205 results about "Fuzzy decision" patented technology

The invention discloses an intelligent coordination control method for traffic flow of an urban road network with public traffic priority. Each intersection is provided with a network type multi-phase intelligent signal controller, and the intelligent signal controller consists of six main parts, namely a local intersection signal control module, an adjacent intersection information processing module, a communication module, a local vehicle information processing module, a social vehicle detection module, and a public traffic vehicle detection and positioning module. A core part of the intelligent signal controller is the local intersection signal control module which consists of three submodules, namely a public traffic priority module, a green light observation module and a phase switching module, and each module adopts a fuzzy decision method. The method realizes the coordination of the traffic flow of the whole urban road network and the prior pass of public traffic through the information exchange and coordination between intelligent signal controllers of adjacent intersections; and in time-varying and large-flow traffic environment, the control effect of the method is obviously superior to that of the conventional single intersection vehicle induction control method.

The invention discloses a wind, light and water-containing multi-source complementary micro-grid hybrid energy storage capacity optimal proportion method. According to the method, an annual output power curve of wind power generation, photovoltaic power generation and hydroelectric generation is simulated according to the distribution condition of natural resources such as wind, light and water, an annual load curve of a micro-grid is combined, system cost and power fluctuation are used as target functions, accumulator capacity and super-capacitor capacity are used as optimization variables, and meanwhile constraint conditions such as power balance constraint, maximum instantaneous power constraint, power supply reliability constraint, super-capacitor charge and discharge current and voltage constraint and accumulator SOC (System On Chip) constraint are determined to establish a wind, light and water-containing micro-grid hybrid energy storage optimization configuration model; optimized solution of the target functions is performed by using a fuzzy decision-containing multi-target planning GA-PSO (Genetic Algorithm-Particle Swarm Optimization) algorithm to obtain the optimal proportion of the hybrid energy storage capacity. Compared with the conventional GA algorithm and PSO algorithm, the method has the advantages that the convergence rate is higher and the problem of mutual conflict of the target functions in the multi-target optimization algorithm is avoided better.

The invention discloses a double-shaft photoelectric rotary table compound control system and a control method based on fuzzy decision. The control system comprises an integrated digital filtering, an estimation unit, a feed-forward control unit, a measuring unit, a change-over switch, a fuzzy-decision unit, a piping and instrument diagram (PID) controller, and a signal superposition unit. The double-shaft photoelectric rotary table position error e is compared with an error threshold K c set in advance, when e is less than K c, the fussy regulation is entered and regulation output controlled quantities delta kp, delta k and delta kd are obtained. PID control and regulation parameters kp, ki and kd are further obtained, and PID calculation is conducted. Otherwise, PID calculation is conducted by the utilization of the fixed regulation parameter set in advance and control signals outputted by PID are obtained. The effects of nonlinearity, time-varying characteristics, and model uncertainty on accuracy of the double-shaft photoelectric rotary table are effectively reduced. The problems of integral saturation and high frequency disturbance in the PID controller are solved, stochastic noise in the given stochastic signals is overcome, and long-term continuous stable work of the double-shaft photoelectric rotary table compound control system is ensured.

The invention discloses a fuzzy control method and a fuzzy controller, wherein the fuzzy control method comprises the following steps: receiving detection input quantity input by a detection device, and mapping a deviation value of the detection input quantity and a set value to an input discourse domain to obtain fuzzy input quantity; performing fuzzy reasoning and fuzzy decision according to the fuzzy input quantity to obtain fuzzy control output quantity of a corresponding output discourse domain; and converting the fuzzy control output quantity into control output quantity, outputting the control output quantity to an actuating mechanism, and controlling the controlled object by the actuating mechanism according to the control output quantity. Through fuzzy control method and the fuzzy controller, the chlorination in a water plant can reach optimal control effect.

