72 results about "Fuzzy subset" patented technology

The invention relates to a method for automatic fault diagnosis of a satellite and a diagnostic system thereof. The method comprises the following steps: dividing the satellite fault into a component level fault, a subsystem level fault and a system level fault; adopting a fault detection method based on a fuzzy set to fuzzify a precise sensor sampling value; defining different alarm weight values for fuzzy subset based on the membership grade belonging to each fuzzy subset; and determining whether to alarm or not by calculating the relation between the summation of the alarm weight values in a time window and an alarm threshold value. The method can effectively overcome the defects of a common fault detection method and effectively reduce the false alarm rate and the rate of missing report; and when certain fault is caused by a plurality of sub-faults, the method performs heuristic search according to the reliability of a system corresponding to each sub-fault and searches a sub-fault node corresponding to a system with low reliability preferentially. Besides, in order to adapt to the synergistic diagnosis between distributed satellites in the future, a synergistic interface is designed to provide diagnosis services for other satellites, thus the aim of the invention is achieved.

The invention discloses a sensor network trust evaluation method based on node behaviors and a D-S evidence theory, comprising the following five steps: 1) designing various trust factor strategies for nodes in a wireless sensor network; 2) setting trust factor weights according to network application scenes and simultaneously calculating the node behavior coefficient mu to obtain the direct trust value and multiple indirect trust value of an evaluated object; 3) calculating a fuzzy subset membership function for each trust value by utilizing the concepts of membership and linguistic variables of a fuzzy set theory, performing fuzzy classification on the various the trust values to form the basic confidence function of the D-S evidence theory; 4) calculating the evidence difference of thedirect trust value and the indirect trust values of the evaluated node and altering the weights of the indirect trust values; and 5) adopting the Dempster synthesis rule to obtain the comprehensive trust value of the evaluated node and a final basic confidence designated value according to the altered trust weights. The invention solves the problem of difficult identification of malicious nodes in a network and ensures the safety of network data transmission.

The invention discloses a gas path fault diagnostic method for a marine gas turbine based on a fuzzy Petri net (FPN), comprising the following steps of: (1) obtaining the operating standard value of the marine gas turbine; (2) based on an actually-measured value, calculating the membership grade of the actual operating parameter deviation belonging to the criterion deviation fuzzy subset by using a membership function; (3) defining the FPN in response to the characteristics of the fault diagnostic Petri net after determining the operating standard value and the deviation; and (4) determining a FPN reasoning algorithm for gas path fault diagnosis for the marine gas turbine based on related definitions of the transitional enabling and igniting rules. Through the gas path fault diagnostic method for the marine gas turbine based on the fuzzy Petri net (FPN), the defects of not strong university, wrong judgment and reasoning of the traditional method are overcome; it is actually proved that the problems of fuzziness and nondeterminacy in an gas path fault can be effectively solved by the gas path fault diagnostic algorithm for the marine gas turbine established by the gas path fault diagnostic method disclosed by the invention.

The invention provides a fracture-cavern type carbonate oil reservoir single well water injection oil substituting effect quantitative evaluation method. The method comprises the following steps that a hierarchical fuzzy subset is set up, quantitative evaluation of the membership degree of all grades of evaluation elements is conducted, and a membership matrix S is obtained; the weight function of all the evaluation elements is determined, and an elevation weight vector A is obtained; the elevation weight vector A and the membership matrix S are calculated by using a fuzzy transformation mathematic model, and a fuzzy comprehensive evaluation measure value vector quantity B is obtained; and a final comprehensive evaluation result is determined through the maximum membership principle. The hierarchical fuzzy subset comprises an evaluation element set U and an evaluation set V. The corresponding expression of the fuzzy transformation mathematic model is shown in the specification, wherein b is a membership degree belonging to a j grade evaluation effect, and ai is the weight value of an i elevation element; sij is a membership degree that the i elevation element belongs to the j grade evaluation effect; p is the number of elevation elements in the evaluation element set U; m is the dimension number of the evaluation set V.

The disclosed detect method for LED display stain based on machine vision comprises: acquiring some images of target display to filter noise and one polynomial curves that can show the data trend; subtracting the image by the polynomial curve to separate the pixel over threshold as the suspected stain target; selecting the contrast grade, area, edge parameter, position, gray scale evenness, and shape of the suspected target as input variables; selecting stain defect grade as output variable; dividing the input and output variables into some fuzzy subsets with membership grade function.

