168 results about "Membership function" patented technology

A method, system and apparatus for performing peritoneal dialysis are provided. To this end, in part, a method of controlling a medical fluid heater is provided. The method includes the steps of determining a first heater control output based on a number of measured inputs for the heater and at least one mathematical relationship between at least two of the measured inputs, determining a second heater control output based on at least one fuzzy logic membership function and at least one fuzzy logic rule, and determining a third heater control output based on the first and second outputs and using the third heater control output to control the heater.

Aiming at the defects in the prior art, the invention discloses a control method of a speed regulator of a servo system of a flat knitting machine. A system in the prior art has low adaptability and low stability. According to a fuzzy proportional-integral (PI) control algorithm of the control method, the running speed of a transmission mechanism of the computerized flat knitting machine is used as a control object; the difference value between the practical reference speed and the feedback speed and the change rate of the difference value are served as input of a fuzzy controller; the input quantity is fuzzified through selecting an appropriate universe of discourse and an appropriate membership function; appropriate fuzzy rule tables are set by utilizing the practical tuning strategies of PI parameters; and after a Mamdani fuzzy reasoning algorithm and defuzzification processing are adopted, the variable quantities of parameter values of the PI controller are output, thereby realizing on-line correction of the PI parameters. By adopting the control method, the disadvantages of a traditional manual correction method for the PI parameters are overcome, and on-line real time correction of the PI parameters is realized, thereby improving the adaptability and the stability of the system.

A Soft Computing (SC) optimizer for designing a Knowledge Base (KB) to be used in a control system for controlling a suspension system is described. The SC optimizer includes a fuzzy inference engine based on a Fuzzy Neural Network (FNN). The SC Optimizer provides Fuzzy Inference System (FIS) structure selection, FIS structure optimization method selection, and teaching signal selection and generation. The user selects a fuzzy model, including one or more of: the number of input and / or output variables; the type of fuzzy inference model (e.g., Mamdani, Sugeno, Tsukamoto, etc.); and the preliminary type of membership functions. A Genetic Algorithm (GA) is used to optimize linguistic variable parameters and the input-output training patterns. A GA is also used to optimize the rule base, using the fuzzy model, optimal linguistic variable parameters, and a teaching signal. The GA produces a near-optimal FNN. The near-optimal FNN can be improved using classical derivative-based optimization procedures. The FIS structure found by the GA is optimized with a fitness function based on a response of the actual suspension system model of the controlled suspension system. The SC optimizer produces a robust KB that is typically smaller that the KB produced by prior art methods.

The invention discloses a comprehensive microgrid planning and design evaluation system based on an OTFN-AHP (optimized triangular fuzzy number analytic hierarchy process), and belongs to the technical field of electric information planning. An evaluation method comprises the steps that 1) a microgrid planning and design evaluation index system is determined; 2) computation models of microgrid evaluation indexes and reasonable and practical non-linear membership function models of the indexes are provided, so that quantized values of the indexes are obtained; 3) the traditional analytic hierarchy process and a fuzzy mathematic theory are combined and optimized to form the OTFN-AHP; 4) the fuzziness of an ordering vector is removed by utilizing a centroid method variable according to preference of a decision maker; 5) relative weights of the indexes are solved by nonlinear planning according to the ordering vector; and 6) an advantage and disadvantage degree of a microgrid is determined according to the quantized values and the weights of the indexes; and an evaluation report of the micogrid is output. The evaluation system can make scientific and objective evaluation on microgrid planning and design schemes in different structures, so that the evaluation system provides a reasonable reference for the decision maker, and has very high actual instruction significance.

