40 results about "Gaussian membership function" patented technology

The invention discloses a variable transmission ratio parameter optimization method for a steer-by-wire control system, which comprises the following steps of: designing a variable transmission ratio controller based on fuzzy control, taking a vehicle speed and a steering wheel angle as input variables, taking a transmission ratio as an output variable, performing fuzzification processing on the input and output variables by adopting a Gaussian membership function, and designing a fuzzy rule; carrying out defuzzification by adopting a gravity center method, and converting fuzzy output into a specific transmission ratio numerical value; parameters of the fuzzy controller serve as optimization objects, the parameters of the fuzzy controller are optimized through a simulated annealing algorithm, an initial solution is an initial value of the parameters of the fuzzy controller, steering response, stability and driving comfort of a vehicle are comprehensively considered, an energy function is designed, the performance of the fuzzy controller is evaluated, and the optimal parameters are searched through iteration. And minimizing the energy function to obtain the optimal parameter of the fuzzy controller. The method does not depend on vehicle model precision and expert experience, does not need manual parameter adjustment, has interpretability, and accords with the steering expectation of a driver.

The invention discloses a bearing fault frequency spectrum evaluation method and device, equipment and a medium, and the method comprises the steps: firstly carrying out the feature extraction of a frequency spectrum of a to-be-evaluated bearing, and obtaining the feature frequency and amplitude of the to-be-evaluated bearing; the theoretical characteristic frequency of the fault of the bearing to be evaluated and the neighborhood of the theoretical characteristic frequency are determined; constructing a distance evaluation matrix by using the Gaussian membership function and the frequencies of the characteristic frequency points of the neighborhood, and constructing a peak value evaluation matrix by using the Gaussian membership function and the amplitudes of the characteristic frequency points of the neighborhood; respectively determining the weight of the distance evaluation matrix and the weight of the peak value evaluation matrix by using a variable coefficient method, and further obtaining fuzzy comprehensive evaluation; and finally, when the fuzzy comprehensive evaluation is greater than a fuzzy comprehensive evaluation health threshold value, determining that the to-be-evaluated bearing has a fault. According to the method, the difference between the theoretical fault characteristic frequency of the bearing and the actually detected frequency spectrum is considered, and the accuracy of evaluating the bearing fault is improved.

The invention provides a federal learning dynamic client selection and privacy protection method based on a dobby machine and fuzzy calculation, and aims to improve the client selection efficiency and the privacy protection effect. According to the method, clients are dynamically selected through a UCB strategy in a dobby machine algorithm, rewards are calculated according to real-time performance (such as accuracy, training loss and the like) of each client, and updating is carried out in the training process. In combination with a fuzzy calculation technology, a Gaussian membership function is adopted to carry out fuzzy processing on uploaded model parameters, so that privacy is effectively protected, and the risk of data leakage is reduced. By introducing a dynamic adjustment factor, the method can balance the proportion of noise to original data, and further enhance privacy protection. Experimental results show that the client selection strategy based on the UCB shows relatively high training efficiency and model performance on MNIST and CIFAR-10 data sets, and is obviously superior to a traditional federated learning method. The method not only optimizes the client selection strategy, but also reduces the calculation and communication overhead, improves the practicability and adaptability of federal learning, and especially can effectively improve the training efficiency and privacy protection capability in a large-scale system.

The invention discloses an alternating-current and direct-current hybrid power transmission network rack optimization evaluation method and system, which are used for solving the technical problems that a traditional alternating-current and direct-current hybrid power transmission network rack evaluation method depends on static parameters or empirical data, cannot dynamically respond to cross-regional load fluctuation and the like, is difficult to cope with risks, and causes low quality of a rack scheme. The method comprises the following steps: generating comprehensive evaluation indexes of a plurality of grid schemes according to acquired operation data of an AC / DC hybrid power transmission network by adopting power system simulation software; constructing a fuzzy evaluation matrix of each grid scheme according to the comprehensive evaluation index of each grid scheme by adopting an improved descending semi-trapezoidal membership function and a Gaussian membership function; calculating a target weight corresponding to the comprehensive evaluation index of each grid scheme; and according to the target weight corresponding to the comprehensive evaluation index of each grid scheme and the fuzzy evaluation matrix, calculating the comprehensive score of each grid scheme, and selecting the grid scheme with the maximum comprehensive score as the target grid scheme of the AC / DC hybrid power transmission network.

