42 results about "Fuzzy neural" patented technology

This invention provides a method and system for predicting vehicle speed in commercial vehicles using the E-Power architecture, relating to the field of commercial vehicle speed measurement technology. First, vehicle operating data is collected via hardwired and CAN bus lines. Second, the PCA method is used to extract features from the collected vehicle operating data, obtaining variables highly correlated with vehicle speed, which serve as input to the speed prediction model, improving computational efficiency. Finally, a speed prediction model is established based on a recursive fuzzy neural network and a multi-step prediction strategy, achieving accurate prediction of vehicle speed during operation and improving fuel economy and driving comfort.

This invention discloses a primary frequency regulation optimization control method for new energy systems that considers the State of Energy (SOC) of energy storage. It overcomes the problem that traditional wind power control parameter tuning often relies on empirical formulas and fails to fully consider the dynamic changes in energy storage SOC. This method analyzes the power frequency characteristics of the grid-connected system and constructs a grid-connected frequency control model. Based on the high and low operating ranges of the energy storage SOC, it divides the system into correction coefficients and power allocation weights, calculates the total demand power based on frequency deviation, and allocates it to the wind turbine and energy storage system, establishing an adaptive frequency regulation power allocation strategy. A multi-objective function is constructed with the goals of minimizing frequency deviation, SOC fluctuation, and energy storage losses. An improved fuzzy neural network-deep reinforcement learning algorithm is used to identify operating conditions and iteratively correct control parameters, forming a closed-loop optimization to adapt to multiple operating conditions. This invention improves the frequency response speed and regulation margin of wind power under complex operating conditions while also considering energy storage lifespan, achieving synergistic optimization of frequency regulation performance and energy storage economy.

The application discloses a fuzzy neural network H∞ yaw moment controller based on phase plane analysis and belongs to the technical field of drive-by-wire chassis and vehicle stability control. The application quantitatively describes the real-time stability boundary in the vehicle driving process by the phase plane analysis technology, realizes accurate evaluation of the overall stability state of the system, designs the yaw moment controller by using the H∞ robust control theory, avoids the system chattering problem from the design level, guarantees the robustness of the control system, simultaneously introduces the fuzzy neural network online approximation controller parameter and the nonlinear mapping relationship between the vehicle driving state, realizes the autonomous optimization of the controller parameters under different working conditions, and thus the driving stability and adaptability of the distributed drive vehicle under the complex driving environment are significantly improved.

The application provides a control system of an LCD attaching device, and belongs to the technical field of control of LCD panel manufacturing equipment. The control system comprises the following modules: a vision module, which acquires a substrate pose and constructs a nonlinear target function, and realizes accurate alignment through ant colony algorithm optimization; a pressure acquisition module, which acquires pressure data and constructs a continuous pressure cloud chart; an extraction and calculation module, which analyzes the cloud chart, extracts a characteristic region, and constructs a risk index; a network analysis module, which outputs a speed adjustment amount and a pressure compensation amount through a fuzzy neural network; and a correction and determination module, which realizes micro-control correction of the attaching speed and pressure. The application realizes high-precision pose alignment and pressure acquisition, quantifies the attaching risk, dynamically regulates and controls the attaching parameters, effectively corrects local deformation of the substrate, and improves the LCD attaching yield.

The application discloses a ceramic capacitor microstructure regulation and control method and system based on cross-scale modeling and fuzzy neural network, and belongs to the technical field of ceramic material design and performance optimization. The method comprises the following steps: constructing a cross-scale digital twin model of a target ceramic dielectric system; constructing an initial data set based on sample data; constructing and training a fuzzy neural network reverse design engine based on the cross-scale digital twin model and the initial data set; inputting target macroscopic performance indicators of a ceramic capacitor to be prepared into the fuzzy neural network reverse design engine to obtain a microstructure target and process parameters; and regulating and controlling the microstructure of the ceramic capacitor to be prepared according to the microstructure target and the process parameters. The implementation of the application is beneficial to breaking through the key technical bottleneck of high-performance ceramic capacitors, shortening the exploration period of new material formula from several years to several months or even several weeks, and greatly reducing the research and development cost.

