133 results about "Approximation error" patented technology

The invention discloses a free-form surface three-axis ball-end cutter equal approximation error finish machining tool path generation method which comprises the following steps: firstly, importing a free-form surface model to be machined, and setting data such as the radius, the line spacing, the approximation error maximum allowable value and the precision of a ball-end cutter; secondly, a group of section planes are planned according to the row spacing to intersect with the curved surface, the intersecting line serves as a cutter contact curve, and an equal-bow-height-error cutter contact iterative search method for driving cutter contact adjustment through the geometric distance is provided for the cutter contact curve on the section planes; the maximum distance between a cutter cutting envelope surface and a cutter contact track line is used as an approximation error, and an adaptive discrete method is adopted to carry out approximation error calculation; and finally, the cutter location points of the equal-bow-height-error cutter contact points serve as initial values, an equal-error cutter location point calculation method of step length self-adaptive adjustment iteration is provided, approximation errors between the cutter location points are all within an allowable range, and therefore the free-form surface three-axis ball-end cutter equal approximation error finish machining cutter path is obtained.

The invention relates to the technical field of motion planning, and discloses an Airthoid curve corner fairing method based on strain energy optimization, which is used for fairing a corner formed by straight line segments in a tool path and taking a fairing curve as an actual machining path of a tool. Comprising the following steps: obtaining an approximate error constraint and a segment length constraint according to a given corner size, a corner edge length and an approximate error; determining a feasible region of a transition length and a deflection angle according to the approximate error constraint and the segment length constraint; selecting a parameter combination enabling the strain energy of the fairing curve to be minimum in the feasible region according to the strain energy of the fairing curve; calculating arc length and curvature extreme values of three pairs of Airthoid curves forming a fairing curve according to the parameter combination; the generated tool path has the advantages of being based on arc length parameter expression, controllable in contour error, and capable of achieving curvature extreme value and arc length analysis expression.

The invention discloses a transformer sound signal adaptive wavelet optimization and feature extraction method and system, and the method comprises the steps: calling a Pywt wavelet analysis library, decomposing an original signal according to a decomposition layer number J, and obtaining a multi-layer detail signal; for each layer of detail signals, the following steps are executed: introducing an M estimator to improve a noise variance calculation model, and calculating a standard deviation and a unified monitoring threshold value; constructing a dynamic threshold value based on a denoising signal approximation error minimization criterion; designing a correction factor; correcting the wavelet coefficient of each layer of detail signal; reconstructing a pure signal by using an inverse decomposition method; dividing the pure signal into a plurality of short-time signals; extracting an MFCC feature vector of each short-time signal; weighting and screening MFCC feature vectors by adopting a support vector machine recursive feature elimination method; and compressing the dimension of the feature vector in combination with a principal component analysis algorithm to generate a final feature matrix. According to the method, the problems of contradiction between noise suppression and signal fidelity and low recognition rate caused by high-dimensional feature redundancy in a traditional method are solved.

The invention relates to the field of nonlinear system control, and provides a preset time reinforcement learning method and system of a continuous nonlinear system, and an electronic device, and the method comprises the steps: constructing a zero-sum game framework based on a kinetic model and a performance index of the nonlinear system; determining a value function and a Hamiltonian function based on a zero-sum game framework; applying a preset neural network model to carry out approximation on the value function, and determining an approximation error; based on the Hamiltonian function and the approximation error, an approximate optimal control strategy and a worst interference strategy are determined; constructing a Lyapunov function based on the value function and the weight error of the value function; and based on a Lyapunov function, an approximate optimal control strategy and a worst interference strategy, a reinforcement learning result is verified. The method and the device are used for overcoming the defects that convergence time cannot be dynamically adjusted, parameter complexity is high and robustness is insufficient in the prior art, and the scheme of the invention can meet dual requirements of a continuous nonlinear system on dynamic convergence and anti-interference performance.