The invention discloses a flexible workshop uncertain processing time optimization method and system, relates to the field of intelligent manufacturing and industrial scheduling optimization, and aims to solve the problems of large flexible workshop processing time prediction error and poor scheduling scheme robustness in a dynamic disturbance scene. The processing time skewed distribution is dynamically represented through an asymmetric Gaussian membership function, fuzzy parameters are updated in real time in combination with sliding window Kalman filtering, and based on meta-learning deep reinforcement learning, small sample fast strategy migration is realized, the processing time prediction error is reduced, the equipment utilization rate is improved, and the new scene response efficiency is improved.

The invention relates to the technical field of process optimization, in particular to a fertilizer production process optimization method and system. The method comprises the following steps: obtaining original data composed of process parameters and evaluation indexes of a plurality of production batches, carrying out standardization processing, processing interval type evaluation indexes by using a Gaussian membership function, calculating an initial weight through an entropy weight method in combination with a standardization value and a time sequence fluctuation coefficient, carrying out correlation correction on the initial weight, and carrying out correlation correction on the initial weight; and when the absolute value of the correlation coefficient of any two evaluation indexes is greater than the corresponding correlation threshold value, reducing the initial weight according to the variance contribution degree, redistributing other indexes by using the reduced weight to obtain a final weight, constructing a comprehensive evaluation model based on the final weight, and determining an optimization scheme. According to the scheme, the interval type evaluation indexes can be standardized, the problems of information redundancy and weight distribution distortion can be eliminated, then the accuracy of the evaluation result is improved, and a more reliable optimization scheme is provided for the fertilizer production process.

The invention provides a hydroelectric generating set bearing bush temperature health degree calculation method, which comprises the following steps: collecting the bearing bush temperatures of n bearing bushes, setting an allowable bearing bush temperature difference as D, and obtaining the temperature of each bearing bush after treatment; calculating the number of the bearing bushes with different bearing bush temperatures; calculating the occurrence probability of the bearing bush with the temperature of k; the information entropy of the tile temperature difference confusion degree at the t moment is calculated; calculating the maximum value Tmax of the temperature of the bearing bush; training a GBDT model by using historical data of the bearing bush; the bearing bush temperature maximum value Tmaxpre at the current moment is predicted through a GBDT model; calculating the temperature residual Tres of the bearing bush at the moment t; and calculating the overall health degree of the bearing bush temperature by using a two-dimensional Gaussian membership function and integrating two dimensions of the bearing bush temperature difference and the bearing bush temperature change trend. The health state of the bearing bush temperature is reflected more comprehensively from the two dimensions of the bearing bush temperature change trend and the bearing bush temperature difference. According to the method, the overall health degree of the bearing bush temperature is calculated, so that the health state of the bearing bush temperature can be expressed in a more comprehensive and more visual manner.

The invention relates to the technical field of slope health degree evaluation, in particular to a slope health degree intelligent evaluation system and method based on multi-source data fusion, and constructs a multi-dimensional feature matrix such as fusion change rate by receiving displacement time sequence and rainfall environment data of a plurality of monitoring terminals of a slope. According to the core technology, all feature values are mapped through a Gaussian membership function and converted into initial reliability distribution under different risk levels, and a risk reliability vector is formed. Differentiated weighting is carried out on the reliability vectors, and evidence items representing local risk contributions are generated. Finally, all weighted evidence items are input into an improved evidence reasoning model, multi-source information contradiction is effectively solved, the global membership probability of the whole side slope on each risk level is solved, a continuous overall health degree score capable of dynamically reflecting a stable state is generated through fusion, the crossing from local heterogeneous perception to overall intelligent research and judgment is realized, and the overall health degree score is improved. And a core basis is provided for slope safety early warning and accurate management and control.