The invention discloses a satellite health state multistage fuzzy evaluation method based on an AHP-entropy weight method. The method includes: constructing a system weight system comprising a subjective weight, an objective weight and a comprehensive weight, providing the accurate comprehensive weight for the fuzzy comprehensive evaluation method by adopting an analytic hierarchy process and an entropy weight method, providing the subjective weight by adopting the analytic hierarchy process, providing the objective weight by adopting the entropy weight method, and obtaining the subjective andobjective combined comprehensive weight from the subjective and objective weights through an optimization function; constructing a system evaluation system comprising a factor discourse domain, a comment set, a membership function, an evaluation matrix and multi-stage fuzzy evaluation; and comprehensively evaluating the use state of the on-orbit satellite, evaluating each layer of elements through a membership principle of fuzzy comprehensive evaluation, and then sequentially recursively evaluating the overall health state from bottom to top by adopting a multi-stage fuzzy comprehensive evaluation method. The method improves the safety and reliability of a satellite system, reduces the effective life cycle operation cost, and guarantees the smooth completion of an in-orbit satellite task.

The invention discloses a feedback control method of a wind energy converting system based on a fuzzy performance estimator and designs a T-S fuzzy state feedback control system including the fuzzy performance estimator specifically to characteristics of strong nonlinearity and parameter uncertainty of the wind energy converting system, belonging to the feedback control field. Firstly, a nonlinear model and a T-S fuzzy model of the wind energy converting system are provided; secondly, the fuzzy performance estimator of the system is designed based on the T-S fuzzy model of the wind energy converting system; then, a linear controller is designed in each linear local model respectively; and a fuzzy state feedback controller of a global model is constituted by dint of membership function. The method provided by the invention has the advantages of a controller is designed based on the fuzzy performance estimator; the feedback control method ensures that a closed-loop control system has good tracking performance; simultaneously, preferable dynamic performance and stable-state performance can be acquired.

The invention relates to a distribution network evaluation method based on power supply partitioning features. The method comprises the following steps: determining a distribution network evaluation index system which is composed of five primary indexes and 17 secondary indexes; building membership functions of the indexes by use of a fuzzy membership degree method and based on the principle of linear interpolation; determining the weights of the evaluation indexes by use of a judgment matrix method of a quantitative calculation method; establishing a direct influence matrix, calculating a comprehensive influence matrix between the indexes, and using the comprehensive influence matrix to optimize the weights of the evaluation indexes; and substituting the calculation result of the previous step to a calculation formula of the analytic hierarchy process to get the final result of distribution network evaluation. Through the method, the comprehensive score level of evaluation samples can be given more accurately, the evaluation result is more accurate, and the distribution network plan and design decision work can be better guided.

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 relates to a clustering method for an attribute missing data set. According to the clustering method, on the basis of accuracy limiting factors of a missed data set clustering algorithm, a nearest neighbor method is employed to determine an estimate constraint space of a missed attribute, real number coding of the missed attribute and a clustering center is carried out, searching optimization is carried out through an ant colony algorithm, a missed attribute estimate and the clustering center are both acquired in an optimization iteration process, fuzzy clustering is further accomplished through a membership function of an FCM algorithm, so a hybrid optimization clustering algorithm of the attribute missing data set is formed. The method is advantaged in that accuracy of the missed attribute estimate is improved, and clustering result error dividing probability is reduced.

The invention relates to a state evaluation method for a power transformer based on fuzzy mathematics. The state evaluation method for the power transformer based on fuzzy mathematics provided by the invention is used for solving the problem that the present state evaluation system cannot accurately evaluate. The method comprises the following steps: 1) preprocessing the transformer body, the casing pressure test data and the oil test data of transformer body; 2) confirming a membership function according to the step 1); 3) acquiring the fault type of the power transformer according to the step 2); 4) comprehensively evaluating the state of the power transformer. According to the invention, on the basis of the test requirement for the substation, a fuzzy mathematic theory is adopted for processing the high-pressure test data and oil test data of the transformer, the membership function of local test data is confirmed, the fault type is confirmed according to the processing and analyzing result and the transformer state is comprehensively evaluated. The state evaluation method is applied to the field of evaluation for the state of the power transformer.

