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49 results about "Fuzzy inference engine" patented technology
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Mamdani fuzzy inference is the most commonly seen fuzzy methodology and was among the first control systems built using fuzzy set theory. It was proposed in 1975 by Ebrahim Mamdani [1] as an attempt to control a steam engine and boiler combination by synthesizing a set of linguistic control rules obtained from experienced human operators.
Intelligent robust control system for motorcycle using soft computing optimizer
A Soft Computing (SC) optimizer for designing a Knowledge Base (KB) to be used in a control system for controlling a motorcycle is described. In one embodiment, a simulation model of the motorcycle and rider control is used. In one embodiment, the simulation model includes a feedforward rider model. 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; 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 plant model of the controlled plant. The SC optimizer produces a robust KB that is typically smaller that the KB produced by prior art methods.
Owner:YAMAHA MOTOR CO LTD
Intelligent electronically-controlled suspension system based on soft computing optimizer
InactiveUS20060293817A1Near-optimal FNNMaximises informationDigital data processing detailsAnimal undercarriagesInput/outputSoft computing
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.
Owner:YAMAHA MOTOR CO LTD
Improved storage battery SOC estimation method based on consistency of unit cell
InactiveCN102569922AJudgment of ConsistencySecondary cells servicing/maintenanceNeural learning methodsData setFuzzy rule
The invention relates to an improved storage battery SOC (State Of Charge) estimation method based on the consistency of a unit cell. In the method, the training data set of a storage battery, SOC predicted data corrected on the basis of consistency, and a self-adoption neural network are use for building a fuzzy rule base with an optimized structure and parameters; and then the fuzzy rule base after off-line learning is implanted into the fuzzy inference machine of an embedded controller of BMS (Battery Management System), so as to carry out on-line correction to storage battery SOC estimation. Compared with the prior art, the invention has the advantage that SOC differences in all unit cells inside the battery can be factually reflected on entire SOC estimation.
Owner:上海松岳电源科技有限公司
Improved smith predicting controller
InactiveCN104570729AImprove adaptabilityGuaranteed stabilityAdaptive controlSelf adaptiveFuzzy adaptive
An improved Smith predicting controlling algorithm comprises the following steps: designing a fuzzy adaptive PID controller to replace an original PID controller, wherein the fuzzy adaptive PID controller mainly comprises a PID control regulator and a fuzzy inference engine, and modifying a PID control parameter according to a fuzzy control rule in real time by using an error e and the change rate ec of the error as the input of the controller and the kp, ki and kd of the PID control parameter as the output, so as to meet the requirements of e and ec for the self-regulation of the PID parameter at different moments; adding an inner feedback loop, sending a feedback signal to a predicting model and an input end of an actually-controller object by the inner feedback loop at the same time, comparing the feedback signal with the output of a main controller, using the difference as an input signal of the actually-controlled object and the predicting model and introducing an adaptive regulator. Therefore, the problem of deviation caused by the change of an open-loop gain parameter is solved by changing the value of a proportional controller continuously.
Owner:NORTHEAST FORESTRY UNIVERSITY
Soft computing optimizer of intelligent control system structures
InactiveUS7219087B2Near-optimal FNNTelevision system detailsChaos modelsEngineeringInternal combustion engine
The present invention involves a Soft Computing (SC) optimizer for designing a Knowledge Base (KB) to be used in a control system for controlling a plant such as, for example, an internal combustion engine or an automobile suspension system. 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 plant model of the controlled plant. The SC optimizer produces a robust KB that is typically smaller that the KB produced by prior art methods.
Owner:YAMAHA MOTOR CO LTD
Intelligent robust control system for motorcycle using soft computing optimizer
Owner:YAMAHA MOTOR CO LTD
Fuzzy neural network model and intelligent prediction method for deep excavation deformation
InactiveCN103116804ATroubleshoot input dependenciesGood explanatory abilityFuzzy logic based systemsNeural learning methodsFuzzy inferenceDefuzzification
The invention discloses a fuzzy neural network model based on a multivariable phase-space reconstruction theory and an intelligent prediction method for deep excavation deformation by using the fuzzy neural network model. The fuzzy neural network model consists of four functional modules including a fuzzification interface, a fuzzy rule knowledge base, a fuzzy inference machine and a defuzzification device. The prediction model and the prediction method have high accuracy, and can effectively avoid major losses of countries and people's lives and properties.
