573 results about "Fuzzy reasoning" patented technology

The invention relates to a method for evaluating traffic jam, congestion and smooth state of urban roads by using floating vehicle data. Concretely, the method comprises the following steps: analyzing the transmission frequency and the traveling tracks of data based on the prior GPS data by using network state estimation technique based on GPS data of floating vehicles in detail; selecting a comprehensive evaluation value of the calculation result of a time shortest path algorithm as the traveling speed of the road in consideration of the OD characteristics of the urban traffic; and comprehensively evaluating the traffic state of the road by using fuzzy reasoning technique based on the combination of the traveling speed and the use function of the road. The improved evaluation method has the advantages of easily satisfied operation condition, easy implementation, and accurate result.

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.

A technique for finding an optimal threshold for binarization of a gray scale image employs fuzzy reasoning. A triangular membership function is employed which is dependent on the degree to which the pixels in the image belong to either the foreground class or the background class. Use of a simplified linear fuzzy entropy factor function facilitates short execution times and use of membership values between 0.0 and 1.0 for improved accuracy. To improve accuracy further, the membership function employs lower and upper bound gray level limits that can vary from image to image and are selected to be equal to the minimum and the maximum gray levels, respectively, that are present in the image to be converted. To identify the optimal binarization threshold, an iterative process is employed in which different possible thresholds are tested and the one providing the minimum fuzzy entropy measure is selected.

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 discloses a target identification method based on the data fusion of an airborne radar and an infrared imaging sensor, comprising the following steps: firstly, identifying a target and generating a corresponding elementary probability partition function M1 by the infrared imaging sensor; then identifying the target and generating a corresponding elementary probability partition function M2 by the airborne radar; fusing the identification results of the airborne radar and the infrared imaging sensor by a D-S evidence theory; finally, identifying the target, and making decisions. In the invention, the airborne radar adopts the target identification method based on fuzzy reasoning to identify the target, thereby overcoming the defect of the high nondeterminacy of data measured by a radar sensor and finally realizing the data fusion of the airborne radar and the infrared imaging sensor. A simulation result shows that the identification effect of an intelligent target algorithm based on the airborne radar and the infrared imaging sensor is much better than that of single radar or single infrared sensor.

The invention relates to an intelligent irrigation fertilization decision-making control system, comprising an input unit, an output unit, a data acquisition unit and an irrigation control unit, wherein, a decision-making analysis unit arranged in a decision control database receives the data from the input unit and exchanges data with a privilege unit after processing the received data, and then exchanges data with a fuzzy reasoning unit which also receives output data of a data management unit; after performing logical judgment and processing according to the received data, the fuzzy reasoning unit outputs the data to a control management unit; a sensor of the data acquisition unit acquires and transfers the soil humidity data to the data management unit; a controller of the irrigation control unit receives the data from the control management unit, and transfers the data to a switching quantity output module after processing, and the switching quantity output module transfers the data to a solenoid valve. The intelligent irrigation fertilization decision-making control system has the advantages that: crop physiological water demand indicators are introduced into the computer automatic control field, the irrigation fertilization decision-making is carried out through an irrigation indicator database and the fuzzy classification technology.

The invention relates to an electrical fire warning system based on data fusion. According to the scheme, the electrical fire warning system comprises an information layer, a feature layer and a decision-making layer, and further comprises an electric arc detection device; when the electric arc detection device detects electric arc signals, the detected electric arc signals pass through a signal preprocessing device and a signal transmission device and then are transmitted to an early warning system to achieve system early warning, meanwhile, the feature layer is started to conduct fusion processing on fire feature signals collected by sensors, and when no electric arc is generated, the feature layer collects data of the information collecting layer in real time to carry out monitoring on electrical fires. In the electrical fire warning system, the phenomenon that electric arcs are generated before fire signals are generated in electrical fires is utilized, the early warning function of the system is achieved by detecting the electric arc signals, fuzzy logic is introduced while the neural network algorithm is adopted, the defect that the neural network is not easy to understand can be compensated for a large degree, accurate fitting can be carried out on existing fire data through the neural network, and fuzzy reasoning can be carried out through the fuzzy logic through a small amount of known fire data.

