797 results about "Fuzzy rule" patented technology

A system generates optimal global set points for an environmental management system. The system comprises a system model for modeling components of a thermal plant, an objective function for modeling a parameter of the thermal plant, and an optimization engine for optimizing the parameter modeled by the objective function. The system model is coupled to an input data collector for receiving building data and weather data corresponding to a particular site. The system model includes models for thermal plant system components that may be implemented using classical models or artificial intelligence models. Classical models are those models that are implemented using linear programming, unconstrained non-linear programming, or constrained non-linear programming methodologies. The artificial intelligence models are those models that may be implemented using a fuzzy expert control system with crisp and fuzzy rules, genetic algorithms for optimization, or neural networks.

The invention relates to a PMSM servo system control method based on fuzzy and active disturbance rejection control. A position signal is given by a differential tracker to arrange a transition process so that the contradiction between rapidness and overshoot of a system is solved, and uncertainty, friction torques and external disturbance due to modeling errors of the system are observed via an expansion state observer; according to the error between the differentials generated by the differential tracker and state variation generated by the expansion state observer, a fuzzy inference rule is obtained with application of experimental experience of technical staff, so that a fuzzy rule control table of an error proportion coefficient, a differential coefficient and an integration coefficient is established; accurate control amount is obtained after de-fuzzification, so that parameter self-adaptive adjustment of a nonlinear error feedback control law is realized; and compensation amounts of the nonlinear error feedback control law and the expansion state observer to the total disturbance forms the control amount, thereby realizing optimal control for an controlled object. The method of the invention improves both tracking precision and disturbance rejection capability of the system.

The invention discloses an intelligent control method of a driverless vehicle tracking a desired trajectory. The method is characterized by comprising the following steps of: establishing a body coordinate system, and determining the position information of the vehicle; calculating a membership degree function of a road curvature characteristic according to a radian value, and defining a fuzzy rule to calculate a decision output speed; according to the distance the vehicle runs for 1.5-2.5 seconds, searching for the trajectory points larger than the distance and closest to the vehicle in the coordinate sequence of the desired trajectory; calculating the moving curvature of the vehicle according to an adaptive proportion-differential-integral control algorithm formula; and finally judging whether the target point of the current control cycle is the last point selected from the road point sequence; if so, outputting a signal to a brake servo control system to start the brake to slow down the vehicle; otherwise, outputting a voltage signal by a controller local area network module to keep and control the accelerator opening so that the vehicle continues proceeding. Through adoption of the intelligent control method, the driverless vehicle can realize a function of tracking the desired trajectory, and has certain adaptability to the change of the road curvature characteristic.

The method discloses a fuzzy matching-based Chinese geo-code determination method, which comprises the following steps: A1, reading descriptive Chinese address information in and adopting a forward maximum searching method to split an original address to obtain an original address element array in a way that the levels of administrative regions are taken as breakpoints; A2, standardizing original address elements through an address dictionary; and A3, reading a standard address tree, adopting a branch-bound algorithm to match the original address element array, simultaneously, utilizing fuzzy rules to control the matching operation, and after acquiring keywords after the original address is split, taking a matching result with the highest evaluation score as the most approximate matching result to obtain a more accurate matched address. The invention provides the fuzzy matching-based Chinese geo-code determination method, which has the advantages of rational address model, relatively higher matching rate and high speed.

A method and apparatus for determining the status of a computer system and software applications running on that system and displaying the status to a system administrator are provided. With the apparatus and method, metrics related to a particular application or subsystem are identified and then collected over a predetermined period of time using a data monitoring or collection facility to generate metric history data. Once collected, the metric history data is analyzed by computing a set of parameters representing statistical measures of the metric history data. A set of fuzzy rules are used to define the relationships between metrics and the ultimate application or subsystem status. This metric history analysis phase may be performed periodically such that the fuzzy sets are dynamically redefined at periodic intervals. The fuzzy rules are then evaluated using a fuzzy reasoning process and an overall status indication is generated. As system performance or status changes, the monitoring system can adapt by changing the shape of the “normal” fuzzy set based on the distribution of metric values. The rules may remain the same but the fuzzy set may change dynamically. This greatly reduces maintenance costs since the monitoring rule set can be slowly tuned over time, while the underlying “normal” fuzzy sets could be adjusted as often as needed. Thus, the method and apparatus provide a mechanism to express the knowledge about the key underlying relationships as fuzzy rules and then to automatically tailor the fuzzy sets that are referenced in the fuzzy rules using statistical data mining techniques.