The invention relates to a heterogeneous wireless network access selection method based on users. According to different service flow communication requirements of the users, network, terminal and user parameters are extracted, parameter key factors are quantized based on a fuzzy function, parameters are compared through an analytic hierarchy process, and the weights of the key factors are obtained; then according to an ordinal number preference algorithm similar to an ideal scheme, the close degree value of alternative network parameters is obtained by combining the parameter quantized value with the weights; network access is selected according to the close degree value. Network received signal intensity is sampled and pre-estimated, and calling drop caused by attenuation of received signal intensity of a selected access network is reduced. Through an improved fuzzy decision quantitative method, algorithm complexity caused by building of a fuzzy rule base is avoided, and calculation complexity is lowered. Optimal network access can be selected according to service types of the users, network congestion is reduced, and network resources are fully utilized.

The invention discloses a fuzzy rough set and decision tree-based track circuit red light strip fault positioning method. The method mainly comprises the following steps of: 1) establishing an initial decision table; 2) carrying out fuzzy discretization on continuous fault feature attributes to establish a fuzzy decision table; 3) inputting fault sample training data to obtain a reduced decision table; 4) establishing a diagnosis decision tree model; 5) inputting measured data into the diagnosis decision tree model, carrying out calculation to obtain a fault diagnosis result, inputting the measured data into a diagnosis positioning decision tree model, carrying out preliminary judgement to obtain a fault positioning result, judging faults of specific equipment by combining expert experiences, and giving corresponding fault maintenance suggestions. The method can rapidly and correctly position fault points of uninsulated frequency shift track circuit red light strip faults, greatly reduce the blindness and complexity of fault diagnosis, have relatively good rule explanation and relatively good robustness, improve the fault positioning speed and correctness and provide a new fault positioning technological means for intelligent fault diagnosis of track circuits.

The invention provides a fuzzy decision-based low-power dissipation self-adaption clustering multihop wireless sensor network topology control method, which is aimed at solving the problem of uncertainty of node energy dissipation and communication path in a self-organization wireless sensor network (WSN). A round is a cycle. Each round comprises an establishment stage and a stabilization stage, wherein the establishment stage comprises three steps of cluster head election, clustering, and route selection among clusters, and the stabilization stage comprises the step of data transmission. The method introduces a concept of fuzzy set, and selects excellent communication relay nodes by using a theory of fuzzy decision, so that a dynamic network route structure is formed. The method provided by the invention can adapt to more network conditions more effectively, balance energy and prolong life cycle of the network.

The invention discloses a high-resolution remote sensing image classifying method based on fusion of multiple classifiers. The high-resolution remote sensing image classifying method comprises the following steps that first, a training sample set is selected in an area of interest; second, the multiple classifiers are used for classifying remote sensing images; then, areas with the ground feature category classifying precision being lower than a threshold value a are classified again by using a voting method based on priori knowledge; at last, areas with the ground feature category classifying precision being lower than a threshold value b are classified by using a fuzzy decision template method, and finally the classified result of the target images is obtained. According to the high-resolution remote sensing image classifying method based on fusion of the multiple classifiers, the advantages of a single classifier are concentrated furthest, the disadvantages of the single classifier are restrained, the influences of 'same object with different spectrums ' and 'different objects with the same spectrum' on the classifying precision are lowered, and the precision of high-resolution remote sensing image classifying is improved.

The invention discloses a vehicle scheduling method based on a fuzzy decision. The method comprises the following steps of: S1, performing schedule filtering on candidate vehicles; S2, performing effective distance filtering on the filtered candidate vehicles; and S3, screening the vehicles which most possibly receive orders by making use of a Bayesian Belief Network. According to the method, the candidate vehicles are excluded according to objective conditions, namely, after the candidate vehicles are excluded according to the schedules of the vehicles and after the distances between the vehicles and the pick up locations are excluded, the order receiving intentions of the drivers of the rest of candidate vehicles are calculated by the Bayesian Belief Network, so that the vehicles which most possibly receive orders are filtered, and then the filtered vehicles which most possibly receive orders are polled, thus, the polling is more targeted, the probability for receiving orders is greatly improved, and the time for polling the candidate vehicles which are less likely to receive orders is saved. Large amounts of data statistics shows that after the adoption of the method provided by the invention, the candidate vehicles are polled, and the probability for receiving orders is improved by 10-15%.