The invention relates to an Internet public opinion crisis early-warning method. The method comprises the following steps: getting a non-thematic word library and trend mining rules; extracting Internet public opinion hotspot words; detecting Internet public opinion hotspot topics; calculating similarity of the hotspot topics and automatically tracking; performing trend mining on the hotspot topics; and performing automatic early-warning on an Internet public opinion crisis. The method is divided into two parts, namely an off-line technology and an on-line technology. The off-line technology adopts a statistical analysis and machine learning method for getting the non-thematic word library and a trend mining rule library. The on-line part comprises the following steps: firstly adopting a two-stage filtration method for fast extracting the public opinion hotspot words; then adopting a word clustering method based on co-word analysis for getting the public opinion hotspot topics; further calculating the similarity of the hotspot topics in a continuous time period, quantifying changes of the hotspot topics as time goes on, and realizing the automatic tracking of the hotspot topics; and finally adopting a fuzzy reasoning technology to mine Internet public opinion trend knowledge and realizing the automatic early-warning of the public opinion crisis. By adopting the method, a public opinion worker can timely make a treatment decision of the public opinion crisis.

The invention relates to a heating control method for electrical cooking equipment. The heating control method includes: continuously heating food, stopping heating til the food reaches to a first preset temperature; judging whether the heating stopping time reaches a first preset time or not; if yes, acquiring capacity of the food according to a current temperature value of the food and a preset capacity judging temperature value; heating the food and retiming after the capacity of the food is acquired, and in a preset periodic time, judging whether the food is boiling or not according to the current temperature value of the food, a periodic bottom temperature and a preset boiling judging temperature value. The electrical cooking equipment is provided with a fuzzy reasoning functional program capable of accurately controlling heating thermal power.

The invention provides a video size switching system and a video size switching method facing a display terminal. The method includes the steps: firstly, computing the size of interested windows according to interactive choices of users and by designing perception window models; then detecting long shots, fusing various factors of human eyes to view videos into each frame of image in each long shot by adopting a knowledge-based fuzzy reasoning decision-making system, automatically identifying interested areas and cutting the interested areas to serve as display content of the display terminal; and finally, performing time-domain smoothing for central points of all the interested areas identified in the long shots, so that consistency and continuity of time domains of the interested areas are guaranteed. Additionally, the video size switching system facing the display terminal is based on the method. From the perspective of video source format switching technology, the method with the technical scheme can be self-adaptive to switch a common video format into a target video format which can be played by a small-sized terminal.

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

The invention provides a synchronization control method of stern hydraulic work platform lifting for shipping manufacturing technique field, which includes steps: establishing close loop control of hydraulic jacking oil cylinder lifting by rope stretching type displacement sensor and proportion servo valve, constructing hydraulic multi-cylinder close loop synchronization control technique based on Master/Slave control strategy, carrying zero drift control of hydraulic multi-cylinder synchronization lifting system, and realizing self-adapting on-line self-setting of PID parameter based on fuzzy reasoning. The invention realizes high precision synchronization for the hydraulic multi-cylinder of the stern hydraulic work platform, synchronization precision of the hydraulic multi-cylinder is controlled in +-3 mm, character of dynamic control is high, robustness is strong, synchronization lifting of the platform is basically not influenced by environment, production cycle of fixing propeller and rudder blade of shipping can be shortened from one week to one day by using the method, and safety of operation is greatly increased.

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.

The invention discloses a brake method capable of distinguishing the brake intention of a driver, comprising: an electric control unit receives information input by a brake pedal position sensor, an accelerator pedal position sensor and the like, then fuzzy reasoning algorithm is used for distinguishing the brake intention of the driver, finally, a corresponding brake intention control signal can be generated; the brake intention control signal can be used for controlling the action of an EHB executing mechanism, thus fully exerting the advantages of the EHB system, accurately reflecting the intention of the driver in time in case of emergency, reducing brake reaction time, shortening braking length and optimizing the stability of vehicle.

The invention discloses a dual-mode switch based self-adaptive cruise control method for an electric car. The method comprises steps as follows: establishing a stable car following mode; establishing a fast approaching mode; establishing a dual-mode switch rule for control moment distribution on the basis of fuzzy reasoning. According to the method, driving demands of a driver are summarized through analysis of actually measured microscopic driving NGSIM data collected by Federal Highway Administration combined with microscopic traffic simulation research scholars; the control rule of the fast approaching mode and the stable car following mode is constructed under a model prediction control frame, and a switch rule between the modes is made according to the fuzzy reasoning. A control mode of a self-adaptive cruise control system is closer to driving characteristics of a real driver and is more adaptive to complicated road conditions. The dual-mode switch based self-adaptive cruise control method for the electric car can really reflect demands for the stable car following mode, the fast approaching mode and the like of the driver in a normal driving process, and can guarantee safety, car following performance, comfort and economic efficiency of the car in a traveling process.