The invention discloses a method for determining the operating condition of a high-speed train based on fuzzy membership. The method includes the following steps of: 1, de-noising speed data generated by a running train; 2, sampling the de-noised data; and 3, determining operating condition of the high-speed train. According to the analysis of the speed data generated by the running train, it is found that the high-speed train parks about two minutes, a plurality of operating conditions are present in one operation cycle, and the high-speed train still changes in speed to a certain extent when the high-speed train accelerates to a certain speed. Therefore, according to the idea of big data, the method de-noise the speed data first, and then uses the fuzziness of the operating conditions of the high-speed train to select a fuzzy variable and a membership function so as to determine a fuzzy membership function and determine the operating condition of the high-speed train by fuzzy reasoning, thereby solving difficulty in determining the operating condition of the train.

The invention discloses an optimized and improved fuzzy regression model construction method based on a nondominated sorting genetic algorithm II (NSGA-II). The optimized and improved fuzzy regression model construction method based on the NAGA-II reduces fuzzy sets, fuzzy rule and redundancy of antecedent of the fuzzy rule, and improves explanatory of a fuzzy regression model. The optimized and improved fuzzy regression model construction method based on the NSGA-II comprises the following steps: firstly an initial fuzzy regression model is constructed by a triangle subordinate function and a WM (WangandMendel) algorithm; and then based on a NSGA-II optimized fuzzy regression model, the redundancy of the fuzzy rule is simultaneously deleted by selecting the fuzzy rule and the antecedent of the fuzzy rule, thereby improving accuracy and explanatory of the fuzzy regression model.

The invention relates to a new method which combines the fuzzy adapting of wavelet transform and curvelet transform in the image noise removal. The noise removal is one of important research programs in the image processing; however, the existing noise removal method can not completely solve the conflict between the noise removal and the edge preserving. The invention aims at providing an image noise removal method with the combination of the wavelet and curvelet fuzzy adapting on the basis of the defect of the prior art. The method of the invention establishes a flatness membership function of a sub-block to fuzzy express the edge information content in the sub-block and takes the membership function as the weight factor to carry out the data fusion to each sub-block by adopting the results from the noise removal with the wavelet transform and the curvelet transform. The method of the invention has the advantages that the data fusion substitutes the compulsory smoothing processing of the adapting combination method to solve the problem of blocking effect more thoroughly and retain more edge details; the advantages of the noise removal with the wavelet and the curvelet are flexibly integrated by the fuzzy data fusion so as to further improve the quality of noise removal.

The invention discloses an optimized and improved fuzzy classification model construction method based on a nondominated sorting genetic algorithm II (NSGA-II). The optimized and improved fuzzy classification model construction method based on the NAGA-II reduces characteristic variable, a fuzzy rule and redundancy of antecedent of the fuzzy rule, and improves explanatory of a fuzzy classification model. The optimized and improved fuzzy classification model construction method based on the NSGA-II comprises the following steps: firstly an initial decision tree is constructed by a C4.5 algorithm, the characteristic variable and fuzzy set numbers are selected; then a triangle subordinate function is utilized to convert the decision tree into the fuzzy classification model; and finally based on a NSGA-II optimized fuzzy classification model, the redundancy of the fuzzy rule is simultaneously deleted by selection of the fuzzy rule and the antecedent of the fuzzy rule, thereby improving accuracy and explanatory of the fuzzy classification model.

The invention relates to the technical field of power systems and automation thereof, in particular to a fuzzy coordination control calculation method for a reactive power compensation system of a power distribution network in DG and EV environments. The method comprises the following steps: calculating a working temperature; calculating a working open-circuit voltage, a short-circuit current anda filling coefficient; calculating output voltage and output power; calculating the output power of the wind turbine generator; setting a target function of collaborative optimization control; considering conditions required to be met by the safe and stable operation of the power grid; introducing a fuzzy decision-making method based on a fuzzy set theory to solve the uncertainty problem caused bynew energy power generation and load fluctuation; fuzzifying a multi-objective function of the power distribution network reactive power compensation system cooperatively scheduled by DG and the EV into a single-objective membership function; and solving the multi-objective optimization model by adopting a MATLAB simulator and utilizing a fuzzy power flow calculation method to obtain an operationstrategy of a decision variable. And two indexes including stability and economy of the power grid are improved.

