49 results about "Function approximation" patented technology

Systems and methods for performing hardware approximation of functions are provided. In one example, a system comprises a controller, a plurality of multiplexors, configurable arithmetic circuits, and a mapping table that stores a set of function parameters. According to a mode of operations, the controller may configure the plurality of multiplexors to forward the set of function parameters or a subset of the function parameters to the arithmetic circuits to compute an approximation result. In a case where the subset of the function parameters is forwarded to the arithmetic circuits, the controller may configure the arithmetic circuits to perform post-processing, such as quantization, of the approximation result.

The invention discloses a mobile robot autonomous navigation planning method based on brain-like pulse deep reinforcement learning. Firstly, a brain-like pulse neural network (SNN) is constructed as a Q function approximator, and the energy efficiency of path planning is improved by using the time sequence processing capability and biological rationality of the SNN. Secondly, designing a double-priority experience playback mechanism, dividing experience samples into a common experience pool and an elite experience pool according to a reward threshold value, adjusting the sampling proportion of the two experience pools by adopting a dynamic self-adaptive sampling strategy, and intelligently balancing the sample utilization rate in the training process according to a loss function and a training stage; in addition, the reward function design is optimized through Manhattan distance reward and a repeated access punishment mechanism, and the robot is guided to efficiently explore the environment.

An example operation may include at least one of loading an Artificial Intelligence (AI) model from the storage, wherein the AI model is one of a diffusion-based model or a flow-based model, receiving tabular input data for execution by the AI model, wherein the tabular input data is scaled with a class-conditional scaler, creating a multi-output Gradient Boosted Tree (GBT), creating a Scalable AI (SAI) model from the AI model by using the multi-output GBT as a function approximator, and generating synthetic data by at least one of: executing the SAI model on the tabular input data or implementing a trained SAI model with the tabular input data, wherein the generating synthetic data reduces processing and memory resources.

The invention relates to a new energy power station configuration optimization method based on an agent model and multi-objective optimization. The method comprises the steps of obtaining historical power system configuration parameters and corresponding historical stability characteristic values, and performing preprocessing; inputting the preprocessed historical power system configuration parameters into a neural network agent model based on a multi-layer perceptron architecture, performing offline training in combination with corresponding historical stability characteristic values and physical prior constraints, and outputting predicted stability characteristic values; taking the prediction stability characteristic value as an optimization target, and constructing a target function of multi-target Bayesian optimization; and iteratively solving a Pareto optimal solution set based on the objective function, and determining an optimal new energy power station configuration scheme. According to the method, multi-objective optimization and Bayesian optimization are combined, a multi-objective acquisition function is adopted to approach the Pareto frontier between stability characteristic values, precision and multi-objective tradeoff can be considered, and a more flexible and scientific configuration scheme is provided for new energy station construction.

A secret share value [y]=[δx2+ax] is obtained through secure computation using a secret share value [x] of a real number x, and a secret share value [func(x)]=[y(ζy+b)+cx] of an elementary function approximation value z=func(x) of the real number x is obtained and output through secure computation using secret share values [x] and [y]. Here, x, y, and z are real numbers, a, b, c, δ, and ζ are real number coefficients, and a secret share value of · is [·].

The present disclosure relates to exponential and logarithmic function approximations for floating point numbers. The methods presented herein enable efficient approximation of exponential or logarithmic functions using values in half-precision floating point format. In at least one embodiment, an activation function of a neural network can perform tasks such as converting raw scores from a network into probabilities using an exponentiation of each output. A fixed point representation of each raw value can be generated that includes a bias factor and used to generate an intermediate representation having a determined number of bits. The intermediate representation can be incremented such that the mantissa corresponds to the fixed point representation. The decimal point can be shifted to obtain a value that is an approximation of the exponent of the raw value and can be used to determine a probability (or other such value) for a corresponding computational operation. At least some of these operations can be reversed to similarly obtain an approximation of a logarithmic function.

The invention discloses a security constraint unit commitment method based on tensor completion approximate dynamic programming, and the method comprises the following steps: firstly, building a security constraint unit commitment model based on a Markov decision process; secondly, decoupling the multi-period security constraint unit commitment model into a single-period sub; then, obtaining a value function and a decision function by adopting an approximate dynamic programming algorithm based on tensor completion; according to the approximate dynamic programming algorithm based on tensor completion, the approximate dynamic programming algorithm is improved from the angle of tensor completion, and all value functions and decision functions are approximately obtained by sampling a small number of state variables and decision variables. Decision is made from the angle of tensor complementation, in the face of a discrete state space, the value function of the whole state space can be approximated only by sampling a small number of state points, the calculation burden of an approximate dynamic programming algorithm in the aspect of value function approximation is effectively relieved, and the calculation efficiency of the algorithm can be greatly improved.