The invention discloses a mountain, water, forest field, lake and grass integrated protection and restoration status investigation and evaluation method, and relates to the technical field of ecological protection and restoration. The method comprises the following steps: dividing a target area into a plurality of ecological monitoring units, and obtaining a subjective data index set and an objective data index set of the ecological monitoring units; according to the subjective data index set and the objective data index set of the ecological monitoring unit, calculating a subjective weight value and an objective weight value of the ecological monitoring unit, and then obtaining a dynamic weight value of the ecological monitoring unit; and based on the dynamic weight value of the ecological monitoring unit, constructing a fuzzy evaluation model based on a Gaussian membership function, obtaining a comprehensive evaluation value of the target area, and performing qualitative evaluation on the ecological quality of the target area according to the comprehensive evaluation value. According to the invention, a scientific basis can be provided for management and evaluation of ecological restoration projects, and project managers can be helped to clearly understand the progress and effect of restoration work.

The invention relates to the technical field of milling process risk assessment and parameter optimization, in particular to a deep neural fuzzy reasoning system which comprises a semantic layer, a rule layer and a reasoning layer which are in signal connection in sequence and cooperatively achieve tool risk and quality risk assessment of a milling process. The method specifically comprises the steps that a semantic layer receives milling parameters, and numerical milling parameters are converted into a fuzzy feature matrix through an interval Type-2 Gaussian membership function; according to the method, expert knowledge is embedded through interval Type-2 fuzzy logic to adapt to a small sample milling scene, an IF-THEN activation rule is output to enable a risk decision basis to be traceable, reasoning real-time performance is guaranteed by means of Top-k sparse aggregation, meanwhile, closed-loop self-adaptive adjustment of milling process parameters is supported, the method is suitable for milling scenes of complex parts and novel materials, and the method is suitable for large-scale popularization and application. And the production efficiency can be improved while the machining quality is guaranteed.

The present application discloses a method, equipment, medium and product for tracing the factors affecting the health status of equipment, and relates to the field of equipment health assessment and management. The method includes: forming a sample matrix based on the original variables, extracting the main component features and the main component statistics features; using the Gaussian membership function to convert the main component statistics features into evidence; using the test sample to activate the evidence, and fusing all the evidence of feature activation based on the evidence reasoning algorithm to obtain the confidence of the evidence support state; constructing the relationship between the confidence of the evidence support state and the main component statistics features and the relationship between the original variables and the main component statistics features, and based on the two constructed relationships, obtaining the relationship between the confidence of the evidence support state and the original variables; based on the relationship between the confidence of the evidence support state and the original variables, the equipment health status influencing factors can be traced. The present application can evaluate the equipment status and trace the key factors affecting the status change in the case of high-dimensional correlated variables.

The invention provides a steam turbine fault symptom identification method based on fuzzy vector mapping and independent element space reconstruction, relates to the field of steam turbine fault diagnosis, and aims to solve the problems that normal working conditions and fault features are difficult to distinguish and the non-linear and non-stationary signal processing capacity is weak in a traditional vibration analysis method, an oil analysis method and an independent element analysis method. The method comprises the following steps: collecting multivariable sensor data of a steam turbine in a normal state for preprocessing; establishing an initial independent element analysis model; a fuzzy observation vector is constructed by using a Gaussian membership function, and the influence of large-amplitude normal fluctuation is weakened; performing dynamic reconstruction in an independent element space through the hybrid matrix, and calculating a residual vector; and constructing a square prediction error statistic based on the residual vector to carry out fault judgment. According to the method, the problem of order and amplitude uncertainty in independent component analysis is effectively solved, the recognition accuracy of the early weak fault of the steam turbine is remarkably improved, and reliable technical guarantee is provided for safe and stable operation of a power system.