A health monitoring system for complex networked apparatus includes a number of neuro-fuzzy inference apparatuses feeding inference results into a data fusion hierarchy. At each level in the hierarchy, fuzzy inference is applied to generate a desired output signal by processing selected input signals in accordance with a knowledge base defining fuzzy membership functions and fuzzy inference rules defined in advance. The knowledge base includes alternative definitions of membership functions and / or inference rules. The apparatus selects which definition to use according to environmental or other conditions, and predetermined selection criteria.

The invention belongs to the technical filed of robot trial planning, in particular to a navigation trail planning method of a mobile robot based on distance-type fuzzy reasoning. According to the navigation trail planning method, a fuzzy reasoning rule is adopted, knowledge of navigation, movement toward a destination and obstacle avoidance of the mobile robot is formulated, linguistic variablesin the fuzzy reasoning rule are quantified through a fuzzy set membership function, and based on distance-type fuzzy reasoning, a walking speed target value and a rotation speed target value of a given path of the mobile robot are calculated in real time by adopting information which is detected by a sensor on the mobile robot, so that the mobile robot achieves highly intelligent behavior like human navigation.

A health monitoring system for complex networked apparatus includes a number of neuro-fuzzy inference apparatuses feeding inference results into a data fusion hierarchy. At each level in the hierarchy, fuzzy inference is applied to generate a desired output signal by processing selected input signals in accordance with a knowledge base defining fuzzy membership functions and fuzzy inference rules defined in advance. The knowledge base includes alternative definitions of membership functions and / or inference rules. The apparatus selects which definition to use according to environmental or other conditions, and predetermined selection criteria.

The invention discloses a multi-level fuzzy evaluation method for a barrier lake. The method comprises the following steps: determining an evaluation index set U influencing subsequent development ofthe barrier lake; establishing a developable comment set V = {v1,v2,v3,v4} of the barrier lake; constructing a membership function equation,and establishing a fuzzy relation matrix R which is equal to[rij] n * m; determining the weight A of each grading index,and determining the weight of each grading index by adopting an improved analytic hierarchy process; adopting the maximum membership principle and finding out the Bmax with the maximum component in the B vector,enabling the evaluation corresponding to the Bmax to serve as the result of fuzzy comprehensive evaluation,and achieving comprehensive evaluation of the exploitability of the barrier lake. According to the method,a fuzzy group decision-making method and a multi-level decision-making theory are introduced,a multi-level multi-factor decision-making weir dam exploitability argumentation mathematical model is established from three criterion layers of engineering safety risks,resource feasibility and ecological environment benefits,the exploitability of the weir dam is evaluated,and reference and guidance are provided for permanent regulation of the weir lake.

The invention provides a processing parameter optimization method based on a fuzzy entropy weight comprehensive evaluation method-grey correlation analysis method and a surface quality evaluation system. The processing parameter optimization method comprises the following steps: (1) establishing a surface quality evaluation system index set; (2) constructing a surface quality evaluation system evaluation set; (3) determining a membership function of each index of the surface quality evaluation system; (4) establishing a single-index fuzzy evaluation matrix of the surface quality evaluation system; (5) calculating a surface quality evaluation system weight set by using an entropy weight method; (6) comprehensively evaluating the surface quality evaluation system; (7) calculating a gray correlation coefficient of each index of the surface quality evaluation system by using a gray correlation analysis method based on the membership degree of each index; (8) calculating the grey correlation degree of each index of the surface quality evaluation system; (9) determining an optimal machining parameter combination of the surface quality evaluation system based on the grey correlation degree; and (10) determining an optimal machining parameter combination of the surface quality evaluation system. By means of the optimized machining parameter combination, the surface quality evaluation system can be optimal.

The invention discloses a transformer bushing evaluation method, which fuses a plurality of indexes reflecting the state of a transformer bushing, comprehensively and comprehensively reflects the state of the bushing based on a fuzzy theory and a neural network algorithm, and introduces a variable weight and the neural network algorithm on the basis of fuzzy comprehensive evaluation. According tothe fuzzy control method, the limitation of conventional weight and the subjectivity of artificially determining the membership function are overcome, and the self-updating and self-adaption of the membership function are realized through neural network training, so that the fuzzy control has certain self-learning capability, and the evaluation result is more reliable.