The invention relates to the technical field of motion planning, and particularly discloses a cutter shaft synchronous optimization method for a fairing track, which comprises the following steps of: inserting an Airthoid curve at a corner of a cutter tip as a cutter tip fairing curve corresponding to the corner of the cutter tip, and determining the transition length of the cutter tip fairing curve according to a cutter tip approximate error and a continuity condition; symmetrical Airthoid curves are inserted into the corner of the cutter shaft to serve as a cutter shaft fairing curve corresponding to the corner of the cutter shaft, and the transition length of the cutter shaft fairing curve is determined according to the approximate error of the cutter shaft and the continuity condition; obtaining preliminary interpolation data of the robot which only considers the tool nose constraint; optimizing redundant control vertexes of remaining linear segments of the linear path of the cutter shaft based on a least square method, and realizing optimization of a fairing curve of the cutter shaft; and finally, obtaining final interpolation data of the robot based on a convex optimization time optimal speed planning method. According to the invention, the processing efficiency is improved on the premise of ensuring that the fairing continuity is not damaged and the constraint of each driving shaft is not changed.

The invention relates to the technical field of motion planning, and particularly discloses a linear path fairing method based on Airthoid and an arc, and the method specifically comprises the steps: 1, determining a curvature radius extreme value of a fairing curve and a solution set of a first tangent angle according to approximate error constraint and non-selfing constraint; step 2, calculating a solution set of parameter combinations meeting tangential continuity, curvature continuity and position continuity constraints; step 3, calculating an intersection of the solution set 1 and the solution set 2 to obtain a solution set 3; judging whether the solution set 3 is null or not, if so, running the step 5; if not, the fourth step is executed; 4, calculating an optimal parameter combination in the solution set 3, and then running the step 6; 5, generating a fairing curve parameter combination not containing an arc according to the non-selfing constraint, and then running the step 6; and 6, analyzing and calculating the curvature extreme value and the arc length of the fairing curve. The method has the advantages of being based on arc length parameter expression, controllable in contour error, and capable of achieving curvature extreme value and arc length analysis expression.

The invention provides a finite time cooperative tracking control method based on an adaptive RBF neural network, and belongs to the technical field of networked multi-agent control strategies. Constructing a dynamic mathematical model of a high-order all-drive uncertain nonlinear multi-agent system comprising a virtual leader and a follower; according to a cooperative tracking control target and uncertain items in the system, constructing an error dynamic system comprising an adaptive RBF neural network estimation compensation item; designing a finite time integral sliding mode surface based on a high-order all-drive system theory; designing linearization parameters of a finite time integral sliding mode surface based on a pole assignment method according to system dynamic performance and finite time convergence requirements, and obtaining a finite time integral sliding mode controller; and designing a self-adaptive updating law of an upper bound of an RBF neural network weight and an approximate error, and substituting the network weight and the approximate error into a finite time integral sliding mode controller to realize cooperative tracking control of finite time.

The invention relates to the technical field of motion planning, and discloses a robot path fairing and synchronizing method based on an Airthoid curve, comprising the following steps: determining a tool nose fairing curve transition length according to a tool nose approximation error and a parameter synchronization strategy; determining a preliminary cutter shaft fairing curve; calculating the transition length of the cutter shaft fairing curve according to the preliminary cutter shaft fairing curve and a parameter synchronization strategy, and determining a final cutter shaft fairing curve; according to the maximum curvature of the final cutter shaft fairing curve and the maximum curvature of the cutter tip fairing curve, curvature parameter synchronization is achieved; and spline parameter synchronization is realized according to the B spline curve corresponding to the remaining straight line segment of the cutter shaft fairing curve and the cutter tip fairing curve. According to the method, the approximation error constraint of the cutter shaft and the cutter tip is realized by utilizing Airtoid curve analysis, and the calculation efficiency is high; parameter synchronization of the fairing curve and the remaining straight line segment is achieved through the remaining straight line segment B spline curve, and it is guaranteed that C3 at the connecting point of the curve segment and the straight line segment is continuous.