The invention discloses a river health evaluation method based on natural and social influence. The river health evaluation method comprises the following steps that (a) an evaluation index system is established, and an evaluation factor set is determined; (b) according to the established river health evaluation index standard and determined river and lake level-5 health degree, and each level corresponds to one fuzzy subset evaluation set; (c) a single-factor subordinating degree function is determined; (d) a fuzzy relation matrix is established; (e) fuzzy weight vectors of rating factors are determined; (f) fuzzy comprehensive assessment is performed; (g) vector analysis is conducted on fuzzy comprehensive evaluation results. By the adoption of the river health evaluation method, the comprehensive evaluation results can be obtained, health degrees of single indexes can be also provided, accordingly convenience is brought to diagnosis performed by a drainage basin manager on a drainage basin water environment, and an accurate and effective management measure is implemented.

The invention provides a proportional amplifier PID parameter self-tuning control method comprising the following steps: a real-time parameter value of an output end of the proportional amplifier is detected, and deviation and deviation change rate are calculated according to the real-time parameter value and a given parameter value; the deviation and the deviation change rate are fuzzified into a corresponding deviation fuzzy subset and a deviation change rate fuzzy subset; a PID parameter fuzzy subset is obtained according to the deviation fuzzy subset, the deviation change rate fuzzy subset and a PID parameter fuzzy inference mechanism; after the PID parameter fuzzy subset is de-fuzzified, a PID control parameter is obtained according to a PID parameter initial value and the de-fuzzified change value; output of the proportional amplifier is controlled according to the PID control parameter, the real-time parameter value of the output end of the proportional amplifier is detected again, and the steps are repeated until the real-time parameter value of the output end of the proportional amplifier is equal to the given parameter value. PID parameters are controlled in fuzzy and self-tuning ways so that the proportional amplifier can be self-adaptive to various application occasions. The invention also provides one type of proportional amplifier and a proportional electromagnetic valve.

The present invention discloses a photovoltaic cell MPPT simulation method based on fuzzy logic control. The method comprises design of a fuzzy logic controller and fuzzy logic control simulation; wherein the design of the fuzzy logic controller comprises: determining input/output amount and the structure of the controller, determining the fuzzy subset and the domain of the input/output amount, determining the subordinating degree function of the input/output amount and determining the fuzzy control rule, etc. Through combination of the control idea of the perturbation and observation method and the fuzzy logic control theory, the photovoltaic cell MPPT simulation method based on fuzzy logic control is provided; the simulation model is built to compared with and simulate the perturbation and observation method and the conductance increment method; according to the simulation result, the fuzzy logic control is very suitable for the MPPT control of the photovoltaic system so as to rapidly and accurately lock the MPP range, and the fluctuation is little after the system is stable.

The invention relates to the design of an embedded electromagnetic speed regulation coal cutter traction system based on fuzzy control, i.e. a controller in the traction system adopts fuzzy PID control, and current and speed are double closed-loop. The traction system comprises an embedded hardware circuit and design of a fuzzy controller. AT91SAM99260 acts as the core of the embedded hardware circuit, and the embedded hardware circuit comprises an SDRAM interface circuit, an underlying circuit, a power supply circuit and a thyristor module circuit. The design of the fuzzy controller comprises establishment of a fuzzy subset and a subordinating degree function, establishment of the fuzzy controller structure and design of a software part, and the design of the fuzzy controller is the control core of a speed regulation system. The triangle subordinating degree function is selected to perform fuzzification on input quantity of the electromagnetic speed regulation traction system. The knowledge base of the traction system is established firstly to confirm the control strategy of input quantity, then inference is performed on the input condition by utilizing a Mamdani fuzzy model, and then defuzzification is performed on the obtained output quantity via a method of weighted average, and finally construction of the fuzzy controller of the embedded electromagnetic speed regulation coal cutter traction system is completed via combination of the software design. The controller of the traction system adopts fuzzy PID control so that system response is rapid, anti-interference capability is high and speed regulation is stable.

