452 results about "Fuzzy inference" patented technology

A system and a method provide a photovoltaic system which regenerates the output characteristics of the photovoltaic at different ambient condition with high precision under all environmental conditions. The photovoltaic system includes a photovoltaic array, a buck / boost converter, a DC link capacitor to connect the buck / booster converter to a load / inverter, an adaptive network-based fuzzy inference maximum power point tracking controller, a voltage control loop, a proportional integral controller to maintain the output voltage of the photovoltaic array to the reference voltage by adjusting the duty ratio of buck / boost converter.

The invention provides an optimization device for a unitary apparatus that can obtain optimum characteristics as a combined apparatus, without losing user's selectivity and unitary apparatus's versatility. The optimization device includes an optimization process section that optimizes dynamic characteristics of the unitary apparatus using genetic algorithms and fuzzy inference, with functional characteristics of a combined apparatus as evaluation reference.

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.

A system and method for selecting a training data set from a set of multidimensional geophysical input data samples for training a model to predict target data. The input data may be data sets produced by a pulsed neutron logging tool at multiple depth points in a cases well. Target data may be responses of an open hole logging tool. The input data is divided into clusters. Actual target data from the training well is linked to the clusters. The linked clusters are analyzed for variance, etc. and fuzzy inference is used to select a portion of each cluster to include in a training set. The reduced set is used to train a model, such as an artificial neural network. The trained model may then be used to produce synthetic open hole logs in response to inputs of cased hole log data.

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.

An audio communication device comprises an input connectable to a narrowband audio signal source. The input can receive a narrowband audio signal having a first bandwidth. An extraction unit is connected to the input and arranged to extract a plurality of narrowband parameters from the narrowband audio signal. An extrapolation unit is connected to receive the plurality of narrowband parameters and arranged to generate a plurality of wideband parameters from the plurality of narrowband parameters. The extrapolation unit comprises one or more adaptive neuro-fuzzy inference system modules. The device further comprises a synthesis unit connected to receive the plurality of wideband parameters and arranged to generate, using the wideband parameters, a synthesized wideband audio signal having a second bandwidth wider than the first bandwidth. And the device comprises an output connectable to an acoustic transducer arranged to output for humans perceptible acoustic signals, for providing said synthesized wideband audio signal to the acoustic transducer.

The invention discloses a breakdown intelligent classification and positioning method of an electric transmission line. The technical scheme of the breakdown intelligent classification and positioning method of the electric transmission line breakdown is that the advantages of three kinds of technologies of a support vector machine (SVM), self-adaptation nerve fuzzy inferences (SVM) and radial based function (RBF) neural networks are concentrated. The breakdown classifiers and positioners of the SVM, the SVM and the RBF neural networks are designed. Positioning errors, classification accuracy and model operation time are used as evaluation indicators. According to the standard that accuracy is preferred and efficiency is taken into account, intelligent selection of an optimal classifier and an optimal positioner is achieved under different breakdown conditions, and optimal breakdown classification and positioning effect is achieved. Meanwhile breakdown serious extent and repair indicators are designed to evaluate breakdown injury extent and breakdown repair difficulty. The breakdown intelligent classification and positioning method of the electric transmission line effectively improves power supply reliability, reduces outage cost, and meanwhile greatly reduces workload of maintenance personnel and improves working efficiency.

A fuzzy logic control arrangement is provided for an impedance match network of the type that is typically employed between a source of RF power at a given impedance, e.g., 50 ohms, and a non-linear load whose impedance can vary in magnitude and phase, e.g., an RF plasma. The fuzzy logic controller fuzzifies the phase and the magnitude error signals. The error signals are applied to a fuzzy logic interference function based on a number of fuzzy sets. The values of the error signals enjoy some degree of membership in one or more fuzzy sets. Fuzzy logic rules are applied to the phase and magnitude error signals. In a defuzzification stage, drive signal values are obtained for moving the tuning elements of the variable impedances. The drive signal values are weighted according to respective fuzzy inference functions for which the error signals enjoy membership. Then the weighted drive signal values are combined to produce output drive signals.

The present invention relates to a method and an apparatus of operating an obstacle avoidance system with camera vision. The invention is used during both day and night, and provides a strategy of obstacle avoidance without complicated fuzzy inference for safe driving. The method includes the following steps: analyzing plural images of an obstacle, positioning an image sensor, providing an obstacle recognizing flow, obtaining an absolute velocity of a system carrier, obtaining a relative velocity and a relative distance of the system carrier with respect to the obstacle, and providing a strategy of obstacle avoidance.

