67 results about "Non linear functions" patented technology

The application discloses a kind of chain CAV queue shaping improvement swarm control methods based on virtual guiding berth, first, intelligent network connection unmanned vehicle team is the research object, the force between each unmanned vehicle in the system of unmanned vehicle intelligent agent is described using nonlinear function relationship;Second, the chain unmanned vehicle queue shaping improvement swarm control algorithm based on multiple virtual guiding berth is designed.The results show that the algorithm realizes the safe, rapid and stable constraint of different initial states, different number of intelligent network connection unmanned vehicles into regular chain queue (single / double chain) and drives out the intersection without signal light, and all unmanned vehicles maintain reasonable spacing, avoid collision while speed gradually reaches agreement during the completion of swarm process, realize the stable planning and tracking of intelligent network connection unmanned vehicle team, which is of great significance to improve the driving safety and road traffic efficiency of unmanned vehicle queue.

The application provides a kind of IGBT junction temperature prediction model construction method and system, it is related to power electronic device thermal management technical field, method includes: obtaining the initial data set of the transient thermal response relationship between the IGBT chip and the NTC temperature measuring point in the IGBT power module;Based on the initial data set, the neural network model is trained, and the nonlinear function relationship between time and thermal impedance is learned and characterized;The preset new time series is input into the trained neural network model, and the optimized thermal impedance data set with continuous time and noise suppression is generated;Based on the data set, the lumped parameter thermal network model is parameter fitted, to identify its thermal resistance and heat capacity parameters, complete the construction of junction temperature prediction model.The application uses neural network as a data optimization tool in the parameter identification process of physical model, only uses the temperature signal of NTC temperature measuring point provided by the module, realizes the accurate, convenient online prediction of IGBT junction temperature.

This invention discloses a rapid method for detecting the moisture content of fine aggregates used in concrete, belonging to the field of building materials testing technology. The method involves collecting samples of different types of fine aggregates, preparing multiple moisture content samples from oven-dry to saturated surface-dry states, and then measuring the resistivity values ​​after compaction under constant pressure to form a dataset. Based on this dataset, a specific model is established using nonlinear function fitting, and a general prediction model is established using machine learning algorithms. During on-site testing, the resistivity values ​​of the fine aggregates to be tested are measured after compaction under the same constant pressure, and the moisture content is calculated according to the type of aggregate using the appropriate model. This invention achieves rapid and non-destructive testing of the moisture content of fine aggregates, providing real-time data for adjusting water usage in concrete production.

The application discloses a Helicobacter pylori breath test signal denoising fitting method and system, and belongs to the technical field of medical detection and signal processing. The method processes the continuous monitoring signal of the 13CO2 / 12CO2 isotope ratio in exhalation: firstly, the original signal is subjected to band-pass digital filtering to suppress high-frequency noise; then, low-frequency drift is eliminated through adaptive baseline correction to generate a drift-removed signal; subsequently, a robust peak value recognition algorithm is used to extract physiological response characteristics; finally, the identified peak value is taken as the center, a nonlinear function with physiological constraints is used for local fitting, and the Delta Over Baseline value is calculated based on the generated fitting curve with high precision. The application effectively solves the signal distortion problem caused by breathing fluctuations, instrument noise and the like in in vivo detection, realizes high-fidelity restoration of the Helicobacter pylori metabolic response, and significantly improves the detection accuracy and reliability of the breath test.

The invention discloses a field weakening trajectory tracking control method for permanent magnet synchronous motors based on enhanced model predictive current control, comprising: S1, establishing a mathematical model of the permanent magnet synchronous motor in a synchronous rotating coordinate system and determining the optimal operating trajectory in the field weakening region; S2, constructing an extended virtual voltage vector set synthesized from the basic voltage vector, and constructing a virtual voltage vector containing continuous components, excess components, and disturbance components as feedback quantities, which are adjusted by the field weakening current controller. d The shaft reference current limits the virtual voltage amplitude within the inverter output voltage limit; S3, a nonlinear extended state observer based on a nonlinear function is designed to estimate the current state and total disturbance in real time, and correct the current prediction model. The optimal voltage vector is selected through a cost function, and a switching pulse sequence is generated to drive the inverter. This invention can effectively reduce current ripple when motor parameters are mismatched and overcome the deviation of the field weakening trajectory when model parameters are mismatched.