The invention discloses a self-adaptive cooperative control strategy based on a variable speed reaching law and a neural network, belongs to the field of intelligent control, and mainly aims at estimating and compensating uncertainty, external disturbance and actuator saturation existing in a queue in queue control and improving the stability, robustness and control performance of the whole queue. The method comprises the following steps: establishing a queue system model with uncertainty, external disturbance and actuator saturation; establishing a Gaussian error function approximation strategy; establishing a tracking error; establishing a variable speed reaching law; establishing a queue integral sliding mode error; establishing a queue coupling sliding mode error; establishing an adaptive parameter estimation mechanism for external disturbance and Gaussian function approximation errors in the queue; establishing a neural network estimation mechanism for uncertainty in the queue; and establishing a queue integral sliding mode control strategy based on a variable speed reaching law, a Gaussian error function approximation strategy, an adaptive parameter estimation mechanism and a neural network estimation mechanism. The method is used for queue cooperative intelligent control.

A label noise estimation method based on a manifold regularization transfer matrix provided by the present invention pre-trains a first network in a second network, and after distilling a data set, inputs the obtained sub-data set into the second network to obtain the probability of the class to which the data instances in the sub-data set belong and obtain a transfer matrix related to the data instances; further calculates the cross-entropy loss of the second network according to the data instance labels, and combines an association matrix expressing the consistency of the data instances belonging to the same manifold and a penalty matrix of the data instances belonging to different manifolds to calculate the loss function of the second network; adjusts the loss function to reduce the training of the second network to obtain a trained second network, thereby completing the estimation of the class to which the data instances belong. The present invention can reduce the estimation error without affecting the approximation error of the transfer matrix, and experiments prove that the present invention can achieve excellent performance in label noise learning.

The invention belongs to the technical field of transient electromagnetic three-dimensional forward modeling. According to the finite difference time domain modeling method and system for the rugged topography and the irregular anomalous body, modeling is carried out on a triangular mesh of a complex target body, intersection information of the triangular mesh and an FDTD mesh is generated through the improved parallel ray tracing technology, the equivalent conductivity of the intersection edge of the triangular mesh and a complex interface is calculated through the conformal mesh technology, and the finite difference time domain modeling method and system for the rugged topography and the irregular anomalous body are obtained. Stepped approximation errors caused by an existing FDTD method are effectively overcome, and fine description of a complex geological structure is achieved.

Methods and systems for performing an operation on at least one homomorphically encrypted ciphertext, the method include determining, by a computing device, a value that is an initial approximation of a result of the operation on the at least one homomorphically encrypted ciphertext; and iteratively improving, by the computing device, the value using a recurrence relation wherein a number of iterations is determined based on a predetermined accuracy to minimize an approximation error.

The embodiment of the invention discloses a harmonic compensation method for an angle error of a magnetic encoder, and the method comprises the steps: enabling the magnetic encoder to rotate at a constant speed in a setting mode, collecting sine and cosine signals outputted by the magnetic encoder, and carrying out the calculation through CORDIC, thereby obtaining an initial angle sequence containing a fundamental wave error and a harmonic wave error; extracting each harmonic component through an adaptive wave trap, constructing a harmonic compensation polynomial, and storing each coefficient; in an application mode, a magnetic encoder signal is collected in real time, a current initial angle value is solved, a harmonic compensation amount is calculated according to a current fundamental wave angular frequency and a stored coefficient, and an accurate angle value is obtained by subtracting the compensation amount from the initial angle value. According to the method, external reference signals and a large amount of storage space are not needed, each harmonic error is directly eliminated through a harmonic compensation polynomial, and approximate errors caused by traditional linear interpolation are avoided; and the compensation precision can be improved only by increasing polynomial terms, and the storage requirement is not obviously increased.

The invention discloses an adaptive coagulation nerve tracking control method for a nonlinear space-time system. The method comprises the following steps: establishing a mathematical model of the nonlinear space-time system and a tracking signal; constructing a coagulation neural network, approximating the nonlinear space-time uncertainty of an open-loop error system into a form of multiplying a coagulation weight by a network base vector, and representing bias input by time-varying disturbance and space-time approximation errors; and constructing a Lyapunov function, counteracting residual bias terms based on a sliding-mode-like smooth function, and designing a virtual control law, a parameter adaptive law and a controller on the basis of a backstepping method. The control scheme designed based on the method can realize effective tracking of any micro-traceable signal by a nonlinear space-time system, and ensures an adjustable bounded tracking error. Meanwhile, when adjustable parameters in the sliding mode function meet the square integrable condition, the tracking error is gradually converged to zero.