The invention relates to a method for analyzing the heterogeneous property of an oil reservoir. The method comprises the following steps that 1, water injection parameters of a water injection well are collected, and a relative apparent water injectivity index Xi reflecting the water absorbing capacity of the oil reservoir is obtained; 2, the water injection intensity of each layer of a water absorbing profile of the water injection well is collected, and a water absorbing profile Lorentz coefficient Vi reflecting the heterogeneous property of the water absorbing profile of the water injection well is obtained through the Lorentz coefficient method; 3, fluid producing parameters of an oil well are collected, and a fluid producing intensity Lorentz coefficient Yi reflecting the fluid producing intensity of the oil well is obtained through the Lorentz coefficient method; 4, water cut rising parameters of the oil well are collected, and a well group water cut rising variable coefficient Wi reflecting the water cut rising differences of the oil well is obtained; 5, the fluid producing intensity of each layer of a production profile of the oil well is collected, and a production profile Lorentz coefficient Zi reflecting the heterogeneous property of the production profile of the oil well is obtained through the Lorentz coefficient method; and 6, a fuzzy decision-making evaluation model is built, and a comprehensive decision-making factor Fi is obtained. According to the method for analyzing the heterogeneous property of the oil reservoir, the heterogeneous property of the oil reservoir is quantified, and bases are provided for development and adjustment of oil fields.

The invention discloses a method for selecting a cell by a multi-mode wireless terminal. In a cell reselection process, the multi-mode wireless terminal firstly divides a cell which is in accordance with a cell selection standard S into a preferential network mode cell and a suboptimum network mode cell, and then a finally reselected target cell is determined by using fuzzy reasoning and fuzzy decision methods, wherein in a plurality of pilot frequency cells, participating reselection, with same frequency and same priority, fuzzy reasoning is utilized to evaluate five measurement parameters, namely, a receiving level value of each cell, a minimum required receiving level value in the cells, relative frequency shift, available uplink maximum transmitting power of a terminal and maximum radio frequency output power of the terminal, then, the requirements of a user on transmission rate and service instantaneity are integrated, an optimal target cell is selected according to a fuzzy decision result, and then the terminal is switched to the cell for residing. According to the method, the time of the switching process is short, seamless transition can be realized, user calls can be avoided being lost, a user is ensured to reside in the cell with the optimal service quality, and furthermore, the risk of missing the call during manual mode switching is eliminated.

The invention discloses a PEPSO-basedsiting and sizing method optimization method of a distribution type power supplyand belongs to the field of electric systems. The method is rapid, precise, effective and proper and is mainly provided for planning optimization of a power distribution network including a distribution type power supply. The objective is to determine an optimal access position and the capacity of the distribution type power supply. The technological means include capacity expanding of a transformer substation, circuit transformation upgrading and new accessing of the distribution power supply. The method is advantageous in that a complete multi-target optimization teaching model is established; the model covers multiple aspects of actors such as economic performance, reliability and environment protection; optimization seeking calculation is carried out based on a novel improved multi-target particle group algorithm PEPSO; a construction planning scheme is determined via the fuzzy decision technology; and power grid auxiliary analysis programs are compiles and planned based on the method, and the optimization seeking calculation is carried out, so a feasible scheme set is formed and an optimal scheme is determined.

The invention provides an intelligent electrical-network fault diagnosis method based on multilevel feedback adjustment. The method comprises the steps that historical switching information and electrical quantity attribute information of different devices in an electrical network when the electrical network has faults are stored in a historical fault information base; electrical quantity information of the devices in the electrical network is obtained when the electrical network has faults; diagnosis at the rough identification level is carried out according to the obtained electrical quantity information of the devices in the electrical network, and suspected fault elements are determined in a minimal breaking zone defining method based on the equivalent network to form a candidate set of the suspected fault elements; diagnosis at the fuzzy decision making level is carried out; diagnosis at the accurate positioning level is carried out; and according to results of three levels of fault diagnosis, intersection elements are obtained from suspected fault element sets E1 and E2 to finally determine a diagnosis result of fault elements. The electrical network with faults is analyzed at different levels according to the different sources of the multi-source fault information and the obtaining and processing difficulty of different type of information, and different types of fault information is fully utilized and information is complementary to improve the accuracy of fault diagnosis.