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.

The invention discloses an NAR (Nonlinear Autoregressive Models) neural network vehicle speed prediction method based on driving intention recognition. The method comprises the following steps of driving intention classification and recognition parameter selection; fuzzy reasoning recognition of the driving intention; NAR neural network off-line training; and NAR neural network on-line vehicle speed prediction: firstly performing driving intention recognition, and then inputting the driving intention obtained through recognition and the vehicle speed time sequence into an NAR neural network together so as to realize the vehicle speed prediction of the vehicle in a period of time in future. The NAR neural network vehicle speed prediction method has the advantages that the NAR neural network is used for performing vehicle speed prediction; the neural network input includes the network output feedback; the method is suitable to be used for solving the nonlinear problem on the time sequence; and the multi-step prediction precision can be obviously improved. The driving intention time sequence and the vehicle speed are introduced to be jointly used as the input; the fuzzy reasoning is used for analyzing the pedaling operation of a driver; the expectation of the driver on the future change trends of the vehicle speed is sufficiently shown; and the vehicle speed prediction accuracy is improved.

The invention discloses a device and method for suppressing subsynchronous oscillation of a power system. The method comprises the following steps of: firstly, filtering the rotation speed signal of a generator to obtain the subsynchronous rotation speed signal of each mode; processing the subsynchronous rotation speed signal of each mode respectively to obtain a change rate; then, generating an additional control signal through a Sugeno type fuzzy reasoning system; and finally, performing amplification, overlapping and amplitude limiting on the obtained additional control signal, and generating an exciting voltage additional control signal so as to change the exciting current, generate a subsynchronous frequency damping torque and suppress the subsynchronous oscillation. In the method provided by the invention, a training sample of a fuzzy controller is established according to the phase compensation principle, and the parameters of the fuzzy system are optimized and trained by use of a learning algorithm of an error backpropagation neural network. The method solves the problem that the expert experience is difficult to obtain by the fuzzy controller, and the additional exciting damping controller can effectively suppress the subsynchronous oscillation of the power system.

Intelligent technique is an effective method to perform the network resource management. A three layer cascade adaptive neural fuzzy inference system (ANFIS) based intelligent controller is proposed for the mobile wireless network to optimize the maximum average throughput, minimum transmit power and interference for multimedia call services. The proposed intelligent controller is designed with a three layer cascade architecture, which mainly contains an ANFIS rate controller (ARC) in the first layer, an ANFIS power controller (APC) in the second layer and an ANFIS interference controller (AIC) in the third layer. The design aim of the proposed three layer cascade ANFIS cognitive engine is maximizing the average throughput of the mobile wireless network, while minimizing the transmit power and interference power.

The invention discloses a method for controlling the frequency modulation of a micro-grid battery energy storage system based on fuzzy control, and belongs to the technical field of electric power system micro-grids. On the basis of the conventional proportion integration differentiation (PID) control, fuzzy control and an implementation mode thereof are introduced into the method, and the method comprises important components such as fuzzification, fuzzy rules, fuzzy reasoning, defuzzification, PID control and the like. According to the method, the micro-grid frequency deviation and the micro-grid frequency change rate are fuzzified into input of a fuzzy controller, PID control parameters of active power output control are output according to a fuzzy control rule, and an active power output reference value Pref of the battery energy storage system is finally output to control the active power output of the micro-grid battery energy storage system. Compared with the conventional PID control, the method has very strong adaptability to the switching of micro-grid parallel network/isolated network operational modes and to the nonlinearity and the time variability of electric network operational parameters, has a good dynamic response characteristic, and effectively improves the active power control accuracy of the battery energy storage system and the frequency stability of the micro-grid.

The invention relates to a method for the fuzzy control of the temperature distribution of steel billet in a heating furnace. The method utilizes a compensation control component fuzzy reasoning module and a compensation control weighted integration component module to form a diffusion reasoning fuzzy controller of the temperature of the steel billet; the temperature deviation distribution between the temperature distribution of the steel billet and the perfect heating curve of the steel billet is used as input information of the fuzzy controller; for each adjusting point of the furnace temperature, one set of two-dimensional fuzzy controllers of the compensation control component fuzzy reasoning module is utilized to generate a set of furnace temperature compensation control components; and the compensation control weighted integration component module is utilized to integrate the set of the furnace temperature compensation control components so as to obtain the compensation dosage of the furnace temperature of each part. Through the dispersive fuzzy reasoning structure, the method solves the difficult problems that the prior fuzzy dynamic compensation control technology is difficult to effectively process high dimensional input information, can fully consider the temperature deviation distribution and the changing trend of the steel billet after the adjusting point of the furnace temperature in the process of adjusting the furnace temperature and better ensure the steel billet to be heated completes heating according to the perfect heating curve.