The present application provides a flow field prediction method, including: collecting sample data under different airfoils and different flow conditions; preprocessing the sample data; inputting the preprocessed data into a neural network for prediction to obtain a prediction result; iteratively refining the prediction result using a physical solver to obtain a flow field prediction result that meets the convergence constraint. The present application applies a neural network for prediction, and calls OpenFOAM as a physical solver for iterative refinement, coupling the neural network with the physical solver to obtain a new data-driven framework that can be used for flow field prediction, making full use of the function approximation ability of the neural network and the characteristics of the physical solver to ensure convergence constraints, accelerating the convergence process, and being able to adapt to different flow conditions and geometric shapes to achieve high-precision and high-efficiency predictions. The present application also provides a flow field prediction system, a computer-readable storage medium, and an electronic device with the above-mentioned beneficial effects.

The application provides a vibration isolation method, system and device of a control moment gyroscope system, which can be applied to the technical field of control. The method comprises the following steps: determining current kinematic parameters based on disturbance forces received by multiple components during operation; constructing a differential equation representing a dynamic relationship based on the current kinematic parameters and coupled dynamic characteristics; inputting a system state vector into a disturbance estimation network to output a disturbance estimation value; inputting the system state vector into the differential equation to obtain a disturbance theoretical value; inputting the disturbance estimation value and the disturbance theoretical value into a Hamilton function to obtain an initial control input quantity corresponding to a Hamilton function value satisfying a preset constraint condition; and updating the initial control input quantity in a gradient descent direction of a performance index function approximation value corresponding to the initial control input quantity until a difference between adjacent two performance index function approximation values is less than a preset threshold value, so as to obtain a target control input quantity.

The application discloses a kind of based on attention mechanism and reinforcement learning's ultra-short-term DNI prediction method, comprising: collecting solar thermal power station historical measured direct normal irradiance data and meteorological characteristic data;Periodic time characteristics and historical lag term are introduced to enhance time series dependence;Utilize bidirectional long short-term memory network to extract DNI time series features, construct attention mechanism based on dynamic change of cloud amount, strengthen the response capability of model to weather mutation;Adopt KAN to replace traditional full connection to high-dimensional nonlinear characteristics Adaptive function approximation is carried out;Introduce PPO reinforcement learning algorithm, dynamically optimize model feature representation and parameter, realize error self-feedback correction;Output DNI prediction result, for the dispatching and operation control of concentrating solar thermal power generation system.The method can effectively improve the DNI prediction accuracy and robustness under complex weather conditions, provide high-precision prediction support for photo-thermal power generation and renewable energy grid connection.

The invention belongs to the technical field of robot intelligent control, and particularly relates to a mechanical arm tracking control method based on transferable depth incremental reinforcement learning, which effectively solves the nonlinear optimal tracking control problem of a mechanical arm in a model-free mode by constructing a transferable incremental reinforcement learning framework. The method comprises the following steps: firstly, constructing a 1-degree-of-freedom depth increment model by utilizing one-step forward data offline learning, and providing universal dynamic representation for a mechanical arm which is difficult to accurately model; furthermore, an asynchronous depth value network is designed for the one-degree-of-freedom mechanical arm, stable and rapid convergence value function approximation is achieved through a separation base layer and an adaptive layer, and a cross-mechanical-arm migration mechanism is established, so that a pre-training model and a network base layer on the one-degree-of-freedom mechanical arm can be directly migrated to a high-degree-of-freedom mechanical arm subsystem; and the system difference is compensated only by updating the adaptive layer online, so that the repeated training overhead is remarkably reduced, and meanwhile, rapid, robust and adaptive tracking control on the mechanical arms with different degrees of freedom is realized.

The application relates to the technical field of computer vision and image processing, in particular to a light, fast and accurate self-supervised depth estimation method and system, which can solve the problem of recovering the depth of a scene from a two-dimensional image and needing a complex depth estimation network model to a certain extent. The method comprises the following steps: acquiring an initial image data set; constructing a weighted coefficient matrix calculation module and a sparse coding module; based on the weighted coefficient matrix calculation module and the sparse coding module, a small depth estimation network model is constructed to speed up the algorithm reasoning speed and reduce the parameter quantity of the model; a large depth estimation network model is constructed and trained by using luminosity loss as a supervision signal; the small depth estimation network model is trained by using luminosity loss and function approximation loss as supervision signals to optimize the depth estimation precision of the small depth estimation network model; finally, input data is input into the small depth estimation network model to obtain an estimation result.