The invention provides a global-local hybrid unmanned ship path planning method based on dynamic constraints. The global-local hybrid unmanned ship path planning method comprises the steps of: S1, constructing an unmanned ship kinematics model, and conducting kinetic analysis; S2, considering a safety range of an unmanned ship, realizing the planning of a global path of the unmanned ship by adopting a storage node mode, and constraining the safe driving of the unmanned ship by optimizing the generated path points; S3, for a dynamic unpredictable environment, constructing a hierarchical structure based on an FDM fuzzy decision module and an FDW precise dynamic window module, and realizing the planning of a local path of the unmanned ship, precisely taking drivable forward, heading angular velocities and acceleration into account in the planning of the local path, and generating a traceable path that enables real-time local collision avoidance; S4, and fusing a global path planning scheme and a local path planning scheme of the unmanned ship to form a global-local hybrid obstacle avoidance strategy. According to the global-local hybrid unmanned ship path planning method, virtual pathpoints are inserted into the global optimal path, so that global and local path planning is perfectly combined, and the whole path tracking task is completed.

The present invention discloses a brushless DC motor fuzzy control system based on a genetic algorithm and a control method thereof. The control system comprises a drive system used for driving a brushless DC motor, a speed observer used for collecting the rotation speed of the brushless DC motor, a rotation speed reference model which provides the reference data of a motor rotation speed and compares the reference data of a motor rotation speed with the collected motor rotation speed so as to obtain a rotation speed error and an error change rate, a parameter fuzzification module which receives the rotation speed error and the error change rate, carries out quantification on the rotation speed error and the error change rate, and maps the rotation speed error and the error change rate to a fuzzy set discourse domain, a genetic algorithm optimization module which uses the genetic algorithm to carry out online optimization on a module control rule, adjusts related parameters in the fuzzy set discourse domain and makes a fuzzy decision, and a defuzzification module which maps the output amount of the fuzzy decision to a basic discourse domain. According to the system and the method, the genetic algorithm is used to carry out online adjustment of the parameter of the fuzzy controller, and the controller can have good static and dynamic performance in different operation environments.

The invention discloses a fault information fusion diagnosis method based on a double-edge linear motor. The mode of data layer fusion, characteristic layer fusion, decision layer fusion and two-stage diagnosis is adopted to mainly avoid the air gap eccentric fault, the short-circuit fault between winding turns, the winding temperature abnormity fault and the vibration abnormity fault of the double-edge linear motor. A park vector fusion method and an improved base 8FFT algorithm are adopted for data layers; a fuzzy artificial neural network method and a fuzzy decision method are adopted for characteristic layers. According to the method, the park vector fusion method, the improved base 8FFT algorithm, the fuzzy artificial neural network method and the fuzzy decision method are combined together, and thus the capacity for diagnosing various faults of the double-edge linear motor is improved.

The invention provides a method for intelligently scheduling buses, which is characterized in that: an electronic terminal of station is set at a bus station which is connected with a bus-mounted system through a wireless module wirelessly, and connected with a bus scheduling center through a GPRS (general packet radio service) module and a transmission network wirelessly so as to acquire the passenger flow volume at the bus station in real time. A predictor of scheduling decision software is connected with a passenger flow volume collector so as to predict passenger flows. A fuzzy decision device is connected with the predictor and the passenger flow volume collector so as to identify a bus running mode; and output of the fussy decision device is connected with a peak BP (back propagation) network, a middle-peak BP network and a low-peak BP network respectively so as to realize the optimal departure interval decisions under different running modes. The method has the advantages that: (1) the passenger flow information is real-time and reliable; (2) the prediction is scientific based on real-time passenger flow; and (3)the scheduling decisions are made under the different modes, and the results are more reliable. The method provided by the invention realizes exact monitoring, scientific prediction and reasonable decision, can be applied to the scheduling of bus system and has huge economic and social benefits.

