329 results about "Fuzzy model" patented technology

A system and method for allocating computing resources among a plurality of units in a distributed network. A system is provided that includes: a relational fuzzy modeling system for providing a relational fuzzy model based on historical resource parameter data and historical system parameter data, and for predicting and allocating resource requirements for each unit based on the relational fuzzy model; a normalization system for ensuring that a sum of the predicted resource requirements is no more than a total available amount of actual resources available on the plurality of units; and an extra resource allocation system for allocating extra resources when the sum of the predicted resource requirements is less than the total available amount of resources available on the plurality of units.

The invention relates to a satellite fault diagnosis and fault-tolerant control method based on a T-S fuzzy model and a learning observer. The satellite fault diagnosis and fault-tolerant control method based on the T-S fuzzy model and the learning observer is used for solving the problems that a conventional fault diagnosis method cannot be used for effectively treating the influence caused by space disturbance torque and ensuring the robustness of a fault diagnosis method, and a conventional fault-tolerant control method has a poor fault-tolerant property. The satellite fault diagnosis and fault-tolerant control method based on the T-S fuzzy model and the learning observer comprises the following steps: step 1. establishing a mathematical model of a non-linear satellite attitude control system; step 2. establishing the T-S fuzzy model of the satellite attitude control system by using results obtained in the step 1; step 3. designing a T-S fuzzy learning observer by using the results obtained in the step 2 so as to realize the robust fault detection, isolation and fault reconstruction of a satellite attitude angular velocity estimation and executing mechanism; 4. designing a state feedback fault-tolerant controller by using the results obtained in the step 3 so as to ensure that a closed loop of a satellite attitude control T-S fuzzy system is stable. The satellite fault diagnosis and fault-tolerant control method based on the T-S fuzzy model and learning observer can be applied to an aerospace field.

The invention provides a generalized predicative control method of a high-speed train based on an adaptive network-based fuzzy inference system (ANFIS) model. The method utilizes a data-driven modeling method to build the ANFIS model in a running process of the high-speed train according to acquired high-speed train running data; adopts subtractive clustering to determine rule number and initial parameters of a fuzzy model, and adopts a back-propagation algorithm and a least square method to optimize parameters of the fuzzy model. The predictive tracking control method of electric multiple unit running speed on the basis of the ANFIS model obtains accurate controlled quantity through multistep predication and circular rolling so as to change blindness of adjustment by experience, enables the high-speed train running speed to track a target curve accurately, solves the problem of large lag, achieves on-schedule, safe and effective running of the train, and guarantees safety of passengers. The method is simple, practical, capable of achieving automatic drive control of the high-speed train and suitable for on-line monitoring and automatic control of a running process of the high-speed train.

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 discloses a quadrotor attitude control method based on a T-S fuzzy model. According to the invention, a T-S fuzzy model is derived by a simplified attitude dynamic equation; a fuzzy state feedback controller is designed by using a parallel step-by-step compensation technique; while the deviation between the simplified model and an actual model is compensated, stable control of an attitude angle of a quadcopter is realized. According to the invention, compared with a quadcopter attitude dynamical system having strong nonlinearity and strong coupling performance, a more reasonable simplified model is brought forward; the simplified model derived from small angle assumption carried out only on roll angles is more accurate than the model derived from small angle assumption carried out both on the roll angles and pitch angles simultaneously; the model deviation in designing of a controller can be reduced as much as possible; the whole control structure is relatively simple; the calculating efficiency is high; and the convergence speed is fast.

The invention provides a sparse characteristics based video image de-blurring method, comprising the following steps: a modeling step of establishing a de-fuzzy model of weighted total variation regularization constraint described in the figure wherein / x represents a restored clear image, B a fuzzy kernel, y a generated blur image, [miu] an adjustable parameter, W a diagonal matrix of weights, and Vx includes the first-order differentials in the transverse, longitudinal and diagonal directions; and a solving step where an alternating iterative method is used to obtain the updated pixel weights of the / x and repetitive iterations are performed until the / x is converged. According to the invention, the de-blurring of a blurred image can be realized for a restored clear image. Moreover, the non-convex constraint model is improved through the method of weight assignment, and the convex optimization model is established to make the first-order differential constraint model of the image sparser and the possibility for rapid solution for the model.