A computer-implemented system and method is provided for determining a risk assessment. The method comprises receiving a plurality of vehicle behaviour data over a defined data collection period. This data is input into a supervised learning prediction model which is trained on historical vehicle behaviour data over a past time period, to generate a predicted value of a frequency of expected claim submissions for the policyholder of the vehicle in a future time period. Then a Shapley estimate is computed for each feature of the behaviour data applied to the model for determining a contribution of each said feature to the predicted value. A spline approximation is applied to the Shapley estimate for each said feature to estimate the contribution of each said feature. Then, a sum of the spline approximation for each said feature is calculated and a corresponding risk score determined based on the sum.

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 invention discloses a nonlinear causal discovery method (KAN-NOTEARS) based on a Kolmogorov-Amold network, and belongs to the technical field of artificial intelligence and causal inference. The method comprises the following steps: acquiring an observation data set containing a plurality of variables; a causal model with the learnable weighted adjacency matrix and a set of KAN functions as parameters is constructed, the KAN functions are based on the Kolmogorov-Arnold representation theorem, and a causal mechanism between variables is modeled through a learnable one-dimensional nonlinear function; constructing a target function containing structural equation model fitting loss and a sparse regular term; converting a directed acyclic graph constraint into a continuous differentiable algebraic constraint; solving the constraint optimization problem by adopting an augmented Lagrangian method to obtain an optimal adjacent matrix and function parameters; and performing threshold processing on the adjacent matrix to obtain a final cause and effect graph structure. According to the method, the theoretical advantages of the KAN network in the aspect of function approximation are utilized, the problems of insufficient accuracy and low calculation efficiency when a traditional method is used for processing a complex nonlinear causal relationship are solved, and the causal discovery precision and efficiency are remarkably improved.

A computer-implemented system and method is provided for determining a risk assessment. The method comprises receiving a plurality of vehicle behaviour data over a defined data collection period. This data is input into a supervised learning prediction model which is trained on historical vehicle behaviour data over a past time period, to generate a predicted value of a frequency of expected claim submissions for the policyholder of the vehicle in a future time period. Then a Shapley estimate is computed for each feature of the behaviour data applied to the model for determining a contribution of each said feature to the predicted value. A spline approximation is applied to the Shapley estimate for each said feature to estimate the contribution of each said feature. Then, a sum of the spline approximation for each said feature is calculated and a corresponding risk score determined based on the sum.

The invention discloses a DNN-based adaptive optimization control method for a fractional order single-machine infinite bus system, and relates to the field of single-machine infinite bus system control. The method comprises the following steps: establishing a dynamical model of a single-machine infinite bus system, and converting the dynamical model of the system into a state model; designing a function approximate DNN architecture, approaching an unknown function in the system, and designing a weight updating law based on a first-order Taylor series to reduce the mathematical difficulty; in the backstepping process, a virtual controller and an actual controller are constructed by utilizing an optimization backstepping technology, and the overall control optimization of the system is realized; an event trigger function is designed, and consumption of system communication resources is reduced; and carrying out Lyapunov analysis to ensure that each signal of the system is bounded. The adaptive optimization backstepping controller based on the DNN architecture is designed for a fractional order single-machine infinite bus system, unknown nonlinear terms in the system can be compensated, and a single-machine power angle tracks a given reference signal.

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.

This invention relates to the field of voice coil motor servo control technology, specifically to a fractional-order high-order sliding mode control method for voice coil motors based on a third-order superspiral sliding mode observer (STSMO) and an adaptive reaching law. The method includes the following steps: S1, establishing a mathematical model of the voice coil motor, deriving the dynamic equations of the voice coil motor including parameter uncertainties and external disturbances, defining the position tracking error, and deriving the error dynamic equation; S2, designing a fractional-order high-order integral sliding surface, combining it with an improved Oustaloup filter to achieve rational function approximation of the fractional-order calculus operator, and constructing the first-order derivative equation of the sliding surface; S3, designing an adaptive hyperbolic tangent reaching law that integrates dynamic feedback of the error amplitude, and deriving the initial expression of the sliding mode control law based on the first-order derivative equation of the sliding surface and the adaptive hyperbolic tangent reaching law. This invention achieves real-time accurate observation and feedforward compensation of lumped disturbances through a third-order superspiral sliding mode observer (STSMO).