The invention provides a neural-fuzzy PID control method of a four-rotor aircraft based on repetitive control compensation. The method comprises the following steps that S10) a dynamic model of a four-rotor unmanned aerial vehicle (UAV) is established; S20) neural-fuzzy PID control based on repetitive compensation is carried out; S21) a grid structure in which a neural network generates fuzzy inference (rules) and a PID parameter can be adjusted by itself is designed; and S22) repetitive compensation control is carried out. According to the method provided by the invention, repetitive control based on an internal model principle is embedded into self-adjusting PID closed-loop control in which fuzzy inference is generated on the basis of neural network, neural-fuzzy PID control based on repetitive compensation is formed, the system is still in the closed-loop state, neural-fuzzy PID carries out real-time control adjustment on an output error, a repetitive compensation controller carries out adjustment when the system is in the stable state, output signals can effectively track input signals in the stable state, neural-fuzzy PID adjusts the input signals when interference is relatively high, the signal error is reduced, and the tracking precision of the aircraft system is improved.

The invention discloses a neural network and fuzzy control fused electrical fire intelligent alarm method. The method comprises the following steps of: 1, acquiring a leakage current signal, current and voltage signals, an arc light signal, a temperature signal and a field electromagnetic environment parameter signal by using a sensor on site, and pre-processing signals acquired by the sensor by using a velocity detection algorithm; 2, transmitting processed data to a three-layer feedforward error counterpropagation neural network and processing, wherein the neural network is subjected to supervised learning and establishes a weight matrix in advance; and 3, transmitting electrical circuit undamage probability, electrical circuit damage probability, and electrical circuit fire probability output by the neural network to a fuzzy inference module and performing fuzzy inference to acquire a forecast result of electrical fire. In the method, the probability of the electrical fire is accurately forecast by using the advantages of advanced theories, such as neural network, fuzzy control and the like, and without depending on deep knowledge of an object, the electrical fire forecasting accuracy is obviously improved and the damage of the electrical fire can be effectively prevented and reduced.

A method for determining traffic conditions comprises: collecting real-time traffic information of a road; calculating a representative speed value for the road by a fuzzy inference, from the collected real-time traffic information and traffic condition factors having an effect on the road; and transmitting the representative speed value to a terminal of a vehicle traveling on the road corresponding to the representative speed value. The representative speed is sensibly determined and reliable information is supplied because the fuzzy inference's performance is based on the information collected from the signal transmitter and based on the factors which can affect the traffic conditions.

A multi-classifier information fusion partial discharge diagnostic method comprises the steps of (1) signal acquisition, namely partial discharge signals of electrical equipment are acquired through a sensor, (2) signal preprocessing, (3) discharge feature extraction, namely extraction of pulse features and statistic features, (4) classifier recognition, namely extracted feature parameters are used as input vectors, and a neural network classifier, a fuzzy reasoning classifier and a distance discriminant classifier respectively give out confidence coefficient results of five discharge types of corona discharge, suspension electrode discharge, free particle discharge, air gap discharge and creeping discharge, (5) possibility judgment and (6) eventual confidence coefficient calculation. According to the multi-classifier information fusion partial discharge diagnostic method, the discharge types can be pointed out, the specific confidence coefficients can be given out, so that a result is more accurate and comprehensive. The multi-classifier information fusion partial discharge diagnostic method can be further popularized and applied to the fields of fault diagnosis, mode recognition and the like, and has broad market prospects and application values.

The invention discloses an asymmetric four-motor UAV and a control method thereof. According to the asymmetric four-motor UAV, the connection line between two large motor propellers is perpendicular to that between two small motor propellers, and the connection line between the two small motor propellers is shorter than that of the two large motor propellers; the problem of control system divergence is solved through a Smith prediction algorithm; fuzzy control rules are designed and fuzzy inferences are utilized to achieve the optimized adjustment of PID (proportion-integral-derivative) parameters under different flight working conditions. The asymmetric four-motor UAV has the advantages of large take-off weight and good control robustness. The control method of the asymmetric four-motor UAV solves the problems of inaccurate control model and large system interference and reduces the control coupling degree of the four-rotor UAV, thereby reducing the control difficulty and achieving the smooth control of the four-rotor UAV.

