7 results about "Fuzzy process" patented technology

The application provides a kind of intelligent textile printing and dyeing process optimization method and system, according to the parameter of membership function determined according to the characteristics of current batch material, fuzzy process characteristics are obtained by fuzzy processing of real-time process parameters using membership function;According to the correlation degree of the historical prediction accuracy of fuzzy rule and process benefit, the activation contribution of each fuzzy rule is calculated, and then the process coordination state is obtained;Sensor time series data and process coordination state are input into multi-modal time series main capsule layer to obtain main capsule output vector, calculate the confidence of the vector, and adjust the influence of the vector when it is routed to the upper capsule layer subsequently according to the confidence, the upper capsule layer generates a set of control parameters for the subsequent process;Obtain the quality detection data of subsequent process, obtain multi-objective deviation vector according to quality detection data and control parameter set, and use multi-objective deviation vector to optimize the upper capsule layer, to obtain the optimized control parameter set.

The application provides a vehicle fault diagnosis method and device based on multi-source data fusion and a storage medium. The method comprises: collecting real-time state data and real-time alarm data of a target part, obtaining external environment data and map navigation data; after data preprocessing, the multi-source data set target features are obtained by fusion, the target features extracted from the multi-source data set are input into the adaptive neural fuzzy inference system, the target features are mapped to multiple fuzzy sets, and the member function is used for fuzzification; the rule weight of each target feature is calculated to reflect the fuzzy strength; the neural network learning algorithm is used to train the inference system, the member function and the rule weight are adjusted, the fuzzy output is converted into a specific numerical value or a fault level in the defuzzification process by using the output member function, so that the health state of the part is judged. The application provides a fault diagnosis output with high interpretability, improves the accuracy, and can perform fault diagnosis in a wide range of vehicle operating environments.

The application relates to a target detection pre-tracking method and system based on fuzzy control optimized particle filtering, and belongs to the field of target tracking. Particle states are predicted, and particle weights are calculated, the particle with the largest weight is regarded as an optimal particle, and the space near the optimal particle is regarded as an optimal region; the distance between the particle and the optimal particle and the particle ratio in the optimal region are calculated and taken as inputs of a fuzzy controller, a moving coefficient is output after a process of fuzzification, fuzzy reasoning and defuzzification; the particle state is updated according to the moving coefficient, and the particle with a lower weight is moved to a high-likelihood region; the iteration is performed for multiple times until the number of particles in the optimal region exceeds a threshold value; and the state of the target is estimated according to the particle state. The application can effectively alleviate the particle impoverishment problem, improve tracking accuracy, control particle movement through the fuzzy controller, be adaptively applied to different environments and scenes, and has good robustness and interpretability, and the rules can be expressed in natural language.

The invention provides an intelligent control method and system for a semi-autogenous grinding-ball milling grading loop. The method comprises the steps that input variables and output variables of the intelligent control system for semi-autogenous grinding-ball milling grading are obtained; on the basis of data in a pre-constructed historical data sample library and a typical working condition sample library of the semi-autogenous grinding-ball milling grading process system, fuzzification processing is conducted on the input variables and the output variables, and the subordination relation between the basic discourse domains of the input variables and the output variables and the corresponding states is obtained; determining the current related working condition based on the basic universe and membership of the input variable and the output variable; determining a target control rule in a plurality of control rules in a pre-constructed core control logic algorithm library in combination with expert knowledge and the working condition; and controlling an execution mechanism in the semi-autogenous grinding-ball milling grading loop to operate based on the target control rule.

The application provides a kind of detection method, system, storage medium and computer of vehicle plastic parts, the method comprises: real-time acquisition vehicle plastic parts is detected image, and corresponding separation threshold is constructed, and according to separation threshold, feature separation is carried out to the image to be detected to obtain feature image;Increase regression prediction in convolution neural network model to obtain deep learning model;Get the labeled image of detection completed, and the labeled image is fuzzy processed, the obtained fuzzy image and labeled image are calculated, the obtained fuzzy degree is input into deep learning model for optimization, to obtain deep learning optimization model;Feature image is input into deep learning optimization model for image processing, and the detection result of image to be detected is generated according to image processing result.The application optimizes deep learning model using fuzzy degree, and uses the constructed deep learning model to carry out image processing to feature image, to generate corresponding detection result.

The present application relates to a multi-dimensional value evaluation method and system for scientific and technological information viewpoints based on fuzzy cumulative prospect theory, the method comprising the following steps: S1: constructing a scientific and technological information viewpoint value evaluation index system and calculating the accurate scores of several dimensions; S2: triangular fuzzy processing the accurate scores of several dimensions and calculating the comprehensive prospect value based on cumulative prospect theory; S3: linear normalization of the comprehensive prospect value of several dimensions to obtain the standardized score; S4: generating the star rating of the scientific and technological information and the corresponding decision suggestion. The system comprises an index calculation module, a fuzzy processing and prospect value calculation module, a linear normalization module and a grade evaluation module. The present application can realize comprehensive, objective and intelligent evaluation of the value of scientific and technological information viewpoints, and provides a scientific, intelligent, practical and interpretable value evaluation solution for the field of scientific and technological information analysis and decision support.

The invention provides a method and a system for controlling the bonding strength of a grid interlayer and paper. The method comprises the following steps: acquiring input parameters such as environment temperature and humidity, paper porosity, adhesive viscosity and production line speed; fuzzy processing is carried out on each parameter, a discourse domain is divided into non-equidistant fuzzy level subsets, the division density of a target bonding strength sensitive interval is improved, an asymmetric membership function is constructed, a Gaussian function is adopted in an ascending section, and adjustment is realized through historical data weighted smooth fitting according to a confidence index of a previous control period in a descending section; performing fuzzy reasoning on the basis of a preset fuzzy rule base, adjusting the weight of each rule, and generating a fuzzy output quantity in combination with the weight and a rule of rule triggering intensity aggregation activation; and performing defuzzification and correction on the fuzzy output quantity to generate a final control quantity, and updating the fuzzy rule weight with the maximum contribution by using a difference value.