The application discloses a nonlinear Fourier reweighting image resolution enhancement method and system without a point spread function. The method comprises the following steps: acquiring an original digital image; performing Fourier transform on the original digital image to separate an amplitude spectrum and a phase spectrum; constructing a nonlinear mapping function with two adjustable parameters, and performing reweighting processing on the amplitude spectrum by using the function to balance high and low frequency energy of the amplitude spectrum and generate a new amplitude spectrum; recombining the new amplitude spectrum and the original phase spectrum; and performing inverse Fourier transform on the combined spectrum to output a resolution enhanced image. The application does not depend on PSF measurement or estimation, only needs one Fourier transform process, and has high calculation efficiency. The core construction is to directly reweight the amplitude in the frequency domain by using a specific nonlinear function, so that the ill-conditioned problem and artifacts of a traditional deconvolution algorithm are avoided. The application can be widely applied to optical fluorescence microscopy, astronomical imaging and clear processing of general digital images.

This invention discloses a method and system for transformer partial discharge pattern recognition based on an optimized probabilistic neural network. The method first acquires the transformer partial discharge signal; then, it establishes a two-dimensional spectrum of the partial discharge phase distribution pattern based on the partial discharge signal and extracts discharge statistical features from this spectrum; finally, it inputs the discharge statistical features into the optimized probabilistic neural network for pattern recognition to obtain the partial discharge pattern. The optimized probabilistic neural network uses a pollination algorithm to optimize the smoothing factor, and the switching probability in the pollination algorithm is a nonlinear function that decreases with the number of iterations. This invention can more accurately and efficiently classify and identify different partial discharge patterns of transformers, providing a data foundation for transformer fault diagnosis and resolution.

The application discloses a method for adaptive secure consensus control of a multi-agent spatio-temporal dynamic system with unknown boundary nonlinearity under hybrid attacks, comprising the following steps: constructing a multi-agent spatio-temporal dynamic system model with unknown boundary nonlinearity and a virtual leader model; establishing a hybrid network attack model containing deception attacks and denial of service (Dos) attacks, and using an adaptive radial basis neural network to approximate the unknown boundary nonlinearity function; defining a consensus error signal to obtain a consensus state error system; designing a composite adaptive neural network secure boundary consensus control scheme, constructing a Lyapunov function for the error system, and obtaining a sufficient condition for the error system to realize mean square secure consensus control. The application effectively solves the problem that the traditional method is difficult to simultaneously cope with complex network attacks and unknown boundary nonlinear disturbances, and significantly improves the security and robustness of the system.

The application discloses an adaptive tracking control method for a random nonlinear system based on output feedback, comprising the following steps of S1, constructing a mathematical model of a random nonlinear system with hysteresis input, unknown control direction, arbitrary switching and output constraint; S2, designing a linear state observer, and reconstructing unmeasurable states of the random nonlinear system by using output and control input of the random nonlinear system; S3, introducing a Nussbaum type function to process the unknown control direction, and constructing a barrier Lyapunov function to ensure that the output constraint is not violated; and S4, designing a framework based on a backstepping method, combining a radial basis neural network to approximate unknown nonlinear functions, designing a virtual control law, an adaptive law and an actual control law, and realizing adaptive tracking control of the output of the random nonlinear system on a reference signal.

This invention provides a control method for a lower limb exoskeleton rehabilitation robot based on the sliding mode optimization method, belonging to the field of medical rehabilitation robot control technology. The method includes: establishing a two-degree-of-freedom dynamic model of the lower limb exoskeleton rehabilitation robot, and transforming it into a linear state-space model using feedback linearization technology; designing a linear quadratic regulator (LQR) for the linearized model to obtain the optimal feedback gain matrix; constructing an integral sliding surface containing the optimal feedback gain matrix, so that the equivalent control law of the system in the sliding mode possesses the globally optimal dynamic performance of the LQR; and based on adaptive preset time theory, constructing a sliding mode reaching law and an adaptive gain update law containing novel nonlinear functions to generate the final control torque.

The present application relates to a kind of discrete multi-motor servo system fuzzy wavelet neural network control method with event triggering mechanism, belong to multi-motor servo system control field, comprising the following steps: S1: the discrete time system model of multi-motor servo system is established;S2: design two type fuzzy wavelet neural network to estimate unknown nonlinear function caused by external interference and internal parameter perturbation;S3: based on the backstepping control framework, introduce event triggering mechanism with dead zone operator, design discrete time fuzzy wavelet neural network controller;S4: using the discrete time fuzzy wavelet neural network controller, control multi-motor servo system is carried out.