A joint angle prediction method and system based on rule dimension reduction and a medium are related to the technical field of human-computer interaction. The method comprises the following steps: acquiring signals related to joint movement, extracting time domain features, frequency domain features and nonlinear features from the signals to form a multi-dimensional input feature vector; clustering the multi-dimensional input feature vector to generate a plurality of cluster centers, configuring a Gaussian membership function for the cluster centers, and constructing an initial fuzzy system; calculating the membership degrees of the current input multi-dimensional feature vector to each original fuzzy rule to form an original membership degree vector; mapping the original membership degree vector to a low-dimensional membership degree vector by using a learnable dimension reduction matrix; performing fuzzy reasoning by using the low-dimensional membership degree vector and corresponding consequent function parameters to output a joint angle prediction value; and determining the dimension reduction matrix and the consequent function parameters through joint optimization. The application can realize accurate prediction of joint angle.

The application relates to the technical field of electric control systems, in particular to a production control method for an antistatic and conductive geomembrane, which comprises the following steps: collecting temperature data in a geomembrane production process, denoising data segments in the temperature data, obtaining reconstruction data, obtaining a first fluctuation coefficient of the reconstruction data according to the difference values of various data points in the reconstruction data, obtaining a fluctuation difference of the reconstruction data according to the difference between second fluctuation coefficients of adjacent data points in the reconstruction data, obtaining a to-be-stabilized time interval according to the second fluctuation coefficients of all data points in the reconstruction data, obtaining a response coefficient of a Gaussian membership function according to the fluctuation difference of the reconstruction data and the to-be-stabilized time interval, obtaining an adjustment coefficient of the Gaussian membership function in combination with the first fluctuation coefficient of the reconstruction data, and then obtaining a variance value of the Gaussian membership function, and performing temperature control in the geomembrane production process. The temperature control is more robust, and the production yield of the geomembrane is ensured.

This invention provides a method and system for controlling construction waste crushing based on load feedback. The method includes: acquiring the stator current signal of the crusher's main shaft motor; extracting the active current component representing torque; constructing a time sliding window sequence; calculating the algebraic difference between the current active current component and a preset target value to obtain the original load deviation; constructing a weighted function to correct the load deviation amplitude; obtaining the load change rate by taking the first derivative with respect to time; building a fuzzy logic controller, using a Gaussian membership function in the fuzzification stage to establish a mapping between sample entropy and the width of the Gaussian distribution, and setting the membership function width in real time; adjusting the fuzzy subset of PID parameters according to fuzzy rules; using the peak-to-mean ratio of the sliding window sequence as an impact factor to weight and correct the centroid method defuzzification results; superimposing the adjustment increment onto the PID reference parameters; and combining the corrected load deviation to calculate the control command and adjust the output frequency of the feed motor inverter. This invention enables multi-dimensional adjustment of the crusher.

The invention relates to a project decision intelligent evaluation method and system based on rule fusion, and belongs to the technical field of data analysis. The method comprises the steps that research and development premise attributes and result attributes are acquired, research and development premise attribute reference values and result attribute evaluation grades are obtained through Gaussian membership function calculation, and a judgment rule base is constructed; a project name is obtained, a database is retrieved according to the project name to obtain a project related file, a fact object set is extracted according to the project related file, a cleaning object set is obtained through calculation of a cleaning model according to the fact object set, and a research and development score is obtained through calculation of an inference engine according to the fact object set and a judgment rule base; a research and development attribute threshold value is preset, judgment is carried out according to the research and development attribute threshold value and the research and development score to obtain a judgment result, the intelligent terminal carries out research and development project statistics according to the judgment result to obtain a statistical result, and the statistical result is transmitted to the human-computer interaction interface. The research and development project can be intelligently found from historical projects.