Owner:SUZHOU UNIV OF SCI & TECH
Proportional amplifier PID parameter self-tuning control method, and proportional amplifier and proportional electromagnetic valve
ActiveCN103713519AImprove controlAvoid mechanical wearOperating means/releasing devices for valvesPower amplifiersFuzzy inferenceAudio power amplifier
The invention provides a proportional amplifier PID parameter self-tuning control method comprising the following steps: a real-time parameter value of an output end of the proportional amplifier is detected, and deviation and deviation change rate are calculated according to the real-time parameter value and a given parameter value; the deviation and the deviation change rate are fuzzified into a corresponding deviation fuzzy subset and a deviation change rate fuzzy subset; a PID parameter fuzzy subset is obtained according to the deviation fuzzy subset, the deviation change rate fuzzy subset and a PID parameter fuzzy inference mechanism; after the PID parameter fuzzy subset is de-fuzzified, a PID control parameter is obtained according to a PID parameter initial value and the de-fuzzified change value; output of the proportional amplifier is controlled according to the PID control parameter, the real-time parameter value of the output end of the proportional amplifier is detected again, and the steps are repeated until the real-time parameter value of the output end of the proportional amplifier is equal to the given parameter value. PID parameters are controlled in fuzzy and self-tuning ways so that the proportional amplifier can be self-adaptive to various application occasions. The invention also provides one type of proportional amplifier and a proportional electromagnetic valve.
Owner:NINGBO HOYEA MACHINERY MFG
Power plant fan fault early warning system based on fuzzy reasoning
PendingCN110322018ASolve the difficult problem of early warningRealize real-time monitoringData processing applicationsInference methodsEngineeringDynamic database
The invention relates to a power plant fan fault early warning system based on fuzzy reasoning. The system comprises a fan real-time online monitoring module which comprises a fan real-time data acquisition unit, a signal processing unit and a fault feature extraction unit; a fan operation state knowledge base module which comprises a fault rule base unit, a static database unit and a dynamic database unit; and a fuzzy inference engine module which comprises a fuzzy inference unit, a forward inference unit, a reverse inference unit; and an early warning threshold determination module, a fuzzyknowledge acquisition module, an early warning interpretation mechanism module and a human-computer interface interaction module. Compared with the prior art, the problem that early warning is difficult due to strong coupling, nonlinearity, variable working conditions, various fault types and the like of a power plant fan in the traditional sense is solved, real-time monitoring of the state of thefan is achieved, the fault type of the fan can be judged, fault occurrence points can be traced back, and early warning reasons can be analyzed.
Owner:SHANGHAI UNIVERSITY OF ELECTRIC POWER
Multi-parameter monitoring device and method suitable for abnormal state of mechanical device
The invention discloses a multi-parameter monitoring device and method suitable for the abnormal state of a mechanical device. The device comprises a data analysis and fault diagnosis server, and a plurality of early warning monitoring terminals and at least two data information acquisition terminals which are in signal connection with the data analysis and fault diagnosis server. During monitoring, the data analysis and fault diagnosis server reads various sensing data, preprocesses the sensing data, performs abnormal vibration analysis and mechanical fault diagnosis on the preprocessed vibration data, obtains a sensing data fusion estimation result by adopting a fuzzy reasoning mechanism, and outputs a result of the overall operation state of the mechanical device. According to the monitoring method, an abnormal vibration analysis and mechanical fault diagnosis method is adopted to judge the abnormal vibration type, the fuzzy reasoning is conducted on the mechanical device operationstate according to a fuzzy reasoning rule knowledge base, and the overall operation state of mechanical device is evaluated, so that the long-term online unattended abnormal state monitoring and faultearly warning of multiple devices are realized.
Owner:LANZHOU INST OF TECH
Roaming switching control method and device and electronic equipment
The invention discloses a roaming switching control method and device and electronic equipment, and the method comprises the steps: carrying out the sampling according to a preset sampling time interval, and obtaining the current state information of a network system where a current wireless AP connected with a to-be-roamed client is located; inputting the current state information into a preset fuzzy inference system, and according to a preset fuzzy inference mechanism in the fuzzy inference system and a rule set containing a plurality of fuzzy logic rules, performing inference to obtain roaming quality determined by downlink signal intensity corresponding to each wireless AP and the load quantity of the client; determining the optimal roaming quality from all the roaming qualities, and taking the wireless AP corresponding to the optimal roaming quality as a target wireless AP; and controlling the to-be-roamed client to roam from the current wireless AP to the target wireless AP. According to the invention, the roaming switching control can be carried out by adopting the rule set suitable for the wireless network environment, and the most suitable target wireless AP can be accurately determined.