This invention discloses an intelligent control system for integrated energy consumption in tunnels based on fuzzy neural networks and photovoltaic power supply, relating to the field of tunnel traffic engineering technology. It includes a multi-source data acquisition and preprocessing unit, comprising an environmental parameter acquisition module, a traffic flow acquisition module for collecting vehicle flow, speed, vehicle type distribution, and spatiotemporal distribution characteristics; a photovoltaic power generation status acquisition module; a fuzzy neural network decision-making center unit; a collaborative energy supply execution unit; and an equipment health assessment and early warning unit. This intelligent control system for integrated energy consumption in tunnels based on fuzzy neural networks and photovoltaic power supply, through a predictive scheduling model and surplus power allocation mechanism, prioritizes the supply of surplus photovoltaic power to surrounding facilities or connects it to the grid to participate in demand response, significantly optimizing the energy structure and improving photovoltaic utilization. Furthermore, through the interaction mechanism between the congestion prediction module and the energy storage optimization scheduling module, it not only reduces peak-hour grid dependence but also ensures the power supply reliability of critical loads during emergency response phases.

The application discloses a line external damage early warning method and system based on a network fuzzy neural algorithm, and belongs to the technical field of power line safety protection, and comprises the following steps: obtaining an external damage classification model based on knowledge distillation, performing feature coding and weighted fusion on a plurality of groups of collected multi-modal data through the external damage classification model to obtain external damage category confidence, extracting short-time energy of vibration data in the plurality of groups of multi-modal data, obtaining an early warning event according to the short-time energy and the external damage category confidence, fusing the multi-modal data corresponding to the early warning event and the external damage category confidence to obtain a multi-dimensional feature vector, inputting the multi-dimensional feature vector into a network fuzzy neural algorithm to obtain spatial influence weight and time influence weight of risk assessment, and obtaining a risk score according to the spatial influence weight and the time influence weight, and performing graded early warning according to the risk score. The technical problem that the prior art is difficult to improve the accuracy of early warning event determination and the accuracy of early warning event risk score is solved.

The present application relates to the technical field of battery management, and more particularly to an electric vehicle aggregator energy-frequency modulation bidding optimization method and device, electronic equipment and medium, in the method, first, EV historical charging and travel data and user survey data are acquired and preprocessed; second, a flexible index system including adjustable capacity, online time and charging power is constructed to form a cluster chino polyhedral flexible domain constraint; third, a fuzzy neural network is used to evaluate user response willingness, and a dispatchable user set is determined according to a threshold value; in a multi-price scenario, the total expected income is maximized as the target, the charging and discharging bidding power, the upward / downward frequency modulation capacity and the subsidy decision quantity of each period are jointly optimized, and the power, capacity dynamics, frequency modulation margin and cluster flexible domain constraints are satisfied, and the energy and frequency modulation resource competition-complementary overall allocation is realized. It is suitable for demand response resource aggregation transaction, improves the bidding executability and income stability, and reduces the examination punishment risk.

The application provides a linear active disturbance rejection control method and a control system of an energy storage converter, the linear active disturbance rejection control method comprising: calculating instantaneous active power and reactive power according to grid voltage components and converter current components; obtaining dq-axis current reference values after adjusting output by a proportional-integral controller after obtaining active power error signals and reactive power error signals; inputting the dq-axis current reference values and the converter current components into a fuzzy neural network, the fuzzy neural network adjusting bandwidth parameters of a linear active disturbance rejection controller through a process of fuzzification, fuzzy reasoning and defuzzification; performing inverse Park conversion on dq voltage instructions output by the linear active disturbance rejection controller to obtain voltage instructions in an alpha-beta coordinate system, and generating PWM signals for driving power switching tubes of the energy storage converter through a space vector pulse width modulation technology based on the voltage instructions. The application solves the technical problems of poor robustness of a conventional PI control and insufficient work condition adaptability of a fixed parameter linear active disturbance rejection control.