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

The invention relates to a method for intelligently analyzing collusive bidding and bid rigging behaviors. Algorithms such as correlation analysis, fuzzy inference and the like are used, probability of suspected bid rigging is calculated, a set of collusive bidding bidders is obtained via clustering and ordination operation, and auditors can be assisted in determining and analyzing collusive bidding and bid rigging behaviors in bidding processes. Three data analyzing models including a bid winner analyzing model, an accompanying bidding behavior analyzing model and a high risk company analyzing model are established via the method. The models put forward in the method are verified with bidding data of real projects, verification results show that work efficiency of auditors can be improved, and a reliable factual basis can be provided for the auditors to define suspected illegal behaviors in the bidding processes. An accurate, reliable, honest and valid bidding behavior system can be built for a bidding system; the bidding processes are enabled to be just, open and fair. The method has broad application prospects and is of great practical significance.

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 invention provides a large legal data management system based on fuzzy inference. The system comprises a data collection module, a data preprocessing module, a database module, a central server, an information push module and a mobile terminal; the data collection module is used for collecting laws and regulations, cases, monitoring, legal practitioners and social data, storing the same in corresponding databases, and storing the data in the database module after being processed by the data preprocessing module to be trimmed and classified, a fuzzy inference module of a central database carries out large data analysis on the legal provisions and case data and generates accurate case analysis and relevant legal analysis reports, and finally, the information push module pushes the reports to the mobile terminal to provide accurate legal services for users. According to the large legal data management system based on fuzzy inference provided by the invention, internal data relations in related legal laws and regulations systems, case systems, monitoring systems, legal practitioner systems and social systems in law management are mined in depth by the fuzzy mathematics algorithm to provide accurate and efficient quantitative tool support for judicial works.

The invention relates to a wireless sensor network trust evaluating method based on a fuzzy logic, which comprises the following steps that: (1) an evaluating node initializes the direct trust vectors of evaluated nodes; (2) the evaluating node periodically updates the direct trust vectors of the evaluated nodes by using a fuzzy inference method; (3) the evaluating node collects the indirect trust vectors of recommended nodes on the evaluated nodes; (4) by utilizing the direct trust vectors and the indirect trust vectors of the evaluated nodes, the evaluating node calculates the comprehensivetrust vectors of the evaluated nodes; and (5) according to the comprehensive trust vectors of the evaluated nodes, the evaluating node carries out the judgement of the trust classification and the decision of the network collaboration on the evaluated nodes. The invention realizes the trust quantification by adopting the trust value definition based on a fuzzy set theory and using a fuzzy inference algorithm, thereby effectively handling the problem of the subjective fuzziness of the trust and simulating the subjective cognitive process of the trust inference. Compared with the traditional algorithm, the wireless sensor network trust evaluating method has higher sensitivity, accuracy and generality.

An intelligent real-time 360° enterprise performance management system and 6-step methodology, which provides an integrated way to evaluate the performance and effectiveness of specific ‘business components’ within organizations (e.g., internal functions, internal processes, and stakeholder relationships). The system serves as a highly scalable, customizable, and context-aware Unified Enterprise Performance Management Application Platform (UEPMAP) that automates static and dynamic real-time and asynchronous business intelligence gathering from employees, customers, suppliers, business partners and other key stakeholders; analyses structured and unstructured feedback data; identifies strengths and weaknesses for each business component by way of SWOT analysis; develops prioritized action plans, supported by an adaptive neuro-fuzzy inference system (ANFIS) in order to address weaknesses identified within business components.

The invention provides a welding spot defect identifying method which comprises a step of performing characteristic extraction on the acquired welding spot image information and a step of identifying the welding spot defect according to the ways of identifying, fuzzy inference, neutral network according to the characteristics and the like, and specifically comprises the following steps of: 1) obtaining a sample for training an artificial neural network according to the principle of an orthogonal test based on the welding spot shape theory; 2) training the artificial neural network by use of an improved neural network algorithm to obtain a network for predicting the possibility of various defects of the welding spots; and 3) performing image processing on the actual welding spot, extracting the shape quality characteristic as the input of the trained artificial neural network, and performing forward calculation by use of the trained network to realize identification of the welding spot defect. In the invention, as the BP neural network is improved and the genetic algorithm is introduced into the neural network algorithm training, the defects of slow convergence, easy trap in local optimal solution and the like of the neural network are solved, and the network performance is improved to some degree so as to realize defect identification of complicated welding spots.

A method and apparatus for the onsite assessment of sensory data during the drilling of a subterranean well to determine when a drilling tool has reached an optimal drilling terminus. One or more sensory data streams are captured and monitored by a terminus decision module, which identifies the appropriate combinations of sensory readings which might indicate that the drilling tool has reached an optimal drilling terminus. The terminus decision module might be either a coded formula-based software component, or might use a fuzzy inference or “fozzy logic” system in the assessment of sensory data. The need for offsite or offline engineering assistance is removed. In addition to the method, both a stand-alone apparatus as well as an add-on instrumentation kit for a PC otherwise in use at the drilling site are disclosed. The system of the present invention could be used in single or multi-axis drilling applications, and could be integrated with the depth or location control system for the drilling tool to automatically adjust the positioning or attributes of the tool.