The invention discloses a multi-objective optimization method for injection molding parameters. The method is a fuzzy decision Gkriging-NSGA-value strategy processing multi-objective optimization design method based on an improved Kriging agent model Gkriging, a non-dominated sorting genetic algorithm NSGA-II and a value set, and is used for solving the problem of fuzzy decision Gkriging-NSGA-value strategy processing multi-objective optimization design. Selecting injection mold sub-runner section size parameters and injection molding process parameters as decision variables to be optimized; respectively taking the maximum volume shrinkage rate, the flow channel total volume and the forming period of the product as evaluation indexes of the product quality, the production cost and the production efficiency, establishing a model, obtaining an optimal value of multiple targets of the quality, and realizing multi-target optimization of the comprehensive quality of the product through a multi-target decision-making method. According to the injection molding parameter multi-objective optimization method, high-quality injection molding products can be economically and rapidly obtained onthe basis of a one-mold two-piece mold.

An embodiment mode of the invention discloses an intuitionistic fuzzy association method for ADS-B monitoring data and radar tracks. The method comprises the steps of carrying out association pretreatment on the ADS-B monitoring data and radar tracks to obtain a plurality of primary association radar tracks, obtaining fuzzy intuitionistic indexes corresponding to association attributes between the ADS-B monitoring data and the primary radar tracks, obtaining fuzzy decision grades corresponding to the association attributes according to the fuzzy intuitionistic indexes, and carrying out weighing summation on the fuzzy intuitionistic indexes to obtain final associated radar tracks. The associated attributes comprise the distance, the speed, the speed difference and the course angle difference. The embodiment mode of the invention further discloses an intuitionistic fuzzy association device for the ADS-B monitoring data and the radar tracks. By means of the method, the intuitionistic fuzzy association method for the ADS-B monitoring data and the radar tracks can improve association accuracy between the ADS-B monitoring data and the radar tracks and is easy to achieve.

The invention discloses a power distribution equipment state evaluation method based on variable weight and fuzzy comprehensive evaluation. The power distribution equipment state evaluation method comprises the steps of 1, selecting evaluation indexes; step 2, index weight primary distribution: adopting an analytic hierarchy process, establishing a comparative judgment matrix by comparing the importance between every two evaluation factors, and finally calculating a feature vector under the maximum feature value to obtain the weight of each index; step 3, calculating the health state score ofeach index; step 4, weight redistribution: carrying out weight redistribution, wherein xi is the health state score value of the ith index; step 5, performing fuzzy decision index normalization processing; step 6, calculating a membership function; step 7, establishing a fuzzy evaluation matrix; step 8, determining an evaluation result by utilizing a membership degree maximum principle. The problem that in the prior art, when power distribution equipment is evaluated, other indexes with small weights cannot correctly reflect the health state of the equipment due to the fact that the indexes with large weights have too large influences on the equipment is solved.

The invention discloses a multi-target plant level load optimization method based on immune optimization and fuzzy decision in the technical field of energy-saving power generation. The multi-target plant level load optimization method comprises the steps that a thermal power plant coal consumption model, a model of time needed for completing distributed loads, and a nitric oxide emission model are established respectively; coal consumption characteristic parameters, current bearing loads, permitted load change rates and nitric oxide emission characteristic coefficients of all units in a thermal power plant are determined; constraint conditions of the models are determined, including upper limit and lower limit constraint conditions and balance constraint conditions of active power; a solution set including the active power of all the units in the thermal power plant, thermal power plant coal consumption, time needed for completing the distributed loads of the thermal power plant, and the nitric oxide emission of the thermal power plant is obtained through an immune multi-target algorithm; an optimal solution is selected from the solution set to serve as output power of all the units in the thermal power plant. According to the multi-target plant level load optimization method based on immune optimization and fuzzy decision, the immune multi-target algorithm is used for realizing multi-target plant level load distribution, an optimal power distribution scheme for the power plant units is decided according to a fuzzy decision theory, and therefore the computational process is faster and more accurate.

