443 results about "Fuzzy inference" patented technology

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.

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 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.

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.

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.

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 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.