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 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.

The invention provides a system and method for intelligent control over an automobile air conditioner according to fuzzy control and relates to the field of control over automobile air conditioners. The system for intelligent control over the automobile air conditioner according to fuzzy control comprises a temperature detecting device used for detecting the temperature in an automobile passenger cabin, a data processing unit used for obtaining the temperature difference in the passenger cabin and the change rate of the temperature difference in the passenger cabin according to the temperature, a fuzzy controller which is used for conducting fuzzification on the temperature difference and the change rate, conducting fuzzy inference according to a control rule list to obtain a fuzzy controlling quantity, obtaining an accurate control quantity after making the fuzzy control quantity clear, and outputting an accurate control quantity signal according to the accurate control quantity and the automobile air conditioner used for changing the temperature of the passenger cabin according to the accurate control quantity signal. By the adoption of the system and method for intelligent control over the automobile air conditioner according to fuzzy control, the temperature of the passenger cabin can be rapidly changed, the temperature of the passenger cabin is kept in the degree that a driver and passengers feel comfortable, the oxygen content and the ventilation condition in the passenger cabin are also considered, the comfortable sensation of the driver and the passengers is improved, and the risk of carbon monoxide poisoning is reduced.

The invention relates to an effect evaluation method of a traffic demand management measures based on identification data of license plates, particularly to an implementation effect evaluation technology for the traffic demand management measures based on the identification data of license plates, which comprises the following steps: analyzing the data mining and effective information extraction from the existing identification data of vehicles in details, extracting traveling routes of vehicles with a shortest route algorithm and fuzzy inference technology in consideration of the basic characteristics of urban transportation, and real-time evaluating the traffic demand management measures in combination with the traffic subzone dynamic partitioning method. By adopting the technology provided by the invention, the effects that the travelling conditions are easily met, the operation method is simple and feasible and the evaluation results are accurate and timely are achieved, and the shortcoming that the evaluation technology in the prior art is difficult in dynamic adjustment of evaluation range and has long data acquisition cycle and poor real-time performance is overcome.

The invention discloses a partial discharge diagnosis method based on data mining. The method comprises the steps of 1) acquiring a partial discharge signal of power equipment; 2) extracting characteristic parameters from the partial discharge signal; 3) implementing interval division in a competitive gathering method according to the extracted characteristic parameters, mining a training database of partial discharge types after interval division via an association rule mining algorithm, obtaining a classification rule according to a set minimal confidence and a set minimal support degree, and according to the classification rule, calculating the subordination degrees of the to-be-classified partial discharge signal to the partial discharge types in a fuzzy inference method; and 4) according to the calculated subordination degrees, determining the possibility that the to-be-classified partial discharge signal represents a certain partial discharge type. The partial discharge diagnosis method disclosed by the invention is high in recognition rate and recognition speed.

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.

The invention provides a physical-information fuzzy inference based smart power grid attack detection method. According to the method, attack behaviors are judged by a physical-information fuzzy inference method by means of analyzing electricity measurement data and network communication flow of a smart power grid and by the aid of a tight coupling characteristic of a physical system and an information system of the smart power grid, and thereby false alarm rate in detection can be decreased remarkably, and smart power grid attack detection effect is improved.

The invention discloses an intelligent greenhouse irrigation control device based on fuzzy inference. The intelligent greenhouse irrigation control device comprises a soil humidity detection module, an evapotranspiration detection module, an intelligent control module based on fuzzy inference and an execution control module, wherein the soil humidity detection module is used for detecting moisture content in soil; the evapotranspiration detection module is used for acquiring greenhouse evapotranspiration according to average temperature of the environment, relative air humidity and illumination intensity; the intelligent control module is used for regarding soil humidity and evapotranspiration as input variables and irrigation quantity of a greenhouse as an output variable, fuzzifying the input variables and the output variable, selecting a triangular membership function, establishing a fuzzy rule, adopting a minimal operation method for fuzzy inference, and converting the inferred fuzzy quantity into accurate quantity for output; and the execution control module is used for outputting the accurate quantity as an irrigation quantity of the greenhouse to an irrigation execution mechanism. The intelligent greenhouse irrigation control device based on fuzzy inference provided by the invention is high in control precision, high in working efficiency, good in applicability and low in irrigation cost.