Owner:TP-LINK
Weld tracking application adaptive fuzzy control method
InactiveCN107390528AAdaptive, real-time and accurateSolve the problems of instability, poor dynamic performance and rough control processAdaptive controlFuzzy ruleControl engineering
The invention discloses a weld tracking application adaptive fuzzy control method comprising the following steps: 1, fuzzifying an obtained discrepancy e and a deviation change rate (e<.>=de / dt) so as to obtain a fuzzy variable deviation E and a deviation change rate EC; 2, combining with expert knowledge and operation experiences, and building fuzzy rules according to the deviation e and deviation change rate ec; 3, inputting the inputted fuzzy variable deviation E and the deviation change rate EC into a fuzzy inference engine so as to obtain a fuzzy output value U; 4, using a maximum value method to defuzzify, and converting the fuzzy output value U into an accurate value u; 5, introducing a controller scale factor ki (i=1, 2,..n) and a controller quantification factor ku, and setting input / output fuzzy universe and a fuzzy rule function; 6, real time updating the fuzzy rule function according to the input / output value changes, and calculating and outputting a offset voltage value of a controlled object. The method can adjust the fuzzy controller parameters in a control process, thus solving the problems that the control process is unstable, poor in dynamic performance and rough in the process.
Owner:SOUTH CHINA UNIV OF TECH
Hybrid intelligent control method and system for power generating apparatuses
A present invention relates to a novel hybrid intelligent control system and method for power generating apparatuses, in which the control system comprises: a fuzzy sliding mode speed controller, embedded with a fuzzy inference mechanism so as to be used for controlling the speed of a power generating apparatus; and a radial basis function network (RBFN) pitch controller, being embedded with an on-line training RBFN so as to be used for controlling the pitch angle of a turbine coupled to the power generating apparatus. In a variable-speed energy conversion system using the aforesaid control system, the turbine can be driven to operate at its maximum efficiency by adjusting its blade pitch angle in response to the variation of the input flowing into the turbine, while allowing the shaft speed of the power generating apparatus to be controlled by a fuzzy interference mechanism so as to achieve its maximum power output.
Owner:INST NUCLEAR ENERGY RES ROCAEC
High-conflict evidence fusion method based on fuzzy reasoning
ActiveCN110188882AImprove accuracyReduce the impactInternal combustion piston enginesInference methodsFuzzy reasoningData mining
The invention discloses a high-conflict evidence fusion method based on fuzzy reasoning, relates to a road condition assessment method based on an automobile physical information fusion system, and belongs to the technical field of multi-sensor data fusion. The method is based on the VCPS technology. The method comprises the steps of acquiring real-time operation information of an automobile, forming evidence for judging the current road condition by multiple automobiles, calculating support probability distribution of an evidence source through a support probability function, further obtaining a maximum distance and an average distance of probability distribution between evidences, and performing effective measurement on a conflict degree between the evidences through a fuzzy reasoning mechanism. And on the basis, the support degree and the credibility of the evidence can be obtained. The method also considers the uncertain information of the evidence at the same time, and calculatesthe relative importance of the evidence through Dane entropy. And the weight of the evidence is obtained by combining the credibility and the importance, and finally weighted average is performed on the evidence to obtain an average evidence. And finally, the average evidence is integrated for multiple times by utilizing a DS combination rule to obtain a reliable assessment evidence.
Owner:HUNAN UNIV
Intelligent air bag system
InactiveUS6889215B2Pedestrian/occupant safety arrangementDigital computer detailsEngineeringBiological activation
An intelligent air bag system includes a controller communicating with a sensor suite and the air bag. The controller preferably includes a fuzzy inference engine. The sensor suite includes a plurality of sensors such as weight sensors, acceleration sensors, seat belt activation sensors, and the like. The fuzzy inference engine determines deployment by using a rule base including a multiple of parameters. The decision to deploy the air bag and the strength of the air bag deployment is determined by controller using fuzzy logic to more particularly tailor deployment to present conditions.