The invention discloses a fuzzy-logic-based ice-covered power transmission line state estimation method, which comprises the following steps of: 1, determining and calculating a characteristic quantity of a specific estimated content in two aspects of the mechanical performance and the electric external insulation performance of a lead wire, a ground wire and a tower which serve as objects according to a monitoring parameter; 2, determining a fuzzy logic inference system and an input characteristic quantity and an output quantity thereof, and designing a membership function; 3, building the fuzzy logic inference system, designing a fuzzy rule, and inferring state fuzzy results of the mechanical performance and the electric performance; and 4, defuzzifying the fuzzy results obtained in the step 3 by a centroid anti-fuzzification method, and outputting a fuzzy subset with the maximum membership according to a maximum membership principle to obtain a state estimated result. The method has the advantages of comprehensive estimated content, scientific calculation mode, high accuracy and effectively prevented faults on a power transmission line.

The invention discloses a built-in PID fuzzy control method based on a PLC board card. Fuzzy control rules are determined through sampling data or experience values and are utilized to conduct fuzzification processing on a measurement error e and an error change rate ec to obtain the membership degrees of fuzzy subsets, membership assignment tables of the fuzzy subsets and parameter fuzzy controlmodels are established according to the obtained membership degrees of fuzzy subsets, fuzzy synthesis reasoning is applied to design a fuzzy matrix table for fractional order PID controller parameters, correction parameters are calculated to serve as inputs of the PID controller, values of three control parameters output by the PID controller are automatically adjusted according to the state of acontrolled object, the error e and the error change rate ec serve as inputs, the fuzzy rules are utilized to conduct self-adaptive setting on the output parameters of the PID controller, so that the controlled object is kept in a good dynamic and static state, a user only needs to input the corresponding control parameter values and does not need to compile PLC control programs, the operation difficulty is low, and the stability and reliability are high.

The invention discloses a data analysis method aiming at evaluation of a server hardware statement. Based on a fuzzy comprehensive evaluation method, the method is characterized in that a fuzzy comprehensive evaluation index system for hardware state evaluation is established from perspectives such as representativeness, completeness and applicability according to actual situations of hardware running; a CRITIC assignment method is used to determine weights of different indexes, so that a weight set of the evaluation indexes is constituted; a 4-grade scale is used to establish an evaluation grade set; single-factor fuzzy evaluation is carried out, and an evaluation fuzzy matrix is determined; an operator is used to conduct fuzzy comprehensive evaluation to compositional operation, so that fuzzy subsets on the evaluation grade set are obtained; and finally, a final fuzzy comprehensive evaluation result is determined. In comparison with the prior art, the invention provides a fuzzy evaluation model which is improved based on the CRITIC assignment method; and the model is applied in the evaluation of the server hardware state. In this way, the method can help a server management worker better master the hardware state, adjust resources in time and increase working efficiency.

The invention discloses an AGF method for evaluating the use performance of the runway pavement of a highway based on the safe use of an aircraft. According to four indexes, namely the pavement surface condition, the flatness, the anti-sliding performance and the bearing capacity, of asphalt pavement use performance comprehensive evaluation, a factor set O of a pavement evaluation index system evaluation object is constructed, a judgment matrix A is constructed, an index weight set W is calculated, the maximum characteristic root of the n-stage judgment matrix A is set as lambdamax, a consistency index CI is defined, a consistency ratio CR is calculated, and the total ranking weight, relative to the target layer, of all the layers is calculated; a gray method is used for calculating the grey weight value R, a weighting subset W and a single factor fuzzy subset R are used for compound operation to obtain a fuzzy synthetic evaluation matrix B, and an evaluation result M is calculated, wherein the result is excellent when M is more than or equal to 7.80, the result is good when M is more than or equal to 7.15 and less than 7.8, the result is just well when M is more than or equal to 6.60 and less than 7.15, and the result is poor when M is less than 6.60. The AGF method has the advantage that a quantitative judgment standard can be provided for the safety performance of the runway of the aircraft.