The present invention provides a type recognition method and device for non-cooperative targets, the method comprising: collecting multiple features of a designated space object; converting the multiple features into standard normal distributions respectively to form multiple standardized features correspondingly; constructing a multi-feature function corresponding to the standardized features, and calculating the probability density function of the multi-feature function; calculating the membership function of each standardized feature based on a bilateral Gaussian membership function consistent with the standard normal distribution characteristics; calculating the probability density and membership of each feature based on the probability density function and the membership function, and calculating the fuzzy probability of the designated space object based on the probability density and membership of each feature; determining whether the designated space object is a first-class true target based on the fuzzy probability. The method of the present invention can quickly and accurately identify space targets.

The invention relates to the technical field of electrical control system monitoring, and discloses a real-time fault detection method and system for a new energy automobile. The method comprises the steps of collecting voltage and current control signals of a battery management system and a motor control system to calculate a working condition feature vector, establishing a fast and slow dual-time scale state observer, calculating an adaptive gain matrix through a mahalanobis distance and a Gaussian membership function to correct the slow observer to obtain a health parameter estimated value, and judging a sudden fault by using a fast observer residual error and analyzing a health parameter trend to judge a gradual change fault. The method solves the problems that the estimation precision is reduced and the false alarm rate is increased due to the fact that the observer gain cannot adapt to multi-working-condition changes in the prior art.

The invention discloses a health data classification method based on an Ising machine training TSK fuzzy system, and the method comprises the steps: collecting target health data to construct fuzzy sets, calculating leading parameters of different fuzzy sets through a Gaussian membership function, solving the activation degree of each sample for different rules, carrying out the weighted average of the output of the rules according to the activation degree, and carrying out the classification of the health data. The output of the TSK fuzzy system is expressed as a linear regression form; linear regression output is converted into a QUBO problem through equivalent mathematical changes; and solving the optimal unwinding solution by using a quantum simulated annealing algorithm, and realizing classification of the target health data by analyzing the unwinding solution. According to the method, efficient approximate calculation is performed locally through a quantum simulated annealing algorithm, or the method is deployed on a quantum computer platform to obtain a higher-speed global optimal solution. While the calculation efficiency is ensured, the quantum optimization process can effectively avoid a common local optimal trap in a traditional method, so that the overall performance of the model in a complex nonlinear problem is improved.

The invention discloses an equipment health state influence factor tracing method, equipment, a medium and a product, and relates to the field of equipment health assessment and management. The method comprises the steps that a sample matrix is formed based on original variables, and principal component statistics features are extracted from principal component features; a Gaussian membership function is adopted to convert the principal component statistic features into evidences; activating evidences by adopting a test sample, and fusing the evidences activated by all features based on an evidence reasoning algorithm to obtain the confidence coefficient of an evidence support state; constructing the relationship between the confidence coefficient of the evidence support state and the principal component statistic feature and the relationship between the original variable and the principal component statistic feature, and obtaining the relationship between the confidence coefficient of the evidence support state and the original variable based on the two constructed relationships; and realizing equipment health state influence factor traceability based on the relationship between the confidence of the evidence support state and the original variable. According to the method, the equipment state can be evaluated under the condition of high-dimensional related variables, and key factors influencing the state change are traced.

The invention relates to the technical field of renewable energy power generation, and particularly provides a multi-type renewable energy integrated power generation control method and system, and the method comprises the steps: obtaining the current power generation data and corresponding environment data of different types of renewable energy power generation equipment, and the current state data of a power generation system; inputting the current power generation data and the environment data into a prediction model established based on a Gaussian membership function, a T-S fuzzy reasoning model and a BP neural network model to obtain a plurality of prediction sequences in a preset time step length; and determining an optimal control sequence based on the prediction sequence, the current state data and a model prediction control algorithm to schedule the current power supply power of the power generation equipment, and updating model parameters of the prediction model in combination with the state data of the power generation system after scheduling. The problems that in the prior art, a multi-type renewable energy power generation control method is difficult to cope with rapid changes of environmental parameters and instantaneous changes of system loads, and the self-adaptive adjusting capacity is insufficient are solved.