Owner:IMS
Autonomous and collaborative driving decision making method for autonomous vehicle
ActiveCN108733063AImprove driving safetyReduce travel timePosition/course control in two dimensionsFuzzy logic inferenceFuzzy rule
The invention proposes an autonomous and collaborative driving decision making method for an autonomous vehicle, and the method comprises the steps: collecting the moving state information of a vehicle through a vehicle-mounted detection system; employing a fuzzy function for achieving the fuzzification process of moving state information parameters of the vehicle, and estimating the moving statesof the vehicle and other neighbor vehicles; taking the moving state information parameters after fuzzification as the input parameters of an indistinct logic computer, and enabling the indistinct logic computer to infer a moving mode of the vehicle according to a set fuzzy rule; outputting an optimal driving mode decision making result of an independent vehicle through defuzzification processing,and achieving the autonomous and collaborative driving among the vehicles of a cluster. The beneficial effects of the invention are that the method achieves the autonomous and collaborative driving among the vehicles of the cluster through the information detection, computing and communication capabilities of the autonomous vehicle and the fuzzification and fuzzy logic inference of a vehicle moving stat parameter set, provides a reference for the selection of an optimal driving mode for the independent vehicle, improves the driving safety of a vehicle, reduces the travel time of the vehicle,and reduces the traffic energy consumption.
Owner:NANTONG UNIVERSITY +1
Landslide hazard degree evaluation method under support of GIS (Geographic Information System) and artificial intelligence technology
InactiveCN105184050AOvercoming sample qualityOvercoming demandsSpecial data processing applicationsSmart technologyFuzzy membership function
The present invention discloses a landslide hazard degree evaluation method under support of a GIS (Geographic Information System) and an artificial intelligence technology. The method comprises: acquiring knowledge about a relation between a landslide and a geographical environment by interviewing a landslide expert, and screening geographical environment factors according to conditions, such as a degree of influence of the geographical environment factors on the landslide, data source quality and an acquisition difficulty degree, in expert knowledge; extracting quantitative geographical environment factors by use of the GIS; expressing the expert knowledge about the relation between the landslide and the geographical environment factors by use of a fuzzy membership degree function, and constructing a fuzzy inference engine capable of calculating a landslide hazard degree value based on the geographical environment factors; and inputting the quantitative geographical environment factors into the fuzzy inference engine, and calculating spatial distribution of a regional landslide hazard degree. The method disclosed by the prevent invention effectively overcomes the defects such as high sample quality and quantity requirements, and poor interpretability and poor portability of a statistical method, improves precision and the detail level of landslide hazard degree spatial distribution speculation, and has extensive application prospects in the aspects of regional landslide hazard degree analysis and landslide disaster prevention and reduction.
Owner:NANJING NORMAL UNIVERSITY
Single intersection traffic signal fuzzy controller
The invention belongs to the field of fuzzy control, and particularly relates to a single intersection traffic signal fuzzy controller which comprises a knowledge base (A*oR), a fuzzy inference engine (oR), quantification factors (Kx, Ky and Kt), a fuzzy input module (D / F) and a clearness output module (F / D), two-input signals are traffic flows x1 and y1 in an annular coil vehicle detector acquisition road network, a single-output single is real-time phase green light extension time u, the traffic flows x1 and y1 are converted into fuzzy input variables X and Y through the fuzzy input module (D / F), the fuzzy input variables X and Y are inputted to the fuzzy inference engine (oR) and ratiocinated and analyzed by the aid of the knowledge base (A*oR) to obtain a fuzzy control green light extension time output variable U, and the real-time phase green light extension time u can be acquired through the clearness output module (F / D). Current phase traffic flow information can be fuzzily controlled, a green light signal is delayed when the traffic flow is large, so that the traffic flow is evacuated, and smooth traffic is realized.
Owner:NANTONG VOCATIONAL COLLEGE
Adaptive color image steganography method based on fuzzy inference system
ActiveCN110751591AIncrease imperceptibilityIdeal embedding capacityCharacter and pattern recognitionImage data processing detailsPattern recognitionColor image
The invention discloses an adaptive color image steganography method based on a fuzzy inference system, which comprises the following steps: step 1, separating color channels of a color carrier image,and extracting features of each color channel of the carrier image; 2, determining a membership function, giving a fuzzy rule, taking the features extracted in the step 1 as input, obtaining a fuzzyoutput through a fuzzifier and a fuzzy inference engine, and obtaining a clear output value through a defuzzifier; and step 3, judging each pixel type, estimating the embedding capacity of each pixeltype according to the complexity of the color channel, encrypting information to be hidden, adaptively embedding secret information with different digits for different pixel types by utilizing an LSBalgorithm, and combining image color channels to obtain a secret-loaded image. According to the method, the color space complexity of the color image is fully considered, different capacities are adaptively embedded according to the texture complexity and visual characteristics of the image, and the image embedding capacity, imperceptibility and secret information security can be improved.