The present invention discloses a color space converting method based on a fuzzy theory, which comprises the following steps: input and output equipment is adjusted to guarantee the equipment to be normally used; according to color space needing to be changed, sampling points are selected, and the modeling data of the sampling points is acquired; input and output color space fuzzy subsets are divided, and fuzzy subset subjection degree functions are selected; at the same time, fuzzy control rules are established, each fuzzy control rule has a fuzzy implicit relationship correspondingly, and the combination of the fuzzy implicit relationships of all the fuzzy control rules is the chief fuzzy implicit relationship of the system; and the chief fuzzy implicit relationship of the system is clearly treated so as to obtain an output value. The color space converting method based on the fuzzy theory uses an artificial intelligence fuzzy set theory and fuzzy logical deduction, so that the conversion of the color space is completed. The method has the advantages of simple step and high data conversion precision.

The invention belongs to the technical field of battery energy storage and especially relates to an energy storage battery soft fault diagnosis method based on GA-Elman. The method comprises the following steps: carrying out acquisition and analysis on terminal voltage signals and state-of-charge signals under different degrees of attenuation; obtaining two feature vectors of terminal voltage deviation Devk and SOC punishment angle [theta]k; carrying out normalization processing on the feature vectors and serving the processed feature vectors as input of the GA-Elman neural network, and serving residual capacity Qk obtained after battery attenuation as output of the GA-Elman neural network; calculating relative inferiority degree of the Qk; determining four fuzzy subsets and membership functions thereof in a large-capacity battery energy storage system soft fault discourse domain according to the relative inferiority degree; and establishing a soft fault diagnosis model having fuzzy output and realizing fuzzy diagnosis of soft fault of the battery energy storage system. The method has higher accuracy, has battery soft fault level comprehensive judgment capability and points out a new direction for soft fault diagnosis of a large-capacity battery storage system.

The invention discloses a fuzzy control method based on adaptive domain partitioning, comprising the following steps: step 1, domain partitioning is carried out, wherein the change interval [x, y] of a controlled variable T is used as a domain interval, x and y are respectively the lower limit and upper limit of the controlled variable, the domain interval is divided into multiple sub intervals, and a fuzzy subset and each element (namely, a membership function of a fuzzy language variable) in the fuzzy subset are defined for each sub interval; and step 2, a controlled object is controlled by adopting a module control strategy and based on the fuzzy subsets and the membership functions in step 1. The fuzzy control method based on adaptive domain partitioning is easy to implement and high in flexibility, and can effectively improve the control effect.

The invention provides an action control method and apparatus, which belong to the field of artificial intelligence. The method comprises steps: the states of N dimensions of an artificial intelligence device are acquired; based on a state activated fuzzy subset and a control model for each of the N dimensions, a plurality of discrete decisions are obtained, wherein one state activated fuzzy subset refers to a fuzzy subset with the state membership degree not to be zero, each fuzzy subset includes a state interval corresponding to the same discrete decision in one dimension, the membership degree is used to indicate the degree of the state belonging to the fuzzy subset, and the control model is used to output a corresponding discrete decision according to an input state; based on the stateof each dimension and the membership degree between the activated fuzzy subsets, the plurality of discrete decisions are weighted and summed to obtain a continuous decision; and based on the continuous decision, the artificial intelligence device is controlled to execute a corresponding action. The outputted decision is a continuous quantity, smooth control on the artificial intelligence device can be ensured, and the smoothness of the action is ensured.

The invention aims at the characteristics that equipment system often has uncertainty and nonlinearity, and provides a fault predicting method which combines fuzzy nearness and a particle filter algorithm. The method provided by the invention comprises the following steps: describing a fault evolution process of a system by adopting a state-space model, and tracking and predicting the operating state of the system by utilizing the particle filter algorithm; designing a normal fuzzy subset which describes normal operation of the system and an abnormal fuzzy subset which describes abnormal operation of the system by utilizing a membership function; calculating a normal membership set of a predicting sequence solved by the particle filter algorithm; calculating the nearness of the normal membership set and the normal fuzzy subset, and calculating the nearness of the normal membership set and the abnormal fuzzy subset. When a real-time monitoring system is operated, if the nearness of the normal membership set and the abnormal fuzzy subset is greater than that of the normal membership set and the normal fuzzy subset, a fault is predicted; the method can predict the fault early, and is an effect fault predicting method.