Methods for controlling fluid management for a patient are disclosed herein. An example computer-implemented method comprises: initializing a fuzzy machine learning model based on a fluid management ruleset for a population including the patient; training the fuzzy machine learning model on historical medical data associated with the population to generate a trained fuzzy ruleset, the historical medical data including a plurality of indications of historical fluid management treatments for the population and associated treatment outcomes; receiving clinical data for the patient; generating a fluid management recommendation by applying the trained fuzzy ruleset to the clinical data for the patient; presenting, via a graphical user interface, the fluid management recommendation and one or more indications of corresponding rales included in the trained fuzzy ruleset to a clinician.

This invention proposes an event-driven fuzzy neural network-based method for predicting total nitrogen in effluent, addressing the problems of large modeling errors and decreased prediction accuracy caused by outliers in the sampled data. The method includes: first, constructing an event-driven fuzzy neural network to predict the future dynamics of the system; second, designing an event partitioning mechanism based on the error fluctuation between the predicted and actual system output values, dividing the system state into four different events; and finally, employing an event-based adaptive parameter update strategy to effectively suppress the interference of outliers on system modeling, improve prediction accuracy, and achieve accurate prediction of the total nitrogen concentration in the effluent, thereby ensuring the efficient and reliable operation of the urban wastewater treatment process.

The present application relates to the technical field of ship cabin intelligent lighting, and particularly relates to a ship cabin intelligent lighting system and a lighting brightness adjusting method thereof, the system comprising a data acquisition module, a data cleaning and estimation module, an illumination calculation module, a fuzzy neural network control module, an adjustable light LED lighting execution module, a human rhythm coordination module, and a fault self-diagnosis and redundancy switching module. The present application improves the accuracy of cabin brightness real value estimation and the system anti-interference ability in complex sea conditions, solves the problem of fixed light source illumination distribution change when the ship is rocking, ensures that each work plane in the cabin always obtains stable and suitable illumination, reduces visual discomfort caused by ship movement, responds faster, has smaller overshoot, and can balance energy saving and comfort targets, helps to adjust the biological clock of the crew, relieve sailing fatigue, improve the psychological and physiological health of long-term sea operation, avoids ship lighting failure, and is convenient for expansion and maintenance.

This invention discloses a preset-time formation control method for vehicle systems under narrow tunnel constraints, belonging to the field of unmanned vehicle control and cooperative formation control technology. The method includes: setting vehicle position constraints and performance constraints for formation errors; integrating nonlinear command filtering techniques with parameter estimation mechanisms; designing a preset-time fault-tolerant formation control strategy by combining generalized fuzzy neural networks and Butterworth low-pass filtering techniques; and achieving stable and safe formation structure maintenance for heterogeneous vehicle systems within a preset time. This invention solves the problems of difficult constraints in general curved tunnels, non-preset convergence time, complex controller design, and difficulty in non-affine fault tolerance, achieving safe, efficient, and preset-time convergent formation control.

This invention belongs to the field of vehicle braking technology, specifically providing a brake pad wear diagnosis method based on a fuzzy neural network. The method includes: determining characteristic variables of brake pad wear thickness as input variables; constructing an initial brake pad wear diagnosis model based on a fuzzy neural network; acquiring training samples and inputting them into the initial brake pad wear diagnosis model, and determining a final brake pad wear diagnosis model using an adaptive particle swarm optimization algorithm; inputting the input variables into the final brake pad wear diagnosis model to calculate the brake pad wear thickness and remaining thickness; determining whether the remaining brake pad thickness is less than a preset minimum brake pad thickness threshold; obtaining the brake pad wear thickness using a brake pad wear prediction model established based on a fuzzy neural network, and optimizing the model parameters using an adaptive particle swarm optimization algorithm; and calculating the remaining thickness based on the wear thickness, thereby achieving real-time monitoring and diagnosis of brake pad wear in the absence of a brake pad thickness sensor.