Owner:FUJIAN NORCA TECH
Fuzzy control method for nonlinear precise forging press
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.
Owner:FOSHAN SHUNDE IND & INFORMATION TECHRES CENT
Energy-saving fuzzy controller method for synchronous motor
InactiveCN109639207AQuick responseImprove dynamic and static performanceAC motor controlElectric motor controlFuzzy inferenceSynchronous motor
The invention discloses an energy-saving fuzzy controller method for a synchronous motor, which comprises a fuzzy interface, a knowledge base, a fuzzy inference machine and a defuzzy interface. The method comprises the following steps of step 1, constructing a fuzzy controller in a Matlab simulation system; Step 2, combining the fuzzy controller constructed in the step 1 with a traditional PID controller to form a fuzzy PID controller. According to the invention, a composite controller is designed to improve the speed response, so that the system is good in dynamic and static performance. Therequirements of a high-precision system are met.
Owner:BAOJI UNIV OF ARTS & SCI
Reactive compensation control system and reactive compensation control method based on fuzzy control
InactiveCN105790284AGuaranteed uptimeReactive power adjustment/elimination/compensationReactive power compensationCapacitanceElectricity
The invention discloses a reactive compensation control system and a reactive compensation control method based on fuzzy control. The reactive compensation control system comprises a fuzzification interface unit, a fuzzy inference machine unit and a defuzzification interface unit. The fuzzification interface unit obtains a fuzzy set and a membership degree table of voltage deviations and power factor deviations. The fuzzy inference machine unit obtains a fuzzy control regulation table and obtains a fuzzy relationship R between a fuzzy control relationship matrix A of the voltage deviation eu and a fuzzy control relationship matrix B of the power factor deviation ephi. The reactive compensation method comprises an initializing step, a capacitance inputting control step and a capacitance eliminating control step. A power factor is calculated and compared with a preset value. When the power factor is lower than the preset value, the fuzzy control module performs the capacitance inputting control step; and when the power factor is higher than the preset value, the fuzzy control module performs an electric eliminating control step. The reactive compensation control system and the reactive compensation control method have advantages of controlling a reactive power absorbed from the power grid side by a load within a certain range, and furthermore preventing oscillation caused by repeated switching of a capacitor set.
Owner:JINAN CITY CHANGQING DISTRICT POWER SUPPLY CO OF STATE GRID SHANDONG ELECTRIC POWER CO +1
Novel electric loading system control method
The invention belongs to the technical field of motor control methods, and particularly relates to a novel electric loading system control method. For solving the problem that loading accuracy and stability of an existing intelligent electric loading system control method are difficult to ensure, the compound control method based on fuzzy CMAC and an indistinct logic computer is provided. The control method includes the steps that in fuzzy CMAC control, a loading instruction signal is mapped to all units in space, then membership degrees of an input signal to all fuzzy sets serve as weight information of corresponding weight values, and weighting summation is carried out on all the weight values to obtain preset output approximate values. Meanwhile, activated input fuzzy sets in the indistinct logic computer are mapped into all output fuzzy sets, and an improved high defuzzifier is used for calculation to reach increment needed by preset output; finally the two are added to obtain a final output signal. The electric loading system control method based on fuzzy CMAC and the indistinct logic computer can improve system control precision and ensures system stability.
Owner:BEIHANG UNIV
System and method of multi-classification based on limited fuzzy rule in big data environment
InactiveCN107818342AImprove classification efficiencyReduce mistakesCharacter and pattern recognitionFuzzy inferenceBasic knowledge
The present invention relates to a system and a method of multi-classification based on a limited fuzzy rule in a big data environment, belonging to the big data classification field. The system comprises a fuzzy generator, a fuzzy inference machine, a basic knowledge base and a defuzzifier. The fuzzy generator performs one-to-one mapping of points determined by an input discourse domain U to a fuzzy set on the U; the basic knowledge base is formed by a plurality of fuzzy rule 'if-then' rules, the fuzzy rules comprise many types, and each type of fuzzy rule is formed by a data rule and a basicrule; the fuzzy inference machine employs the fuzzy rules to correspond the fuzzy set on the discourse domain U and a fuzzy set on an output discourse domain V on the basis of a fuzzy logic principle; and the defuzzifier performs one-to-one mapping of the fuzzy set on the V to points determined on the V. The system and the method of multi-classification based on the limited fuzzy rule in the bigdata environment greatly improve the classification efficiency, and perform supplement of positive and negative rules of determinative rules so as to reduce errors caused by fuzzy operation and ensurea classification accuracy.