The invention discloses an underground water level early warning method and system. The method comprises the following steps: acquiring an underground water level time sequence data set of all stations in a target area; a hierarchical clustering algorithm based on dynamic time warping is adopted to recognize spatial heterogeneity of underground water level changes, and all stations are classified; for each type of stations, acquiring the groundwater level mean value and the water level change rate of each type of stations as input variables, and standardizing the input variables; performing fuzzification processing on the standardized input variables through a Gaussian membership function to generate multiple pieces of input variable fuzzy data; performing fuzzy reasoning on the multiple pieces of input variable fuzzy data according to a preset fuzzy reasoning rule to obtain multiple pieces of output variable fuzzy data; the multiple pieces of output variable fuzzy data are converted into underground water level risk coefficients through a gravity center method; and determining the underground water level early warning level of the corresponding type of station according to the underground water level risk coefficient of each type of station.

The invention relates to the technical field of industrial robots, and discloses a motor fuzzy control industrial robot which comprises the following steps: collecting current motor state information data through a sensor group, and preprocessing the collected motor state information data; and converting the preprocessed data into fuzzy set membership, and determining the degree of each input variable belonging to each linguistic variable by using a membership function. According to the motor fuzzy control industrial robot, the current motor state information data are collected through the sensor set, the collected motor state information data are preprocessed, the influence of noise interference and abnormal values can be remarkably reduced, the reliability and stability of the data are improved, the performance of a control system is improved, and the control precision is improved. And the service life of the equipment can be prolonged, the system can better process the uncertainty and nonlinearity problems through a Gaussian membership function and fuzzy reasoning, and the robustness and adaptability of control are improved.

The invention provides a color box printing self-adaptive drying method based on multi-mode perception, and belongs to the technical field of intelligent manufacturing and printing process control. Multi-modal data in the printing process are collected through a multi-modal sensor; preprocessing the multi-modal feature sequence into a multi-modal feature sequence with consistent space; and inputting the multi-modal feature sequence into the lightweight cross-modal adaptive coding network, obtaining spatial features of each modal through the shallow feature extraction network and performing weighted optimization, capturing a data time evolution rule of each modal through the time dynamic modeling network, and realizing heterogeneous modal fusion and consistency calibration through the modal consistency correction network. And outputting a comprehensive representation vector of the printing ink drying state, inputting the comprehensive representation vector into an adaptive controller, completing feature fuzzification mapping through a Gaussian membership function, and realizing neural fuzzy reasoning based on a fuzzy rule to generate an optimal drying parameter. According to the method, intelligent upgrading is realized on a closed-loop control link of perception, analysis, decision making and correction, and the energy consumption and the rejection rate are remarkably reduced.

The invention provides a coal mining machine cutting part transmission system fault diagnosis method based on a multi-source vibration signal, and the method comprises the steps: S1, installing a plurality of sensors at a plurality of key positions of a coal mining machine rocker arm cutting part transmission system, and carrying out the collection of a multi-channel vibration signal; s2, processing the multi-channel vibration signals to obtain a data set; s3, constructing a deep learning model, and training to obtain a matched fault recognition model so as to output a corresponding preliminary diagnosis result; s4, calculating an error distance among the plurality of preliminary diagnosis results, and mapping the error distance into a trust function by using a Gaussian membership function; and S5, evidence conflict detection is carried out based on the trust function in combination with other related parameters, and fusion diagnosis is carried out by adopting a D-S evidence theory synthesis rule to obtain a diagnosis result. The coal mining machine transmission system is analyzed, the positions of the sensors are reasonably arranged, an omnibearing fault diagnosis system is constructed from the three aspects of mechanical, electrical and hydraulic, and multifunctional fault monitoring and alarming are achieved.

The present invention discloses a laboratory safety management system based on artificial intelligence, including a data acquisition module, a laboratory safety management model establishment module, and a laboratory safety management module. The present invention belongs to the field of data processing, specifically referring to a laboratory safety management system based on artificial intelligence. In this solution, an interval type-2 asymmetric Gaussian membership function is introduced to adapt to the dynamic changes of the laboratory environment in real time and avoid false alarms; a variable interaction weight and a time activation mechanism with a time window are introduced to record the long-term risk trend and accurately identify multi-variable risks in complex environments, thereby improving the laboratory safety management effect; a rule self-adaptive adjustment unit is designed to generate or remove rules in real time based on feature importance and rule usage, automatically adapting to risk changes; a dynamic interval reduction coefficient is introduced to balance the credibility of the upper and lower bound activation intensities according to historical data, reduce the impact of uncertainty on risk assessment, and thus improve the early warning accuracy.