The invention discloses a servo system controller with self-adaptive fuzzy friction compensation. The servo system controller is used for improving the output tracking precision and fast response of a motor servo system, and is particularly applicable to a precision motor servo system which requires high precision and fast response. The invention comprises a parameter self-adaptive adjustment module, a fuzzy friction compensator and a robust control module. By adopting a fuzzy model to approximate a friction force model, the online assessment of the friction force value is realized by a self-adaptive adjustment of the fuzzy model parameters, and then a friction compensation is carried out so as to eliminate the adverse effects of the friction force on the output tracking precision and fast response of the servo system; and the adjustment of the fuzzy model parameters adopts a composite self-adaptive law and simultaneously uses the information relative to the system output error and the parameter evaluation error for carrying out the parameter adjustment so as to improve the parameter convergence speed. As the controller can realize fast and accurate friction model evaluation and friction compensation, the output tracking precision and fast response of the servo system can be greatly improved.

The invention discloses a feedback control method of a wind energy converting system based on a fuzzy performance estimator and designs a T-S fuzzy state feedback control system including the fuzzy performance estimator specifically to characteristics of strong nonlinearity and parameter uncertainty of the wind energy converting system, belonging to the feedback control field. Firstly, a nonlinear model and a T-S fuzzy model of the wind energy converting system are provided; secondly, the fuzzy performance estimator of the system is designed based on the T-S fuzzy model of the wind energy converting system; then, a linear controller is designed in each linear local model respectively; and a fuzzy state feedback controller of a global model is constituted by dint of membership function. The method provided by the invention has the advantages of a controller is designed based on the fuzzy performance estimator; the feedback control method ensures that a closed-loop control system has good tracking performance; simultaneously, preferable dynamic performance and stable-state performance can be acquired.

The invention belongs to the technical field of a random fuzzy power flow algorithm for distributed wind power, photovoltaic power generation and other uncertain energy sources connected to a power system, and discloses a power flow algorithm considering the fact that the power of the uncertain energy sources connected to the power system has random fuzzy characteristics. The load of the uncertain energy sources connected to the power system is taken as a random fuzzy variable, and the node load power is randomly simulated in a fuzzy manner; the node load power is embedded to Newton-Raphson power flow calculation to obtain voltage amplitude values and phase angle data of the corresponding nodes of the system; the probability distribution characteristics of the node voltage amplitude values and the phase angles are subjected to extraction and statistics; a probability distribution model suitable for fitting the node voltage amplitude values and the phase angles, and the parameter fuzzy characteristics are analyzed and determined; and the random fuzzy model for the node voltages and phase angles is established. According to the power flow algorithm, the influences on the node voltages of the power distribution network from the uncertainties of the distributed type power supply outputs can be more comprehensively analyzed, so that corresponding guiding evidences can be provided for power generation plan arrangement and dispatching for a large number of distributed wind power, photovoltaic power generation and other uncertain energy sources connected to the power system in the future.

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.

The invention designs a fuzzy model parameter analysis method based on depth learning. The method is characterized by including: step 1, adopting a depth belief network structure for extracting and classifying fuzzy features, namely using a semi-supervised depth belief network to project an input image block to a difference feature space, and then classifying each feature; step 2, recognizing parameters of a fuzzy kernel, namely extracting from the edge of a transform domain to guarantee that the depth belief network structure confirms fuzzy parameters with high accuracy. By the image fuzzy model parameter analysis method based on depth learning, the fact that the depth belief network structure confirms the fuzzy parameters with high accuracy can be guaranteed; according to repeated trial verification in multiple image databases such as Berkeley segmentation, Pascal VOC 2007 and the like, favorable performances superior to those of the existing best fuzzy estimation method are achieved.