Owner:CHONGQING UNIV OF POSTS & TELECOMM
Air compressor group load prediction method and control equipment thereof
PendingCN110443428AReduce energy consumptionImprove estimation accuracyEnergy industryForecastingFuzzy ruleReconstruction method
The invention provides an air compressor group load prediction method and control equipment thereof. Prediction is carried out based on a fuzzy inference machine of a Mamdani model, and the method mainly comprises the following steps: step A, constructing a time sequence model based on a phase space reconstruction method, and inputting the time sequence model into the fuzzy inference machine; stepB, initializing a fuzzy rule based on a fuzzy C-mean clustering algorithm, and adjusting membership parameters of the fuzzy rule based on a maximum density method to construct a fuzzy rule base; stepC, the fuzzy inference machine in the step A, based on the fuzzy rules in the fuzzy rule base in the step B, calculating output prediction results. The invention provides an air compressor group loadprediction method. According to the method, the compromise of the coverage degree and the specificity of the fuzzy rule membership function in the Mamdani fuzzy system can be realized, so that the estimation accuracy of the load amplitude of the air compressor is improved. The energy consumption of an air compressor group can be effectively reduced, and the production cost of an enterprise is reduced.
Owner:上海宝能信息科技有限公司 +1
Method for verifying integrity of investigation information
ActiveCN106326472AImprove processing efficiencyImprove processing precisionData processing applicationsSpecial data processing applicationsInformation processingInformation integration
The invention provides a method for verifying the integrity of investigation information. The method includes the steps that investigation information is read from a database; the investigation information is preprocessed based on a preprocessing rule base, and feature extraction is carried out to obtain feature vectors; the obtained feature vectors serve as input vectors of a fuzzy inference machine, and a feature-level integrity verification result of the feature vectors is obtained; comprehensive decision-making processing is carried out on the feature-level integrity verification result, and an investigation information integrity verification result is obtained. The method suitable for verifying the integrity of the investigation information is designed, and the investigation information is preprocessed three times in a progressive mode; besides, the processing process can be backtracked, so that processing rules are easy to modify, and the investigation information processing efficiency and processing precision are improved; moreover, integrity verification is carried out based on the feature level first, and then integrity verification is carried out after feature-level verification is modified according to conflicts and the Euclidean distance, so that the precision of investigation information integrity verification is improved, and the processing speed of investigation information integrity verification is improved.
Owner:GUANGDONG JINGAO INFORMATION TECH CO LTD +1
Improved storage battery SOC estimation method based on consistency of unit cell
InactiveCN102569922BJudgment of ConsistencySecondary cells servicing/maintenanceNeural learning methodsData setFuzzy rule
The invention relates to an improved storage battery SOC (State Of Charge) estimation method based on the consistency of a unit cell. In the method, the training data set of a storage battery, SOC predicted data corrected on the basis of consistency, and a self-adoption neural network are use for building a fuzzy rule base with an optimized structure and parameters; and then the fuzzy rule base after off-line learning is implanted into the fuzzy inference machine of an embedded controller of BMS (Battery Management System), so as to carry out on-line correction to storage battery SOC estimation. Compared with the prior art, the invention has the advantage that SOC differences in all unit cells inside the battery can be factually reflected on entire SOC estimation.
Owner:上海松岳电源科技有限公司
Fuzzy reasoning coprocessor
InactiveCN1093287CSimple structureWork well togetherDigital data processing detailsSpecific program execution arrangementsCoprocessorAlgorithm
The present invention relates to a fuzzy reasoning coprocessor, including: fuzzy reasoning machine which features that its algorithm flow chart includes input scale factor computation, input fuzzing,fuzzy reasoning, output anti-fuzzing and output scale factor computation; fuzzy knowledge base containing rule, membership functiona nd scale factor, which is the basis of whole fuzzy reasoning process; I / O buffer memory for storing controller input and output variables and intermediate data in the fuzzy reasoning process; and main processor interface, which is in charge of the coordination work between fuzzy reasoning coprocessor and main processor.
Owner:宁波中科集成电路设计中心有限公司
Solid-liquid phase change material surface heat flow measurement method and system based on dispersive fuzzy reasoning mechanism
PendingCN114018972AImprove utilizationImprove utilization efficiencyMaterial heat developmentInference methodsEngineeringInternal radiation
Owner:ZHUZHOU NAT INNOVATION RAILWAY TECH CO LTD
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