The invention provides a fuzzy control method and a system for the automatic combustion of an incinerator. The fuzzy control method for the automatic combustion of the incinerator comprises the steps of receiving a detection input variable sent by a detection device, calculating the deviation and the deviation change rate between the detection input variable and a given value, and adopting the deviation change rate as the input variable of the fuzzy control; fuzzifying the input variable as a fuzzy input variable, conducting the fuzzy reasoning and the fuzzy decision-making based on the fuzzy input variable to obtain a fuzzy output variable, and defuzzifying the fuzzy output variable to be as a control output variable; outputting the control output variable to a controller for the corresponding component of the incinerator so as to control the operation of the corresponding component of the incinerator. The detection input variable includes the oxygen content at the outlet of a boiler and the load of the boiler. The control output variable, corresponding to the oxygen content at the outlet of the boiler, contains the given data of the primary air flow. The control output variable, corresponding to the load of the boiler, contains an infrared temperature given value and a feeding speed value. Based on the above method and the above system, the complex control problems of multiple variables and large time delay during the waste incineration process can be solved.

The invention discloses an unmanned crane space collision avoidance strategy based on fuzzy decision inference. The unmanned crane space collision avoidance strategy includes the steps of firstly, determining the starting point and the ending point of a crane and a possible route; secondly, scanning the whole environment inside a workshop through lasers, and drawing a laser scanning result graph; calculating the space topological relations between the crane and entities such as devices and goods in all areas through the fuzzy inference and decision theory and a design algorithm when the route is selected, and judging whether the workshop is separated from the entities of equipment, goods and the like in each area of the workshop; replanning the route and executing the first step if the workshop is not separated from the entities of equipment, goods and the like in each area of the workshop; selecting and using the route and executing the hoisting task if the workshop is separated from the entities of equipment, goods and the like in each area of the workshop. The space topological relations between the crane on the assigned route and the other entities in the workshop are calculated through the fuzzy control theory and the fuzzy decision inference algorithm, the non-collision requirement of the hoisting task is met through supporting of the space topological relations, the detection means is provided for the feasibility of the route planning result of the unmanned crane, and reliability of the hoisting tank under the unmanned operation environment is improved.

The invention discloses a wireless sensor routing algorithm based on the fuzzy multi-attribute decision. The method comprises the following steps that (1) communicable nodes around the current node are created as decision schemes by starting from a source node; (2) the fuzzy attributes of the decision schemes in the step (1) are determined, and a fuzzy evaluation matrix X is established; (3) standardized processing is performed according to the fuzzy evaluation matrix so that a standardized fuzzy decision matrix H is obtained and the objective weight is determined by using the H; and the subjective weight is determined according to the preference in the fuzzy evaluation matrix; (4) the combination weight is determined by using the objective weight and the subjective weight of the previous step; (5) the combination weight is combined with the fuzzy decision matrix so as to calculate a comprehensive evaluation value, the optimal point is selected to act as a next jump node and set as the current node; and (6) whether the node belongs to a sink node is judged, and the algorithm is ended if the node belongs to the sink node; or the process returns to the step (1) for repetition if the node does not belong to the sink node. According to the technical scheme, the more stable, reliable, scientific and objective fuzzy multi-attribute wireless sensor routing algorithm can be obtained.

The invention relates to an intelligent identification method of a road traffic status. In the method, road section traffic statuses are defined into five grades of smoothness, normality, crowdness, blockage and severe blockage, and traffic flow ratios and occupation ratios are both defined into five grades of very small, small, medium, large and very large; a fuzzy reasoning method is used for intelligently identifying the road section traffic statuses; mode combination is carried out to form a fuzzy relation according to the traffic flow ratio, the traffic occupation ratio and a traffic status rule, and then actual measurement data of the traffic flow ratio and the occupation ratio is used as an input to work out a fuzzy set; and finally, the traffic status at that time is obtained by adopting fuzzy decision. The invention has the advantages that the traffic status is synergetically measured by the two main parameters: the traffic flow ratio and the occupation ratio, thus the accuracy is high; only one traffic flow detector needs to be arranged for measuring the traffic flow and the occupation ratio, thus the site construction cost is lower; and the traffic statuses are expanded to five grades, thus the integrity and the systematicness are high.