The invention discloses an image collaborative segmentation method based on minimum fuzzy divergence, and belongs to the technical field of image processing and computer vision. According to the method, the segmentation effect is judged through an Intersection over Union (IOU) value, and the method comprises the following steps: 1, acquiring an image segmentation data set, and performing conversion from an RGB space to an LAB space; 2, constructing a fuzzy divergence formula by using a Gamma-type membership function, constructing a new energy function, and performing curve evolution accordingto a minimum fuzzy divergence criterion to achieve a good segmentation effect. The target edge is better processed by using the fuzzy set theory. The color information of one image is introduced intothe energy function of the other image, so that the robustness of initial curve replacement can be enhanced. An optimal segmentation effect is achieved by solving a local minimum value of an energy function by utilizing a region-based active contour model. The established model can reduce the complexity of calculation time, and can be applied to early-stage work of an integrated imaging three-dimensional display system.

The invention discloses a boiler flue gas denitrification control method based on a self-adaptive fuzzy PID algorithm. The implementation processes comprises the following steps of collecting an actual measured value of the nitrogen oxide at a flue gas outlet, and calculating a deviation value and a change rate of the nitrogen oxide at the flue gas outlet; determining membership functions of nitrogen oxide deviation, a deviation change rate, a proportion change value, an integral change value and a differential change value; outputting a new fuzzy set membership function according to a fuzzy control rule, calculating the proportional change value, the integral change value and the differential change value according to the fuzzy set membership function, and adjusting corresponding parameters in the PID in real time to obtain new PID parameters; and controlling the ammonia spraying amount according to the new PID parameters. According to the denitrification control method disclosed by the invention, the PID control parameters can be adjusted by adopting a fuzzy control algorithm, on one hand, the condition that the content of nitrogen oxide at the outlet exceeds the standard irregularly can be avoided; on the other hand, the usage amount of ammonia gas can be more reasonably and efficiently controlled.

The invention discloses a power distribution network grounding line selection method and system based on multi-measuring point and fault vertical characteristics; the line selection method verticallycompares fault characteristics of different positions along the line, uses a fuzzy theory to determine a membership function, converts complex fault criteria considering multi-terminal data into eachline fault support degree, uses a heredity algorithm to determine a membership function parameter, and calculates the line fault membership so as to determine a fault line, thus obtaining a line selection result. A conventional method depends on each line fault zero sequence current; the power distribution network grounding line selection method can solve said problems, can expand data sources, can improve data fault tolerance, and is suitable for multi-fault grounding conditions including an arc extinguishing coil grounding system, thus improving undercurrent earth fault line selection accuracy and applicability.

The invention discloses a distribution automation switch fault diagnosis method and device, and a readable storage medium. The method comprises steps of 1, selecting a characteristic quantity, building a comment set and a fault diagnosis fault set of a state evaluation index system, and forming a distribution automation switch state evaluation index system; step 2, establishing a fuzzy evaluationmodel factor set, layering and evaluation factor membership functions, and finally establishing a power distribution automation switch state evaluation model based on layered fuzzy comprehensive evaluation; and step 3, establishing a fuzzy C-means clustering distribution automation switch fault diagnosis model according to an improved grey correlation degree method of the fuzzy C-means. The methodis advantaged in that the most representative sequence is selected by clustering the reference sequence through the fuzzy C-means clustering method, the improved analysis method improves calculationefficiency, a purpose of completing efficient fault diagnosis through system information is achieved, the result can be well distinguished, the error of judging bias is not easy to generate, and accuracy and efficiency of fault identification are improved.