This invention provides an adaptive window clustering method for shale MAPs based on a fuzzy neural network, relating to the fields of oil and gas development and image segmentation. Specifically, it includes the following steps: performing domain decomposition, nonlocal mean filtering, and gray-level normalization on the shale MAPs; dynamically adjusting the sliding window based on local gray-level variance; characterizing the shale micro-features within the sliding window into gray-level features and structural features using the gray-level co-occurrence matrix and structural parameters; and combining the gray-level features and structural features as a feature vector and inputting it into a fuzzy neural network (FNN) for micro-feature clustering. The technical solution of this invention overcomes the problem in existing technologies that cannot simultaneously consider the multi-scale micro-features and boundary uncertainties in shale MAPs.

This invention relates to a fuzzy neural network-based self-tuning PID control system for corrugated board warpage deformation, applied in the field of corrugated board production technology. The system includes: a data acquisition unit for real-time acquisition of raw paper moisture content, preheating cylinder temperature, ambient relative humidity, production line speed, and composite tension; a warpage detection device for detecting the amount of board warpage; a fuzzy neural network controller for outputting PID parameter adjustment values; a PID parameter self-tuning unit for real-time calculation of PID parameters; a PID controller for calculating control values; an actuator unit for adjusting operating parameters; and a graded trigger optimization module for graded online fine-tuning based on a superior product threshold and a qualified product threshold: no update when deviation ≤ superior product threshold, data is stored only when superior product threshold < deviation ≤ qualified product threshold, and an update is triggered when deviation > qualified product threshold. This invention solves the problems of low control accuracy and frequent ineffective adjustments in existing technologies, improving system stability and adaptability.

The application discloses a battery state of health evaluation method and device based on an improved adaptive fuzzy neural network, and belongs to the technical field of battery management. The method comprises the following steps: firstly, a lithium battery capacity aging model is constructed, a plurality of groups of health indexes are extracted from charging and discharging data and are subjected to median-quartile range normalization processing; then, key features are screened out by comprehensively considering feature accuracy, correlation and extraction complexity through a random forest-AHP method; subsequently, Bayesian optimization is adopted to determine hyperparameters such as the number of membership functions and initial standard deviation, a rule activation mechanism of a dynamic parameter optimization system driven by working conditions is introduced, model parameters are iteratively updated in combination with a recursive least square method and a gradient descent method, and high-precision estimation is realized; finally, a full life cycle evaluation system covering four stages of normal, attention, abnormal and serious is constructed, and health indexes are dynamically adjusted. Through the introduction of dynamic parameters driven by working condition features, high-precision SOH estimation under complex working conditions is realized.

The application relates to the technical field of new energy battery thermal management, in particular to a water-cooled proton exchange membrane fuel cell thermal management control system, which comprises a data acquisition module, a model calculation module, a risk prediction module and a collaborative control module. The data acquisition module collects operating condition data such as stack output current and voltage, cooling liquid temperature and flow, and environmental temperature and humidity; the model calculation module constructs an improved electrochemical-thermal coupling model based on an extended equivalent circuit model, calculates the internal heat generation power distribution and temperature field of the stack; the risk prediction module predicts the cooling liquid phase change risk area and evaluates the local hot spot generation probability; and the collaborative control module adopts an improved fuzzy neural network algorithm optimized by an online self-adaptive mechanism, and generates a cooling liquid circulating pump and stack fan collaborative control instruction in combination with multiple parameters. The scheme can finely analyze the internal thermal state of the stack, accurately predict thermal hazards, and realize dynamic collaborative regulation of the heat dissipation components.

This application discloses a hot blast stove control method and system based on the inlet temperature of an SCR reactor, relating to the field of automatic control. The method includes: acquiring multi-source data; obtaining predicted second SCR reactor inlet temperature data through a long short-term memory network; constructing a multi-objective fitness function to obtain the optimal inlet target temperature value and the optimal air-fuel ratio; calculating the temperature difference and change; inputting the data into a pre-trained fuzzy neural network to obtain the changes in gas and air flow rates; and controlling the hot blast stove. By predicting the SCR reactor inlet temperature and dynamically correcting the optimal SCR reactor data using a multi-objective fitness function, optimal control performance is achieved under the optimal combination of SCR reactor denitrification efficiency and gas air-fuel ratio, avoiding energy waste and significant pollution during SCR reactor operation.