This application belongs to the field of delay differential equation modeling, specifically disclosing a delay differential equation modeling method based on Bayesian optimization and neural networks. The method includes: generating multiple trajectory data of the system; searching for the optimal delay term using a Bayesian optimization algorithm, where the delay term to be solved is the optimization variable, and the error based on neural network simulation is the objective function, obtaining the optimal delay term through iterative optimization using a surrogate model; constructing a state matrix and a delay matrix from the trajectory data based on the optimal delay term, using the concatenated matrix as input to a neural network to construct a neural network for approximating a nonlinear function; integrating a linear multi-step method into the loss function of the neural network, training the neural network using the trajectory data to obtain a nonlinear function approximation model; and separating the numerical discretization error and the neural network approximation error based on this model to construct a total error bound. This application can achieve high-precision, high-efficiency delay differential equation modeling with quantization error guarantee.

The application discloses a privacy protection joint diagnosis method based on confusion and belongs to the technical field of information security, and comprises the following steps: three computing servers acquire feature secret shares and weight secret shares in the form of replicated secret sharing respectively; each computing server performs privacy protection reasoning of a diagnosis model according to the feature secret shares and the weight secret shares held by the computing server, wherein multiplication based on replicated secret sharing is used to realize linear function reasoning in the diagnosis model, intermediate feature secret shares of input nonlinear functions are disordered by a random permutation protocol, then the intermediate feature secret shares are reconstructed into plaintext, plaintext nonlinear operations are performed, and inverse permutation protocol is used to restore the nonlinear function reasoning results in the form of replicated secret sharing, that is, nonlinear function reasoning in the diagnosis model is realized. The data space structure is disordered, so that the privacy is ensured before the data is opened, and complex nonlinear operations are removed from the MPC closed loop, so that the interaction cost is greatly reduced.

The application provides a function secret sharing-based Transformer secure inference method, which is used for completing Transformer inference without leaking the privacy of participants.The method mainly includes three parts: a linear layer secure calculation protocol, a bounded univariate nonlinear function secure calculation protocol and a nonlinear function secure calculation protocol.The application can be used for secure inference service of a Transformer-based large model, and the privacy information of the participants is protected.Compared with the existing method, the running time and communication volume are optimized by 3.00 and 3.88 times.

The present disclosure provides a model training method, comprising: determining a first data set, the first data set comprising a plurality of samples in an initial density distribution, the initial density distribution representing an initial value probability distribution within a preset sample interval; training an initial model using the first data set to obtain a first training result; adjusting a sample density distribution weight of the preset sample interval based on a difference between the first training result and a target training result to obtain a second data set, the target training result being based on a corresponding data activation of a second activation function, the second activation function being a nonlinear function; and repeatedly adjusting the sample density distribution weight and training to obtain a target model. The present disclosure adjusts the sample density distribution weight of the preset sample interval and trains the model using the data set after adjusting the distribution weight, thereby improving the accuracy of model training.

Methods and devices of a function approximation unit configured to approximate a nonlinear function within a neural processing unit are described. According to one embodiment, the method includes storing an input value through an input register of the function approximation unit, transmitting the input value to a selected one of a plurality of preprocessing circuits of the unit according to a control signal, generating a preprocessing result corresponding to the input value by the selected one of the preprocessing circuits, transmitting the preprocessing result to a programmable function approximation circuit and a selected one of a plurality of post-processing circuits of the unit, generating an approximated function output based on the preprocessing result in the programmable function approximation circuit, and generating a final output value by post-processing the preprocessing result or the approximated function output in the selected one of the post-processing circuits.

The application discloses a kind of micro steering engine control methods based on adaptive instruction filter, the method first utilizes extension state observer to estimate load torque in real time;Second, the normalized load estimate is mapped to the time constant of instruction filter by nonlinear function, the filter bandwidth is adaptively reduced with load increasing;Finally, the error control is carried out to filtered instruction and feedback signal, and combined with load estimate value to carry out feedforward compensation, and drive signal is generated jointly.The application makes micro steering engine keep fast response when light load, effectively suppresses overshoot and oscillation when heavy load or load mutation, significantly improves the dynamic performance and robustness of system in whole working condition, and engineering implementation is simple, and compatibility is good.