This invention provides a method and system for controlling construction waste crushing based on load feedback. The method includes: acquiring the stator current signal of the crusher's main shaft motor; extracting the active current component representing torque; constructing a time sliding window sequence; calculating the algebraic difference between the current active current component and a preset target value to obtain the original load deviation; constructing a weighted function to correct the load deviation amplitude; obtaining the load change rate by taking the first derivative with respect to time; building a fuzzy logic controller, using a Gaussian membership function in the fuzzification stage to establish a mapping between sample entropy and the width of the Gaussian distribution, and setting the membership function width in real time; adjusting the fuzzy subset of PID parameters according to fuzzy rules; using the peak-to-mean ratio of the sliding window sequence as an impact factor to weight and correct the centroid method defuzzification results; superimposing the adjustment increment onto the PID reference parameters; and combining the corrected load deviation to calculate the control command and adjust the output frequency of the feed motor inverter. This invention enables multi-dimensional adjustment of the crusher.

The invention provides a composite error-based fuzzy fast terminal sliding mode robot control method and system, and the method comprises the steps: constructing a robot dynamic model, decomposing a nominal part and a lumped uncertainty part, enabling a controller to achieve the processing of known dynamic and unknown disturbances, and improving the adaptability of a system; a tracking error and a fast terminal sliding mode surface model are defined, and correction processing is combined, so that the smoothness and the stability of the control process are ensured; a fuzzy basis function is calculated through a fuzzy rule base and a Gaussian membership function, and a composite error driven adaptive law is coupled, so that the system can still maintain high-precision tracking in a steady-state stage; and finally, by integrating the nominal part torque, the self-adaptive compensation torque and the robust switching torque and carrying out amplitude limiting processing on the total torque, high-frequency buffeting of the control torque is restrained, actuator abrasion is reduced, it is guaranteed that the output torque is within a safe range, and the method is suitable for high-dynamic scenes such as precise assembly or medical operation.

The present application relates to the technical field of data classification, and particularly relates to a performance testing method and system of special cable for petrochemical system, comprising: performing quantile transformation on cable performance testing data and dividing the cable performance testing data into multiple feature subsets according to attribute domains, presetting multiple groups of candidate kernel parameters for each feature subset and generating parallel candidate feature representations through kernel principal component analysis, constructing a probability shadow set model for the candidate feature representations, transforming the probability shadow set model to obtain membership degrees by using a Gaussian membership function, calculating local outlier factors and converting the local outlier factors into local uncertainty weights to adjust the membership degree threshold, and then dividing positive domains, negative domains and boundary domains, generating basic probability assignment functions according to the division results, selecting a feature representation combination with the minimum total evidence conflict coefficient as an optimal model, and outputting a cable performance analysis conclusion. The present application can adjust the domain division boundary according to the local distribution characteristics of data points, and reduce the evidence conflict in the multi-granularity fusion process.

The application relates to the technical field of electrical control system monitoring, and discloses a new energy automobile real-time fault detection method and system. The method comprises the following steps: collecting voltage and current control signals of a battery management system and a motor control system to calculate a working condition characteristic vector; a fast-slow double-time-scale state observer is established; an adaptive gain matrix is calculated through Mahalanobis distance and a Gaussian membership function to correct the slow observer and obtain a health parameter estimation value; a fast observer residual error is used to determine a sudden fault and analyze a health parameter trend to determine a gradual change fault. The application solves the problem that, in the prior art, the observer gain cannot adapt to changes in multiple working conditions, resulting in a decrease in estimation accuracy and an increase in false alarm rate.