This invention provides a machine learning-based power equipment simulation and analysis system, comprising a data acquisition module, a data processing module, a power equipment simulation module, and a fault analysis module. By integrating electro-thermal coupling modeling, orthogonal basis order reduction analysis, and modular ROM assembly, a rapid simulation model is constructed, significantly improving the modeling efficiency and response speed of power equipment under complex operating conditions. The simulation module introduces a GL-MLP-Trans model to achieve intelligent prediction and stability control of the simulation scheme. The fault analysis module employs a low-rank-Bi-LSTM model, combined with a spectral projection gain mechanism and a sub-modulus function approximation projection strategy, reducing model complexity while improving fault identification accuracy. This system is applicable to smart grids, power operation and maintenance, and industrial equipment condition monitoring scenarios, possessing high efficiency, robustness, and engineering adaptability.

The invention discloses an urban road traffic state intelligent estimation method based on microphysical representation, and the method comprises the steps: constructing a road section boundary condition based on the arrival and departure accumulated flow information of an intersection and a gate, and forming multi-source traffic perception input through combining with road section observation data; constructing a physical-data hybrid driving model, constructing a deep learning model in a data driving branch, learning a mapping relation between road section boundary cumulative flow and a traffic state in a road section, and converting an indistinguishable Newell traffic flow model into a distinguishable computational graph structure through function approximation and structural conversion in a distinguishable physical branch; and constructing a loss function of the hybrid drive model, so that data drive output and a physical model are kept coordinated, and traffic flow parameters are jointly estimated. Through deep embedding of the microphysical calculation graph, the dependence of a pure physical model on an ideal assumed condition is made up, and meanwhile, the problem that a pure data driving method is insufficient in generalization ability in a sensing blind area is solved.

The invention provides a trigonometric function calculation circuit and calculation method based on piecewise quadratic function approximation, and electronic equipment. The calculation circuit comprises an angle mapping unit and a numerical calculation unit, the angle mapping unit is configured to receive an input angle, determine sign bits of a sine value and a cosine value according to a quadrant where the input angle is located, and map the input angle into a mutual complementary angle with the sum of two actual values being 90 degrees in a first quadrant; and the numerical calculation unit is configured to calculate a sine value and a cosine value based on mutual complement angles by adopting a piecewise quadratic function approximation mode, and output a final result in combination with the sign bits. According to the method, efficient calculation of the trigonometric function is achieved through angle mapping and piecewise quadratic function approximation, non-equal-length segmentation and special left end point optimization are adopted in a numerical calculation unit, and storage resources and calculation complexity are reduced. While the precision is guaranteed, hardware resources and calculation delay are remarkably reduced, and the method is suitable for application scenes with high requirements for real-time performance.

The invention discloses an unmanned aerial vehicle communication multi-priority queue modeling and performance analysis method, and the method comprises the steps: building an extensible state model through a WiMarkov chain and a hypercube transfer unit, and achieving the low-delay access of different priorities through a threshold and a window in combination with a weighted COS statistics and fixed backoff mechanism; semi-analytical expressions of the success rate and the time delay are obtained through steady-state distribution and generation function approximation, and parameters are learned through optimization constraints. Simulation shows that the low-load success rate is larger than or equal to 99%, the high-priority delay is still kept at the millisecond magnitude during high load, and the method is suitable for an unmanned aerial vehicle network communication system with the strict real-time requirement.

Methods and systems related to neural networks or other function approximators operate for training a neural network is provided, or another function approximator, for inferring a predetermined time of arrival of a predetermined transmitted signal on the basis of channel-impulse-responses, CIRs, of transmitted signals between a mobile antenna and a fixed antenna, the method having: obtaining a channel impulse response condition characteristic, CIRCC, descriptive of channel impulse responses of transmitted signals associated with mobile antenna positions within a reach of the fixed antenna; generating, by simulation, a training set of simulated CIRs which are associated with different times of arrival in one or more simulated scenes, and which fit to the CIRCC; training the neural network, or other function approximator, using the simulated CIRs and the different associated times of arrivals to obtain a parametrization of the neural network, or other function approximator, associated with the CIRCC.

The present invention belongs to the field of parameter adaptive control, and specifically discloses a method for adaptive optimization of laser welding parameters based on machine learning, the method comprising: configuring hardware equipment, predicting weld width, and optimizing laser welding parameters. This solution establishes a deep neural network as a weld width prediction model, establishes a Markov decision process and introduces a discount factor, adopts the SAC algorithm to maximize entropy regularization to reinforce the learning objective, utilizes a neural Q network as a function approximator, optimizes the Q function parameters by minimizing the Bellman residual, and optimizes the welding strategy parameters by defining a loss function using the KL divergence. The weld square error minimization formula is rewritten as a spatial discount form, with the weld length and laser position as variables, and an integral reward function is defined in combination with the instantaneous welding speed. The spatial discount mechanism reduces the impact of the cumulative error, thereby achieving spatial adaptive regulation of the welding speed.