The invention relates to a quadrotor unmanned plane control method based on a fuzzy expansion state observer and an adaptive sliding formwork. The method comprises steps that a quadrotor unmanned plane system model is established, and a system state and controller parameters are initiated; a tracking differentiator is designed; a non-linear expansion state observer is designed; fuzzy rules are established; a parameter adaptive law is designed; an adaptive sliding formwork controller is designed. According to the method, the expansion state observer is designed to estimate indetermination of a system model and external disturbance; a pole assignment method is utilized to determine an initial value of an expansion state observer parameter; the fuzzy rules are introduced to realize online setting for the expansion state observer parameter; the parameter adaptive law is designed to acquire ideal controller gain; the adaptive sliding formwork controller is designed, rapid stabilization of a system tracking error and convergence to a zero point are guaranteed, and rapid and stable position tracking and pose adjustment of the quadrotor unmanned plane are realized. The method is advantaged in that system performance is improved, and rapid and stable system position tracking and pose adjustment are realized.

The present invention relates to an entity relationship recognition method and apparatus. The method comprises obtaining a statement sequence from a target text in a corpus, and performing named entity recognition and dependency grammar marker on the statement sequence to obtain a marked text sentence; matching and retrieving the marked text sentence on basis of an entity relationship seed to obtain a training example; replacing the entity relationship seed word in the training example with predetermined identification, processing the training example after replacement combined with the named entity recognition and the dependency grammar marker, and generating a candidate rule; fuzzifying the candidate rule to obtain fuzzy rules; determining whether the fuzzy rules comprise a new rule; and retrieving the corpus according to the fuzzy rules to obtain a seed set when the fuzzy rules comprise the new rule, and using the obtained seed set as an entity relationship recognition result. Manual participation can be effectively reduced, dependence on the calibrated corpus is reduced, a new entity relationship can be found timely, and the entity relationship recognition method and apparatus are self-adaptive to entity relationship mining in different fields.

Appropriate updates to a respective user profile are inferred from monitored user activity. Such updates are suitable for use with applications programs that customize services based upon data and attributes stored in a user profile. The apparatus is arranged to personalize various aspects of a preferred profile updating process on the basis of stored user preferences, applying meta-rules to apply those user preferences to the definition of a set of personalized rule weights to be applied in a fuzzy inference process that infers profile updates by applying fuzzy rules and fuzzy sets to the analysis of event statistics derived from the results of user monitoring. The personalized rule weights are applied to selectively adjust the effect of particular fuzzy rules. In addition, mapping of event statistics to fuzzy rule premises in the fuzzy inference process may be personalized through the use of user-defined fuzzy sets.

A method and a device for motion estimated and compensated Field Rate Up-conversion (FRU) for video applications is disclosed and claimed. The invention provides for dividing an image field to be interpolated into a plurality of image blocks, where each image block includes a respective set of image elements of the image field. In one embodiment, for each image block of a subset of image blocks, a group of neighboring image blocks is selected. A motion vector for the image block is estimated that describes the movement of the image block from a previous image field to a current image field on the basis of predictor motion vectors associated to the group of neighboring image blocks. Each image element of the image block is determined by interpolation of two corresponding image elements in the previous and current image fields related by the estimated motion vector. To estimate a motion vector, the invention provides for applying each of the predictor motion vectors to the image block to determine a respective pair of corresponding image blocks in the previous and current image fields. For each of the pairs of corresponding image blocks, an error function which is the Sum of luminance Absolute Difference (SAD) between corresponding image elements in the pair of corresponding image blocks is evaluated. For each pair of the predictor motion vectors, a degree of homogeneity is also evaluated, followed by the application of a fuzzy rule having an activation level that is proportional to the degree of homogeneity of the pair of predictor motion vectors and the error functions of the pair of predictor motion vectors. An optimum fuzzy rule having the highest activation level is selected, from which the best predictor motion vector is determined, having the smaller error function of the pair associated to the optimum fuzzy rule. In most cases, the motion vector for the image block is estimated on the basis of the best predictor motion vector.