Provided is a method for acquiring a thermal power generating unit boiler mixed coal optimized coal blending scheme. The method, by using an objective function with maximum boiler efficiency, lowest mixed coal cost, and minimum pollutant discharge, establishes an optimized coal blending mathematic model, including a multi-objective fuzzy mathematic model, a boiler NOx discharge prediction model, and a boiler efficiency computing module, and enables the in-boiler coal blending scheme to be reasonable so as to guarantee the economical efficiency and the environmental friendliness of the operation of the boiler. The method comprises establishing the optimized coal blending mathematic model by using the objective function with maximum boiler efficiency, lowest mixed coal cost, and minimum pollutant discharge and using the boiler NOx discharge prediction model, a multi-objective fuzzy decision model, and the boiler efficiency computing module as a basis, and achieve prediction by using support vector machine technology and achieve optimization by using multi-objective fuzzy mathematic technology. The method is used for acquiring the thermal power generating unit boiler mixed coal optimized coal blending scheme.

The invention discloses a power grid vulnerability assessment method considering the minimum singular value sensitivity entropy and belongs to the field of power grid vulnerability assessment. The method includes the steps of obtaining a Jacobian matrix of a tidal current equation for a power grid to be evaluated, conducting singular value decomposition on the matrix, obtaining the sensitivity ofthe minimum singular value to a system state variable; obtaining the sensitivity of the minimum singular value to the control variable according to the hidden function derivation rule, selecting out the sensitivity of the minimum singular value to the node input active power and normalizing the data vectors; calculating the minimum singular value sensitivity entropy of the Jacobian matrix definedby the method according to the information entropy definition in combination with the weighted entropy theory, and defining a power grid vulnerability comprehensive evaluation index by using three indexes including a comprehensive power flow entropy, a minimum singular value and a minimum singular value sensitivity entropy, through a hesitation fuzzy decision method considering the reliability. According to the power grid vulnerability assessment method, the vulnerability of the power grid under multiple states can be evaluated, and the fragile link of the power grid can be effectively identified.

The invention provides an airport terminal departure passenger traffic volume prediction method based on a fuzzy decision-making tree, and relates to an airport terminal departure passenger traffic volume prediction method. The airport terminal departure passenger traffic volume prediction method aims at solving the problems that in the prior art in solving the phenomenon that an airport terminal is crowded, optimizing speed is low, timeliness is poor, generalization ability is low, application is complex, efficiency is low and simple and accurate prediction requirements can not be met. The method comprises the steps of step 1, obtaining the membership degree; step 2, selecting an attribute with the smallest fuzzy information entropy to be served as a root node of the fuzzy decision-making tree; step 3, obtaining a joint confidence degree of a leaf node lambaba; step 4, if lambaba satisfies lambaba>lambaba0, ending continuation and setting the joint confidence degree of the leaf node lambaba as the passenger traffic volume level probability; step 5, if the joint confidence degree of the leaf node lambaba <= lambaba0, repeating step 3 until the leaf node satisfies lambaba>lambaba0 to obtain the fuzzy decision-making tree; step 6, ensuring the achievement of the steps such as obtaining the fuzzy decision-making tree.

The invention discloses a power system load prediction method using a fuzzy decision-based neural network model. The method includes the following steps that: the degree of membership mui of historical load samples is obtained by using the fuzzy decision-based neural network model, and a historical sample membership degree curve is drawn; historical sample data of which the degree of membership is smaller than mui are removed, processing is performed, and therefore, samples which contain missing data and data that vary sharply can be basically eliminated; and after data pre-processing, an appropriate neural network model is selected, and change rules are found out based on the historical data of power load, and a power load neural network prediction model is established. According to the power system load prediction method using the fuzzy decision-based neural network model of the invention, the advantages of fuzzy decision and neural networks are combined, so that the convergence precision and convergence speed of the method can be both improved, and dependence on an initial value can be decreased, and therefore, the accuracy of prediction can be improved.