount of each influence index factor according to the weight of the information amount and the reduction attribute of the geological element; calculatinga comprehensive weighted information amount of all influence index factors contained in each grid cell according to the weighted information amount. The invention combines the space weighting theory,adds the weight determined by the fuzzy rough set method into the traditional information quantity model, and greatly improves the prediction accuracy.

The invention discloses a calculating method for the journey time on the basis of a soft set. The calculating method comprises the steps that a traffic condition data sheet of a certain road section is built, wherein the sheet mainly comprises factors of speed, flow, environment weather, emergency and time; the mutual relation among the factors is obtained, and a fuzzy approximation space (U, R) is built, wherein U represents a set constituted by all the factors influencing the journey time, and R represents the fuzzy relation among the factors in U; the fuzzy rough set ([Q with a bar below]Ii, [Q with a bar above]Ii) of the fuzzy approximation space (U, R) represents the traffic condition of the road section, the threshold value set, occurring to the road condition, of the road section represents a certain type of traffic state of the road section through the fuzzy set Ii on the set U, and then the fuzzy rough set ([Q with the bar below]Ii, [Q with the bar above]Ii) is obtained to build a soft fuzzy rough set; the relevancy of ([Q with the bar below](t), [Q with the bar above](t)) and ([q with a bar below](t), [q with a bar above](t)) is obtained through a matching function, and then the traffic state of the road section is judged according to the magnitude of the relevancy; and the journey time is mapped according to the traffic state of a road.

The invention relates to a wind turbine generator output power prediction method which comprises the following steps: S1, acquiring meteorological data and fan historical output data, and constructinga meteorological matrix and a fan output matrix; S2, calculating the fuzzy membership degree of each attribute by using fuzzy C-means clustering; S3, performing attribute reduction on the meteorological matrix by using a fuzzy rough set method; S4, removing redundant samples from the reduced meteorological matrix by using a neighbor aggregation method; S5, creating a three-layer BP neural network, and training the neural network by using the data of the meteorological matrix from which the redundant samples are removed in the step S4 to obtain a prediction model of the fan output; S6, verifying the effectiveness of the wind power prediction model by using test data; According to the method, the number of samples of meteorological physical values is removed, the prediction precision of theoutput power of the wind turbine generator is improved, meanwhile, the calculation speed of a wind turbine generator output power prediction model is increased, and a necessary theoretical basis canbe provided for power supply management and economic dispatching of a power system.

The invention relates to the technical field of equipment maintenance support, and discloses a new research equipment spare part variety determination method. Through fuzzy rough definition and a spare part variety determination model of hesitant fuzzy rough set decision, a hesitant fuzzy decision information system of spare part variety determination, a numerical continuation method of risk preference coefficients, and attribute reduction and rule acquisition of improved inclusion degree calculation are constructed. The method comprises the following specific steps: calculating selection of all decision-making conditions, obtaining hesitant fuzzy decision-making information after condition attributes are recombined, calculating the inclusion degree of the condition attributes in the decision-making attributes in a hesitant fuzzy decision-making information table after the combination, deleting condition attributes item by item, calculating a new decision rule, if the new decision rule is not changed, removing the deleted condition attributes redundantly, and repeating calculation, so a maximum decision reduction set is obtained. The invention provides an extraction method for determining the variety of spare parts. The problem of decision information application under the condition of incomplete information is solved. The effect of the method is verified, and the method has wide application value and application scenes.

The present invention provides an interval value fuzzy rough set attribute selection method based on Gini index, comprising the following steps: Step 1, selecting an interval value decision system IVDS=(U, C∪D), where U is the universe of discourse and C is a conditional attribute Set, D is the decision attribute set, giving the similarity rate α and the stopping condition ε; step 2, use the RBD similarity to construct the similarity matrix of each object in the universe U in step 1; use the similarity rate α to obtain the universe U The α-similar class of each object ui relative to other objects uj in and other steps; this method introduces the Gini index into the rough set, defines the attribute importance formula, and proposes an attribute selection algorithm for interval values.

The invention discloses a spectral pattern recognition method for finely recognizing a spatial target. The spectral pattern recognition method comprises the following steps: acquiring spectral data ofvarious spatial targets by adopting a spectrograph; Preprocessing the acquired spectral data to remove noise interference; Performing data feature extraction on the preprocessed data; performing Moderecognition on data obtained after feature extraction, wherin a modeling method is information entropy weight fuzzy rough neighbor (Entropy Weight Fuzzy-rough nearest Neighbour, referred to as EFRNN)method .According to the method, concepts of an information entropy weight and a fuzzy rough set are introduced; The information entropy weight considers all information of the sample and quantizes the information, and introduction of the fuzzy rough set can avoid fuzzy uncertainty caused by overlapping and uncertainty caused by insufficient characteristics to a certain extent, so that the pattern recognition precision of similar samples is improved. The method has the advantage that parameters do not need to be preset, and accurate distinguishing can be carried out under the condition that samples are similar.

The invention provides a multi-core SVM training and alarming method, device and system for intrusion signal recognition, and relates to the technical field of intelligent security and protection, andthe method comprises the steps: obtaining an intrusion signal data set, and carrying out the normalization processing; selecting a plurality of basic kernel functions, determining the similarity between kernel matrixes corresponding to the plurality of basic kernel functions according to the plurality of basic kernel functions and the intrusion signal data set, and determining the kernel weight of each basic kernel function according to the similarity; determining a multi-kernel function according to the kernel weight; and determining a sample membership degree according to a fuzzy rough setmethod, performing multi-kernel SVM training and optimization according to the sample membership degree and the multi-kernel function, determining an optimized kernel weight and a Lagrange multiplierof an optimal solution, and completing training of the multi-kernel SVM. According to the method, the kernel weight is determined through alignment to construct the multi-kernel function, and multi-kernel SVM training and optimization are carried out in combination with calculation of the sample membership degree, so that the classification interval reaches the maximum, and recognition of different types of intrusion signals is more accurate.

The invention relates to an intelligent medical system, and provides an intelligent disease diagnosis system. The intelligent disease diagnosis system comprises a user terminal and a medical knowledgedatabase, and is characterized in that the user terminal is used for inputting symptoms into a system by a user or outputting a diagnosis result by the system; the user terminal is in two-way connection with a fuzzy rough set processing control module, the fuzzy rough set processing control module performs fuzzy processing on the symptoms inputted by the user and then carries out attribute reduction to obtain a conclusion; the medical knowledge database is used for storing a clinical symptom description knowledge base, a disease knowledge base, a treatment knowledge base and a historical record knowledge base, and the output of the medical knowledge database is connected with the fuzzy rough set processing control module and used for providing a calculation and analysis basis for the fuzzy rough set processing control module. The intelligent disease diagnosis system is simple in principle and convenient to use, enables patients to know the physical condition thereof at any time and place via a desktop computer or a notebook computer at home or in the community through the system, thereby preventing diseases or performing accurate preliminary diagnosis on the symptoms, and solvingthe problems of low doctor seeing efficiency and inconvenient doctor seeing of the patients.

The invention discloses a malicious domain name detection method and detection system based on multi-dimensional features and a fuzzy rough set, and provides a new scheme for solving the problems that an existing Android malicious software detection scheme based on domain name features cannot effectively detect malicious domain names and DNS domain name feature extraction is not comprehensive enough. According to the method, DNS domain name information is deeply analyzed from the angles of structure, language and statistics, totally 26 features are extracted, multi-dimensional features of domain name detection are expanded, the weight of a clustering center is dynamically adjusted by using an online incremental fuzzy rough vector machine algorithm, and finally the detection precision is improved.