The invention discloses a fuzzy control based automotive intelligent cruise assisted driving system control method. The method includes the steps: firstly, acquiring speed VF of a front key target vehicle, relative distance Sr and real-time speed Vh of a cruiser; judging whether the front vehicle exists or not, and entering an intelligent following mode if yes; otherwise, entering a constant-speed cruise mode; for the intelligent following mode, taking relative distance deviation RD and relative speed deviation RV as input parameters, and taking an output linguistic variable as pedal increment u of an accelerator pedal or brake pedal at one movement; according to the fuzzy rule, determining a fuzzy value of an output variable u; adopting a centroid method to perform defuzzification computation to obtain an accurate controlled variable so as to control throttle percentage or brake pedal travel. By the control method, automatic regulation and trigger control of the throttle percentage and brake pedal travel can be realized at the same time, an integrated control function integrating ACC(active cruise control) and run-stop control can be realized, running safety of an automobile is improved, and driving fatigue of a driver is alleviated.

The invention provides a reinforcement learning based air combat maneuver decision making method of a UAV. A motion model of an airplane platform is created; principle factors that influence the air combat situation are analyzed; on the basis of the motion model and analysis on the air combat situation factors, a dynamic fuzzy Q learning model of air combat maneuver decision making is designed, and essential factors and an algorithm flow of reinforcement learning are determined; a state space of air combat maneuver decision making is fuzzified and serves as state input of reinforcement learning; typical air combat motions are selected as basic motions of reinforcement learning, and the triggering intensities of fuzzy rules are summed in a weighted manner, and a continuous motion space is covered; and on the basis of an established air combat dominant function, a return value of reinforcement learning is set in a rewards and punishment values weighing-superposing method. Thus, the autonomous maneuver decision making capability of the UAV during air combat can be improved effectively, the robustness is higher, an autonomous searching optimization performance is higher, and decisionsmade by the UAV are improved continuously in continuous simulation and learning.

An explicit assumption of continuity is used to generate a fuzzy implication operator, which yields an envelope of possibility for the conclusion. A single fuzzy rule A B entails an infinite set of possible hypothese A′B′ whose degree of consistency with the original rule is a function of the “distance” between A and A′ and the “distance” between B and B′. This distance may be measured geometrically or by set union/intersection. As the distance between A and A′ increases, the possibility distribution B* spreads further outside B somewhat like a bell curve, corresponding to common sense reasoning about a continuous process. The manner in which this spreading occurs is controlled by parameters encoding assumptions about (a) the maximum possible rate of change of B′ with respect to A′ (b) the degree of conservatism or speculativeness desired for the reasoning process (c) the degree to which the process is continuous of chaotic.

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.

The invention relates to a method for distinguishing working conditions and managing and controlling the energy of a plug-in hybrid electric vehicle. The method disclosed by the invention mainly comprises two parts of a method for distinguishing the working condition and a method for managing and controlling the energy. For the working condition distinguishing part, a support vector machine (SVM) model is adopted for training and studying the characteristic parameters of the working conditions so as to distinguish real-time working conditions; for the energy managing and controlling part, a fuzzy rule is formulated. Through the method for distinguishing the working conditions and managing and controlling the energy, and on the premise of ensuring power performance, the fuel economy of the vehicle can be markedly improved, and the energy conservation and emission reduction can be realized.

The invention discloses a method for predicting residual service life of a flight control computer system, which comprises the steps of: storing historical reliability data of the flight control computer system by using an onboard health monitoring computer, obtaining working state data, and executing failure detection, reliability analysis and service life prediction of the flight control computer by using a fuzzy expert system. The working state data is obtained from a state sensor group. The development of the fuzzy expert system is used for modeling the reliability of the flight computer system, and the working condition of the flight control computer system is estimated according to the working state data and the fuzzy based reasoning for carrying out failure detection and predicting residual service life. The invention has the advantage of being capable of estimating the residual service life of the flight control computer system on line.

The invention relates to a feedback fuzzy inference-based unmanned plane real-time route planning method. The method comprises the following steps of: 1, updating a mode of the unmanned plane route; 2, establishing a fuzzy rule base; 3, acquiring a fuzzy antecedent; 4, designing a feedback fuzzy inference method; 5, defuzzifying; 6, determining other parameters; 7, determining the next position of the unmanned plane; and 8, completing the design. Compared with the conventional unmanned plane real-time route planning method, the method ensures that the unmanned plane has primary independent studying capability by applying the feedback fuzzy inference method so as to improve the route planning effect. The real-time route planning method designed according to the method has the advantages of short route, smooth path, global astringency and easy implementation, and is a more practical and reliable unmanned plane real-time route planning method for engineering application.