The invention relates to the field of pattern recognition, and discloses an unbalanced data classification method based on intuitive fuzzy integration and a system based on the method. The method comprises the following steps of: a) cleaning original data, and classifying original point-of-sale (POS) class samples according to intra-class positions to generate POS class artificial samples; b) training a base classifier by using different sample sets of inter-class approximate balance; c) converting the classification output equal utility of the base classifier into an intuitive fuzzy matrix; and d) integrating samples to be classified into the membership and the non-membership of the POS class and the negative (NEG) class by combining the weight of the base classifier, and making a classification decision. The invention has the advantages that: over learning is avoided by integrating over sampling and under sampling; the training samples of the base classifier are different, so that the difference of the base classifier is ensured; the base classifier is not specifically limited, so the method has good expandability; the intuitive fuzzy reasoning method quantitatively describes the uncertainty in classification so as to improve the performance of integrated learning; therefore, the system based on the method can better support the medical diagnosis decision and the like.

The invention discloses an assisted power controlling method for an electric power assisted steering system. The method includes the steps that a microprogrammed control unit (MCU) collects a torque signal and a vehicle speed signal of a steering wheel to calculate to obtain power assisted motor target electric current Im of the power assisted steering system and meanwhile, collects current actual electric current Ireal of a power assisted motor; stretch factors alpha (e) and alpha (ec) of an input domain and stretch factors Beta (e, ec) of an output domain are obtained by the aid of control thoughts of a varying domain and a functional expression; a fuzzy control unit is combined with stretch factors of the varying domain to obtain control parameter modification values delta Kp and delta Ki through fuzzy reasoning; the calculation is performed to obtain an incremental value of a duty ratio of pulse-width modulation (PWM) by an incremental PI algorithm, and the motor is controlled in a PWM driving mode to output power assisted torque. By means of the assisted power controlling method for the electric power assisted steering system, defects such as nonlinearity and multiple coupling of the electric power assisted steering system can be effectively overcome by the established fuzzy control strategy, the stretch factors can adjust the domains in an on-line mode, and adaptive ability and control accuracy of the system are improved.

The invention discloses a self-adaptive control method for diagonal gait of a four-footed robot, which is used for self-adaptively controlling the walking of the four-footed robot in the nonstructural terrain with the diagonal gait. The self-adaptive control method bases on a fuzzy reasoning learning method and a foot trajectory real-time correction method and takes the diagonal gait as the motion mode so as to comprehensively control a robot body to adapt for the nonstructural environment. The gait planning is carried out on the environmental information collected by the sensor through the fuzzy reasoning learning method; the stability judgment is carried out on the gait planning information; if the planned four-footed robot has stable diagonal gait, the joint corner information is sent to a control system; if not, the supporting time of the diagonal feet is corrected by the foot trajectory real-time correction method so that the four-footed robot has better stability when walking under the changed gait in the nonstructural terrain. The self-adaptive control method is used for correcting foot trajectory planning and walking stability under changed gait when the four-footed robot walks diagonally in a dynamic way, thus realizing the self-perception, self-correction and self-adjustment of the four-footed robot.

The invention discloses a method and a device of car emission fault detection and diagnosis based on fuzzy reasoning and self-learning and belongs to the field of car maintenance and diagnosis. The method of the car emission fault detection and diagnosis based on the fuzzy reasoning and the self-learning comprises collecting all data values of a to-be-tested car exhaust gas under different working conditions, comparing the data values with standard values, if the data values reach the standard values, then the diagnosis is finished, if the data values do not reach the standard values, then confirming the possible corresponding causes of the problem according to a built fuzzy diagnosis matrix through a fuzzy reasoning method and outputting a diagnosis result, implementing maintenance according to the diagnosis result until the data value reaches the standard value through staff, and at last correspondingly updating vehicle profile information and the right fault diagnosis information of the diagnosis result to a data base to be used for self-learning and adjusting the fuzzy diagnosis matrix. According to the method and the device of the car emission fault detection and diagnosis based on the fuzzy reasoning and the self-learning, a car emission fault example is studied through the fuzzy reasoning, fault features are extracted from a large amount of samples, detection accuracy is high, dependency of the technical merit of the operators is low, and operation is convenient.