The invention discloses a method for optimizing a robot control system based on NSGA-II fuzzy logic inference. The method comprises the following steps of analyzing the mobile robot control system, and determining the input-output volume and fuzzy subsets; and based on the NSGA-II fuzzy logic inference, optimizing the robot control system: preparing input data: generating the initial population ofa state quantity m based on an empirical data set and a state quantity domain; performing fast non-dominated sorting; selecting, crossing, and mutating state variables to generate a first sub-generation m1; evolving again to generate a second sub-generation m2 by using an elitism strategy, and merging the sub-generations and a parent generation for performing fast non-dominated sorting again; calculating the crowding degree and selecting suitable individuals to form a new parent generation, and performing multiple iteration to obtain an optimal solution set of objective functions; and selecting the weight of each objective function, and determining the optimal solution of the objective functions. The method converts the multi-input multi-output logic inference problem into an error optimization problem, obtains the optimal fuzzy solution, and enables a fuzzy controller to quickly and accurately approach the objective functions.

The invention discloses a load density index deriving method based on fuzzy maximum closeness theory, characterized by firstly introducing the fuzzy maximum closeness theory to derive the load density index, refining and quantizing factors of influencing the load density index, and comprising the steps of: forming a sample library according to surveyed and statistic data; dividing a planned region into several functional regions; for some type of functional region, setting main factors of influencing the load density index as environmental parameters; calculating membership grade sets of to-be-measured functional region and primary-screened sample regions; obtaining maximum closeness fuzzy subsets of each sample region and to-be-measured functional region, namely corresponding closeness values; selecting load density index of a sample region with the maximum closeness as a forecasted value of the to-be-measured functional region. And it can derive the forecasted load value relatively fitting the actual conditions.

The invention relates to a hybrid adaptive particle filtering method for mobile robot fault diagnosis. A state space adaptive mechanism and a particle number adaptive mechanism are organically combined. A core thought of the state space adaptive mechanism is that a sampling space is constrained to a fuzzy subset of a total state space according to domain knowledge of the mobile robot fault diagnosis; a particle number is adjusted through a KL distance between approximate distribution represented by particle sets of two different particle numbers at the same moment; the domain knowledge is usedfor describing occurrence probabilities of various faults of a mobile robot in different motion modes; and domain knowledge uncertainty caused by driver and execution mechanisms is represented by a fuzzy set.

The invention discloses a model learning method applicable to cross rolling, comprising the following steps: defining a fuzzy set theory domain U by a mathematical method, building a subordinate function of a fuzzy subset A, representing the fuzzy set by a ZADEH representation method, wherein the subordinate function in the theory domain U is a real-valued function, U is an element of a set [0, 1], determining the subordinate function by a fuzzy distribution method, carrying out clustering analysis on similarity level of steel types and specifications by an MINKOWSKI distance close degree method; determining cut set level of a satisfied fuzzy similar matrix by the golden mean method, determining a fuzzy rule number corresponding to the cut set level by a maximal tree method, thus building a rolling line forward slip learning fuzzy matrix, wherein with respect to the quantity with matrix value of 1, the learning can be completely inherited, with respect to the quantity with matrix value of 0, the learning is not inherited. According to the invention, the stability of the production process can be improved, a sleeve rising steel blocking accident resulted from model learning trend errors in the cross rolling process are effectively prevented, the precision of the product quality is improved, and free rolling is realized.

The invention discloses a black-start scheme evaluation method based on fuzzy entropy weight, and belongs to the field of power supply management. When each black-start scheme is evaluated, combiningthe entropy weight of each evaluation index with the subjective weight of the expert to obtain a comprehensive weight; respectively standardizing according to the cost type indexes and the benefit type indexes to obtain an evaluation matrix; constructing a fuzzy evaluation matrix by the standardized evaluation matrix; performing fuzzy operation on the comprehensive weight and the fuzzy evaluationmatrix to obtain a fuzzy subset; and normalizing the fuzzy subset to obtain a fuzzy comprehensive evaluation result, and sorting the black start schemes by utilizing the evaluation result. According to the technical scheme, when the weights of the evaluation indexes are considered, objective data are processed through the entropy weight method and then combined with preference judgment of expert experience, the evaluation indexes of all the black-start schemes are fuzzified within a reasonable range, and objectivity of an evaluation result is enhanced while project subjectivity is considered.The method can be widely applied to the fields of power grid operation adjustment and emergency management.