This invention claims protection for a digital back-end calibration system for pipeline ADCs, comprising a down-conversion unit, an ADC to be calibrated, a low-speed, high-precision ADC, an LMS adaptive filter, and a subtractor; it solves the problem of decreased system accuracy in pipeline ADCs due to inter-stage gain errors. An inverse hyperbolic tangent function is introduced into the original algorithm model to construct a nonlinear functional relationship between the step size and the error signal, jointly constraining the step size value so that the current step size value is related to the square of the ratio of the current error to the previous error, improving the convergence speed of the algorithm. Simultaneously, step size normalization processing is combined to increase the range of step size selection and improve stability. A low-speed, high-precision ADC is used as a reference, connected in parallel with the ADC to be calibrated, and the difference between their digital outputs is sent to the LMS adaptive filter for processing, so that the output of the ADC to be calibrated continuously approaches the output of the low-speed, high-precision ADC, while the conversion process of the original ADC remains unaffected, achieving the purpose of digital calibration.

The present application belongs to the field of computer vision and unmanned aerial vehicle autonomous navigation technology, and specifically relates to a smooth path planning method for unmanned aerial vehicle three-dimensional reconstruction, which iteratively adjusts the front cluster segmentation threshold according to the field of view overlap ratio of adjacent sampling viewpoints on the scene surface, realizes dynamic adjustment of global viewpoints according to the complexity of the environment, and solves the problems of poor path smoothness, viewpoint redundancy, and difficulty in balancing reconstruction efficiency and quality in the prior art. On this basis, a two-stage viewpoint optimization strategy is adopted: the first stage reduces the number of viewpoints, reduces redundant data and planning complexity; the second stage selects high-contribution viewpoints through a neighborhood competition mechanism to improve reconstruction effect. At the same time, a nonlinear function is introduced to model the coupling relationship between the linear velocity and the yaw angular velocity of the unmanned aerial vehicle, and to correct the deviation of the traditional path cost estimation. As can be seen from the above, the present application can effectively improve the reconstruction integrity and accuracy, reduce the path complexity, balance the reconstruction efficiency and quality, and is suitable for unmanned aerial vehicle three-dimensional reconstruction tasks in complex scenes.

The embodiment of the application provides a kind of entity unit isogonic analysis method, device, equipment and storage medium for variable-thickness composite material, the method comprises: based on entity geometry model, construct the entity grid model of NURBS, and extract the parameterization information of NURBS;Local coordinate system is established in each gauss point inside geometry entity using parameterization information, determine the first conversion relationship of local coordinate system and global physical coordinate system;Based on entity grid model, construct nonlinear function in parameter space, use L-M algorithm to iteratively solve the intersection of each ply offset surface and thickness direction parameter curve of variable-thickness composite material, determine the integral interval inside each entity unit;Based on geometric volume integral term and the ply trigonometric function of variable-thickness composite material, determine global stiffness matrix;Based on global stiffness matrix, execute isogeometric analysis to determine the laminated structure strain energy, vibration frequency and buckling factor of composite material by loading and defining boundary conditions.

This invention discloses a position angle controller and its control method based on an improved fractional-order active disturbance rejection (ADRROC) module. The position angle controller includes a TD module, a FO-ESO module, and a FO-NLSEF module. The output of the TD module is connected to the input of the FO-NLSEF module. The output of the FO-NLSEF module is connected to the controlled object and the FO-ESO module. The input of the FO-ESO module is connected to the controlled object, and its output is connected to the FO-NLSEF module. This invention utilizes improved ADRROC technology to achieve motor position angle control. By improving the convergence, continuity, and differentiability of the nonlinear function and adding a fractional-order module, the performance of the ADRROC position angle controller is improved and enhanced.

This invention provides a large-scale, high-precision optical Ising computer that can significantly increase the number of spins, achieve optimal binary bifurcation for the problem to be solved using an arbitrary nonlinear function, and converge stably and quickly. [Solution] The system includes a pulse light source 1, an optical fiber loop 3 into which optical pulses from the pulse light source 1 are introduced, an erbium-doped optical fiber amplifier 4 provided within the optical fiber loop 3, a homodyne detection circuit 5 for detecting optical pulses from the optical fiber loop 3, a matrix-nonlinear calculation circuit 6 for digitally calculating an interaction function and a nonlinear function for binary branching for the optical pulses detected by the homodyne detection circuit 5, and an AM optical modulator 2 which receives the optical pulses from the pulse light source 1 and the information obtained from the digital calculations of the matrix-nonlinear calculation circuit 6, modulates the input optical pulses, and then injects them into the optical fiber loop 3.