This application provides a method and system for learning and imitating the three-dimensional trajectory of a robotic arm. During human-computer interaction between a patient's unaffected hand and the robot, a teaching trajectory is collected. The collected three-dimensional trajectory data is preprocessed and then input into a Dynamic Management Model (DMP) to obtain multiple sets of nonlinear terms. A Gaussian Mixture Model (GMM) is used to cluster these nonlinear terms, resulting in multiple Gaussian distribution models. Regression is then performed to determine the target nonlinear term. A Gaussian kernel function is used to approximate the target nonlinear term, and the weights of the Gaussian kernel function are calculated to obtain the learned nonlinear term. The learned nonlinear term is then substituted into the DMP model to solve for the trajectory, obtaining optimized teaching trajectory data. The patient can then train by imitating the optimized trajectory. This demonstration trajectory can preserve the patient's motion characteristics while reducing the impact of non-smooth and unrepresentative trajectories on trajectory imitation, resulting in a smooth imitation trajectory with good convergence at the start and end points.

The present application relates to the technical field of charging facility optimization, and particularly relates to a truck type mobile charging station online collaborative optimization scheduling method, a two-stage optimization scheduling model framework is constructed, the optimization scheduling model framework comprises: an offline training stage, a TMCS multi-period optimization decision model is established, and then a look-ahead rolling value function approximation algorithm (LRH-VFA) is established to iteratively learn from EV charging historical data, so that the influence of current period decision on future profit of the operator is considered; an online scheduling stage, based on the approximate value function obtained through offline training and short-time prediction and real-time information, TMCS online scheduling decision is updated rolling. The present application can fully consider the influence of EV charging demand uncertainty on TMCS scheduling results, effectively utilize the demand change adjustment decision updated dynamically, guarantee the quality of EV charging service, and improve the operator's income by coordinating TMCS to participate in power grid energy arbitrage.

The invention provides a timing estimation method and device, electronic equipment, a storage medium and a product. The method comprises the steps of obtaining a raised cosine filtering peak value, a peak value left-side correlation value and a peak value right-side correlation value; based on the peak value, the peak value left-side correlation value and the peak value right-side correlation value, determining a target loss function and a gradient value corresponding to the target loss function; iteratively decreasing and updating the gradient value based on a preset adjustment step length; and when the updated gradient value meets a target threshold value, determining a timing estimation result. Therefore, compared with an existing estimation method based on quadratic function approximate fitting, the method has the advantages that the target loss function is constructed by directly utilizing the peak value and related value information of the raised cosine filter, continuous optimization is carried out in a gradient descent iteration mode, and estimation can be carried out by fitting the actual characteristics of the impulse response function of the raised cosine filter better.

The invention belongs to the field of delay differential equation modeling, and particularly discloses a delay differential equation modeling method based on Bayesian optimization and a neural network, and the method comprises the steps: generating multiple pieces of trajectory data of a system; an optimal delay term is searched by adopting a Bayesian optimization algorithm, the Bayesian optimization takes a delay term to be solved as an optimization variable and an error based on neural network simulation as a target function, and the optimal delay term is obtained through iterative optimization of an agent model; constructing a state matrix and a delay matrix from the trajectory data based on the optimal delay term, taking the spliced matrix as neural network input, and constructing a neural network for approaching a nonlinear function; fusing a linear multi-step method into a loss function of the neural network, and training the neural network by using the trajectory data to obtain a nonlinear function approximation model; based on the model, a numerical discretization error and a neural network approximation error are separated, and a total error bound is constructed. According to the method, high-precision and high-efficiency delay differential equation modeling with quantization error guarantee can be realized.

The application discloses a kind of based on attention mechanism and reinforcement learning's ultra-short-term DNI prediction method, comprising: collecting solar thermal power station historical measured direct normal irradiance data and meteorological characteristic data;Periodic time characteristics and historical lag term are introduced to enhance time series dependence;Using bidirectional long short-term memory network extracts DNI time series features, constructs attention mechanism based on the dynamic change of cloud amount, and strengthens the response capability of model to weather mutation;Adopt KAN to replace traditional fully connected to high-dimensional nonlinear characteristics Adaptive function approximation;Introducing PPO reinforcement learning algorithm, dynamically optimizing model feature representation and parameters, realizing error self-feedback correction;Output DNI prediction result, for the dispatching and operation control of concentrating solar thermal power generation system.The method can effectively improve the DNI prediction accuracy and robustness under complex weather conditions, and provide high-precision prediction support for photo-thermal power generation and renewable energy grid connection.