This invention relates to the field of adaptive actuation technology for ramps, specifically to a dynamic wind-resistant stability control system and method for the ramp adjustment process of a roll-on / roll-off platform. In this invention, a fuzzy neural network is introduced to compare the extreme pressure values ​​of the accumulator, mitigating the phase lag caused by simple feedback control. By calling Gaussian process regression to deduce the state prediction sequence, boundary values ​​are extracted, constraint compression operations are performed, and the pump station's output rating is compared, avoiding the risk of trajectory deviation caused by extreme gusts leading to hydraulic components approaching saturation dead zones. The matrix is ​​decomposed to extract the hinge deformation residual vector, dynamically isolating the amplification effect of high-frequency measurement noise, improving the smoothness of state estimation and the robustness against disturbances. By calling the slope matrix to convert the duty cycle and adjust the servo valve opening, the large-scale articulated mechanism maintains a strict passage threshold range under the combined effects of severe wind and waves, preventing fatigue fracture failure of the mechanical structure due to local sudden load changes.

The application discloses a dynamic load prediction and scheduling optimization method, system, device and storage medium, which comprises the following steps: collecting demand side multi-source influence factor data and preprocessing; constructing a daily characteristic vector based on the preprocessed data, calculating the multi-factor comprehensive similarity of the prediction day and each historical day, and screening the historical similar days; performing multi-scale decomposition on the load sequence of the historical similar days, constructing a fuzzy neural network prediction model, optimizing the network parameters of the model, and generating a load prediction result; setting an optimization target based on the load prediction result, implementing different demand side response strategies for different users, solving a multi-objective optimization model, generating an optimal scheme, and delivering the optimal scheme to a power load management terminal for execution; and when it is detected that the deviation between the actual load and the prediction value exceeds a preset threshold or the equipment is abnormal, an emergency optimization process is automatically triggered. The application can realize accurate load prediction and optimal allocation of power resources, and improve the safety, economy and reliability of the power system.

The application discloses a multivariable sewage treatment optimization control method and system based on a fuzzy neural network, belongs to the technical field of sewage treatment, and comprises the following steps: determining a control parameter, and acquiring an actual concentration value and a set concentration value of the control parameter in sewage; acquiring a concentration error based on the actual concentration value and the set concentration value; acquiring an ideal weight vector and an estimate of the control parameter based on an identifier-comment-execution-fuzzy neural network; acquiring a weight error based on the ideal weight vector and the estimate; acquiring a Lyapunov function of the control parameter based on the concentration error and the weight error; acquiring an update law of the identifier estimate based on the Lyapunov function; and adjusting the concentration of the control parameter based on the update law to reduce the concentration error and optimize the control process of sewage treatment. The application estimates unknown functions by using an identifier-comment-execution-fuzzy neural network in a design process, and realizes efficient adaptive control of sewage treatment.

The application provides a torpedo tank positioning control method based on a fuzzy neural network and inertia compensation, an inertia compensation model is established, the total response time of a braking system is taken as a key parameter, inertia displacement is compensated in advance, and a braking triggering position is accurately determined; meanwhile, an improved T-S fuzzy neural network is introduced to construct a dynamic mapping of working condition parameters and correction coefficients, online self-learning and adaptive adjustment of positioning parameters are realized. The scheme can effectively eliminate systematic deviation, ensure that the torpedo tank can maintain a positioning accuracy within 5cm in various complex scenes, improve production efficiency, and significantly reduce the impact on mechanical structures.