The present application relates to a kind of electromagnetic reversing valve life prediction method based on flow signal, belong to hydraulic component life prediction field.The present application utilizes improved lumped average modal empirical decomposition method, by adding positive and negative pairs of noise to reduce the degree of modal aliasing in modal decomposition, using permutation entropy to detect abnormal component, realize the accurate adaptive modal decomposition of nonlinear measured signal;Application kernel principal component method, introduce nonlinear function as kernel function, based on the principle of mapping, convert original space into high-dimensional space, form new data set, using principal component analysis for data dimension reduction in high-dimensional data space, form feature vector, get performance degradation fusion index;Through cubic exponential smoothing processing;Finally based on the trained adaptive neural network model, establish the life prediction model of electromagnetic reversing valve, calculate the life of electromagnetic reversing valve.This method can effectively predict the pressure drop trend and life of electromagnetic reversing valve.

The application discloses a kind of without speed electromagnetic bearing-rotor system automatic balancing method, this method first establishes the mathematical model of linear extended state observer (LESO), then designs the control structure of linear extended state observer wave trap, further carries out linear extended state observer pole configuration and inlay physical model, finally introduces Fal nonlinear function.The application does not need real-time speed information, can effectively reduce the same frequency vibration of electromagnetic bearing-rotor system, and significantly reduces control current and base vibration.

This invention relates to the field of satellite navigation and positioning and multi-source information fusion technology, specifically a low-cost, high-precision GNSS navigation method for complex urban environments. The method constructs a dual-channel adaptive weighting model based on scene classification results. Pseudorange observations are weighted using a nonlinear smooth reduction based on the Sigmoid function, while carrier phase observations are weighted using a two-way reward / penalty system based on signal-to-noise ratio and environment type. This invention achieves a smooth transition of pseudorange weights through the Sigmoid nonlinear function, avoiding filter divergence caused by hard cutoff. A unique open-field carrier reward mechanism improves positioning accuracy in ideal environments. Furthermore, without increasing hardware costs, only algorithm upgrades can significantly improve the navigation performance of low-cost chips in complex urban environments, demonstrating high engineering application value.

Embodiments of the present application provide a method and device for constitutive model construction and parameter optimization of honeycomb materials, computer equipment and medium, relating to the technical field of electronic digital data processing, wherein the method comprises the following steps: obtaining original experimental data sets generated by mechanical experiments of the honeycomb material under multiple different working conditions; fitting to obtain a shape function representing the stress-strain relationship under a reference working condition, constructing a density function representing the influence of density on mechanical properties, using an exponential nonlinear function, constructing a strain rate function representing the influence of strain rate on mechanical properties, and constructing a temperature function representing the influence of temperature on mechanical properties; constructing a constitutive model of the honeycomb material based on multi-factor correction factors; taking the minimum mean square error between the stress-strain curve predicted by the constitutive model and the target curve as the optimization target, performing global iterative optimization and outputting. Through the constructed constitutive model, the problem of strong dependence of traditional model parameter fitting and poor universality is solved.

A method includes: generating a first sum value at least by a first resistor; generating a first shifted sum value based on the first sum value and a nonlinear function; generating a pulse number based on the first shifted sum value; and changing the first resistor based on the pulse number to adjust the first sum value.

This invention relates to a secure computation method and system for nonlinear functions based on unintentional shuffling and splitting outsourcing, belonging to the field of computer software technology. The method includes: generating dynamic permutation parameters for inference requests via a client; performing unintentional shuffling on the ciphertext through two mutually distrustful servers based on the dynamic permutation parameters; splitting the unintentionally shuffled data into a first part and a second part, with each server reconstructing the first and second parts respectively to obtain partial plaintext; calculating a nonlinear function using the partial plaintext and merging the results; and performing inverse unintentional shuffling on the merged results to obtain the final nonlinear layer output. This invention transforms expensive online secure matrix multiplication into low-cost vector addition and single-round communication operations, achieving "one-time pad" dynamic permutation at extremely low cost, fundamentally resisting statistical inference attacks.

A vehicle comprises: an advanced driver assistance system (ADAS) generating a command for an aspect of vehicle motion; an actuator for controlling the aspect of the vehicle motion, the actuator connected to the ADAS; and a bias estimator circuit that estimates a bias between the command for the aspect of vehicle motion and an effective vehicle motion aspect, the bias estimator circuit estimating the bias using an operator that predicts a next timestep of nonlinear functions of vehicle state, wherein the command for the aspect of vehicle motion is corrected using the bias estimated by the bias estimator circuit to provide a corrected command for the aspect of vehicle motion for use by the actuator.