The embodiment of the invention discloses an air-conditioner self-adaptation control method which is used for solving the problems of non-uniform temperature distribution of indoor areas and large temperature differences between some areas and a temperature set by a user, which are easily caused in a comfortable temperature control stage of a conventional air conditioner. The method provided by the embodiment of the invention comprises the following steps: calculating the deviation value between an actual temperature value and a set temperature value; converting the deviation value into a corresponding fuzzy quantity according to a preset fuzzy rule; judging whether the fuzzy quantity is less than the deviation value, if no, performing PID fuzzy control which comprises the following steps: calculating a deviation change rate corresponding to the deviation value according to the actual temperature value and the set temperature value; acquiring a calibration frequency through a PID control algorithm and the preset fuzzy rule according to the deviation value and the deviation change rate; and adjusting the working frequency of a compressor into the calibration frequency. The embodiment of the invention further provides an air-conditioner self-adaptation control system.

The invention relates to a self-learning mechanism-base fast matching fuzzy reasoning method. The method includes the following steps that: a Gaussian membership degree function method is adopted to construct parameter fuzzification information; a fuzzy rule base is established; external parameters are fuzzificated, so that a fact item can be obtained; the fact item is matched with rules in the fuzzy rule base by adopting a rete algorithm, so that a fuzzy reasoning result can be obtained; the fuzzy reasoning result is subjected to defuzzification, so that a final reasoning result can be obtained; and a sample set is constructed according to the final reasoning result and an actual feedback result, and rule strength self-learning correction is carried out based on the sample set. According to the self-learning mechanism-base fast matching fuzzy reasoning method of the invention, the rete algorithm is adopted, so that the efficiency of fuzzy reasoning can be improved, and the fuzzy reasoning method can be applied to the engineering field with high real-time requirements.

A method for diagnosing faults in a particular device within a fleet of devices is provided. The method comprises receiving performance data related to one or more parameters associated with a fleet of devices and processing the performance data to detect one or more trend shifts in the one or more parameters. The method then comprises detrending the one or more parameters to derive noise-adjusted performance data related to a particular parameter associated with a particular device. The method further comprises generating a fleet-based diagnostic model based on trend patterns and data characteristics associated with the fleet of devices. The fleet-based diagnostic model comprises one or more fuzzy rules defining one or more expected trend shift data ranges for the one or more parameters associated with the fleet of devices. The method then comprises computing one or more scaling factors for the particular parameter associated with the particular device and scaling the one or more of fuzzy rules defined for the one or more parameters in the fleet-based diagnostic model, based on the one or more scaling factors, to generate a personalized diagnostic model for the particular parameter associated with the particular device. The method finally comprises evaluating the personalized diagnostic model against the one or more trend shifts detected for the one or more parameters, to diagnose a fault associated with the particular device.

The present invented method consists of 1, establishing expert knowledge base and blur rule base; 2, based on said knowledge base and blur rule base, utilizing fuzzy reasoning technology establishing fault modification fuzzy model in injection process; 3, according to fed die setting process technological parameter, jet molding piece fault type and fault level, reasoning and calculating to obtain technological parameter correction. The present invention also obtains a jet molding unit. Said invention has advantages of quick adjusting technological parameter, fast modifying jet molding piece fault, and raising jet molding piece quality.

A motor vehicle comprises an HVAC system including a climate control circuit coupled to onboard sensors, a human-machine interface, and climate actuators. The actuators are responsive to respective command parameters generated by the control circuit in response to the sensors and the human-machine interface. A wireless communication system transmits vehicle HVAC data to and receives crowd data from a remote server. The control circuit initiates a request for crowd data via the communication system to the remote server, wherein the request includes peer parameters for identifying a vehicle environment. The control circuit receives a response via the communication system from the remote server. The response comprises crowd data and at least one weight indicating a confidence level associated with the crowd data. The control circuit generates at least one command parameter using a set of fuzzy rules responsive to the crowd data and the weight from the response.

The invention discloses an electric car driving range evaluation method based on working condition identification and fuzzy energy consumption. The electric car driving range evaluation method is established based on working condition identification and fuzzy energy consumption and includes the steps of dividing working conditions based on fuzzy C mean value cluster algorithm to obtain parameters for identifying working conditions, establishing a fuzzy rule base between characteristic parameters and energy consumption, optimizing driving mileage L with kilometer as the unit, and estimating residual driving range Srest. The method combines working condition identification and energy consumption modeling, obtains related information of an electric car on a real-time basis, and is rarely affected by external factors such as ambient temperature and vehicle driving states. Energy consumption is further divided in to a certain level, so that real energy consumption under certain speed can be reflected.