The present application relates to a kind of urban sewage treatment process multi-working condition optimization control method, first, using fuzzy neural network to build each working condition operation cost prediction model, establish dissolved oxygen, nitrate nitrogen concentration and effluent quality, multi-working condition data-driven model of operating energy consumption;Second, the automatic topology multimode optimization algorithm is developed, simultaneously as particle optimization variable, the dynamic planning performance is solved dissolved oxygen and nitrate nitrogen concentration multi-working condition dynamic optimization setting set with the number of topological branch particles of multi-working condition comprehensive objective function, according to the decision of appropriate setting value according to each working condition operation cost prediction result;Finally, using PID controller to adjust dissolved oxygen transfer coefficient and internal reflux, the optimized setting value of dissolved oxygen and nitrate nitrogen is tracked control, avoid the high calculation burden of multi-working condition complex constraint modeling, realize the efficient, intelligent optimization control under the multi-working condition of urban sewage treatment process.

This invention proposes a laser power nonlinear control method and system based on fuzzy neural networks, relating to the field of laser control technology. The method includes: acquiring power control parameters of a fiber laser and constructing a power input feature vector based on the power control parameters; performing digital conversion and preprocessing on the power input feature vector to obtain control input parameters; fuzzifying the control input parameters to obtain membership data, and constructing a laser power nonlinear control model based on a fuzzy neural network according to a preset fuzzy rule base and the membership data; inputting the control input parameters into the laser power nonlinear control model, performing fuzzy inference and fuzzy neural network forward calculation to obtain a laser drive control quantity for adjusting the fiber laser, and using the laser drive control quantity to regulate the power of the fiber laser.

The application discloses a kind of based on digital twin driving and modal space decoupling adaptive magnetorheological liquid force inertia damper array system and control method.System includes sensor module, control module and execution module.Sensor module gathers tower cylinder multiple-point vibration response signal and prospective wind field data;Execution module is composed of magnetorheological liquid force inertia damper arranged at different heights of tower cylinder, and each damper realizes independent continuous regulation of damping coefficient and inertia coefficient by exciting coil and flow regulating valve;Control module is based on intrinsic orthogonal decomposition algorithm and decouples multimode generalized coordinate online, combined with digital twin calculation engine and enhanced fuzzy neural network to execute double time scale collaborative decision, and the generalized modal control force is mapped into each damper independent execution instruction by independent modal space control strategy, and the array actuation phase is controlled in cooperation, to eliminate control force internal loss.System has model online evolution and hierarchical redundant fault-tolerant mechanism.

This invention relates to the field of agricultural product logistics technology, and discloses an intelligent logistics transportation system and method for agricultural products. The method includes: collecting multi-dimensional sensor data from the vehicle's cargo compartment; establishing a dynamic baseline model after wavelet denoising and Kalman filtering; identifying and labeling anomalies in temperature, humidity, and gas concentration; generating environmental control commands through a fuzzy neural network to achieve precise control; integrating equipment health, traffic, and weather information for collaborative path planning to obtain a dynamically optimal driving path; constructing a tamper-proof traceability chain by associating full-process data at each logistics node to achieve verifiable quality traceability throughout the entire process; simultaneously predicting health status and failure probability based on equipment operating signals to generate maintenance suggestions; and finally, aggregating full-process data through a cloud platform to continuously optimize the model, forming a self-evolving intelligent logistics system. This invention achieves collaborative management of environmental control, path optimization, full-process quality traceability, and predictive maintenance of equipment in agricultural product transportation.

The application provides a general emergency door lock opening method and system applied to a new energy vehicle, and belongs to the technical field of new energy vehicle emergency safety. The method comprises the following steps: collecting multi-source signal data of the vehicle in real time; pre-processing the collected multi-source signal data to generate a standardized signal data set; inputting the standardized signal data set into an improved fuzzy neural network decision model, combining a scene adaptive threshold value output in real time by a dynamic threshold value self-adaptive adjustment model to judge whether a vehicle door unlocking condition is met; and when it is determined that the unlocking condition is met, sending an unlocking instruction to a door lock controller by a vehicle control module. The application realizes accurate identification and rapid response to multiple dangerous scenes such as collision, battery overheating and power failure, solves the problems of high misjudgment rate and poor adaptability of a traditional scheme, and has high reliability, strong universality and continuous evolution capability.