The invention provides a device evaluation method, and the method comprises the steps of building an evaluation index system of a device, receiving the parameter values of the to-be-evaluated indexesgenerated by the device in an operation process, wherein the evaluation index system comprises a plurality of key components, and each key component comprises a plurality of to-be-evaluated indexes; inputting the parameter values into a membership function, and outputting a membership numerical value of each to-be-evaluated index; calculating the first weight of each key component in the evaluation index system, and generating a first matrix by using all the first weights; converting the membership numerical value into an evaluation numerical value, and converting the evaluation numerical value into a fuzzy matrix through a fuzzy comprehensive evaluation method; and generating the plurality of target evaluation numerical values of the device through the first matrix and the fuzzy matrix, according to the corresponding relationship between the target evaluation numerical values and the evaluation results, selecting the evaluation result corresponding to the maximum target evaluation numerical value from the plurality of target evaluation numerical values as the target evaluation result. According to the present invention, the evaluation result is simple and clear.

The invention relates to a vehicle-mounted entertainment system interface evaluation method based on fuzzy comprehensive model and medium, and the method comprises the steps: firstly quantitatively extracting condition perception index and vehicle index data when a driver interacts with a vehicle-mounted entertainment system interface, and building a multivariate fuzzy set; secondly, determining a comment set, and calibrating a perceptual membership function of each piece of index information; and finally, fusing each piece of modal information to calibrate the weight of each piece of modal information, and forming a fuzzy sensing model of multi-modal information fusion. Compared with the prior art, the method has the advantages of determining the safety boundary of the vehicle-mounted entertainment system interface, reasonably limiting the interaction information amount, reasonably simplifying the information interaction mode, limiting the interconnection rule, ensuring the driving safety and the like.

The invention relates to the field of industry automatic control, in particular to a fuzzy control method for a nonlinear precise forging press. A control system comprises a fuzzification interface, a fuzzy reasoning machine, a knowledge base and a defuzzification interface. Input controlled variables are the displacement error e, the displacement error change rate ce and the displacement y of a slider; an output controlled variable is the rotation angle increment deltau of a motor; the displacement error e and the displacement error change rate ce, are converted into appropriate linguistic values in a domain of discourse after being fuzzified by the membership function of the fuzzification interface; fuzzy reasoning is performed on a fuzzified input variable according to a fuzzy rule through the fuzzy reasoning machine to obtain a fuzzy output variable; and weighted average defuzzification is performed on the fuzzy output variable to obtain the actual output rotation angle increment deltau of the motor. The output of the control system provided by the invention is capable of tracking input better, is capable of realizing product processing of which the precision is 10mu m when being applied to the nonlinear precise forging press, and can be widely applied to various nonlinear precise forging presses.

The invention relates to the field of crane control, in particular to an anti-swing driving control method for a crane. The anti-swing driving control method comprises the following steps: step 1. inputting variables through a sensor, including a trolley position x, a speed load swing angle theta, and a swing angle acceleration; step 2. inputting the input variables into a sliding-mode control module, introducing the output quantity of the sliding-mode control module into a type-2 fuzzy system for processing; step 3. setting an input-output membership function in a type-2 fuzzy controller; step 4. performing type-2 fuzzy reasoning and type reduction according to the fuzzy rules, and obtaining the final accurate output value Y by solving ambiguity; step 5. converting the output variables ofa fuzzy domain into the actual output value control force according to u=k0*Y, wherein k0 is a constant; and step 6. applying the value of u to an anti-swing model of the crane to obtain the swing angle, and then going back to step 1 until the swing angle reaches the set value. The anti-swing driving control method has relatively robustness, excellent handling performance and relatively strong anti-interference ability.

The invention provides a parameter adjusting method and device of a PID controller and electronic equipment. The method comprises the steps of obtaining a current PID control parameter of the PID controller, a current control error of a target control object and a current error fluctuation rate; based on a preset PID evaluation index, determining weights corresponding to the control error and the error fluctuation ratio respectively; determining a target control quantity of the target control object based on the current control error, the current error fluctuation ratio and the weights corresponding to the control error and the error fluctuation ratio; and updating the current PID control parameter based on the target control quantity and the current control error. The problem that a traditional control method is not accurate is solved, intelligent electrification is achieved, the method is different from an existing PID parameter adjusting method, a membership function and a fuzzy rule base do not need to be selected, influences of human experience and other inaccuracy factors are reduced, the whole parameter adjusting process is simpler and more efficient, and timely processing of system overshoot is facilitated.