The invention discloses a CKKS fully homomorphic encryption scheme approximation error reduction method, and solves the problems of low efficiency and rapid increase of error scale in practical application in the prior art. The method comprises the following steps: generating a public parameter, a public key and a private key; generating a relinear key and a self-isomorphic key according to the public key and the private key; performing multi-layer coding on the received multiple message vectors to obtain a coded plaintext polynomial corresponding to each layer; performing encryption to obtain a ciphertext corresponding to each layer; operation is carried out according to the encryption requirement of a user and the ciphertext, and a ciphertext operation result corresponding to each layer is obtained; carrying out relinearization on the ciphertext operation result and scaling the ciphertext operation result to the next layer step by step to obtain scaled ciphertext corresponding to each layer; a multiplication ciphertext is obtained; decrypting and decoding to obtain a message vector; according to the method, the noise introduced by the ciphertext in the initial encryption process and the noise generated in the key switching process are reduced, and meanwhile, the rounding error introduced in the operation process is greatly reduced.

An information processing apparatus obtains measurement data representing measurement values measured after a quantum gate sequence including first and second quantum gates is executed a plurality of times, defines, in representing the first quantum gate as the product of a first matrix representing an ideal value of the first matrix and a matrix exponential of a second matrix representing an error, a variable representing the second matrix, generates a function that linearly approximates an effect of the error on the measurement values, by approximating a composite quantum gate that is a combination of the first and second quantum gates by the product of the first matrix, a third matrix representing an ideal value of the second quantum gate, and a matrix exponential of a transformation result of transforming a value of the variable using the third matrix, and estimates the error using the function and measurement data.

The invention discloses an air-space-ground Internet of Things performance analysis method and system based on SWIPT and NOMA, and belongs to the technical field of air-space-ground integrated Internet of Things. Comprising the following steps: constructing an air-space-ground Internet of Things system model comprising an LEO satellite, an unmanned aerial vehicle relay and a ground user, and considering heterogeneous channel characteristics and node dynamics; deducing a closed-form solution of the outage probability of the system through joint transformation of a moment generation function and a characteristic function of a random variable; a high-order Taylor expansion and moment matching method is adopted to carry out approximation on the closed-form solution, a first-order approximate solution and a second-order approximate solution are obtained, and an upper bound of an approximate error is deduced. According to the method, the problem of accurate modeling of the outage probability under heterogeneous link coupling in the air-space-ground Internet of Things is solved, theoretical preciseness and engineering practicability are both achieved, and an effective tool is provided for network reliability analysis and optimization.

The invention discloses a roadside multi-mode fusion sensing method. According to the method, an initial 3D view cone tensor with depth information is generated through a depth estimation network, features of pixel points are projected to a coordinate system in combination with internal and external parameter matrixes of a camera, then a three-dimensional space point coordinate set is generated according to projected feature position information, a Gaussian function is adopted to distribute weights of neighborhood grids, and a three-dimensional space point coordinate set is obtained. And finally, inputting the image features and the features into a fusion unit, respectively carrying out uncertainty modeling on the two types of features, automatically generating a fusion weight, and finally, carrying out normalization weighted aggregation on the features in the neighborhood so as to reduce the position approximation error and obtain the calibrated image features, and finally, inputting the image features and the features into the fusion unit, and respectively carrying out uncertainty modeling on the two types of features and automatically generating a fusion weight. And dynamic optimization is carried out according to a self-supervised loss function in the fusion process, so that the stability of multi-modal feature fusion is ensured, the detection accuracy is high, the performance is relatively good, and the problem of global feature dislocation caused by external parameter deviation and depth error can be avoided.

The invention discloses a method for calculating a five-axis finish machining tool path approximation error through a multi-task neural network. The method comprises the following steps: firstly, acquiring core parameters required by calculation of a neural network model, and mapping the parameters to the same scale by applying mean variance normalization; then, according to PCA (Principal Component Analysis) dimension reduction, screening is carried out on the data characteristics; regularization processing is added to optimize a network structure, and a part of neurons are hidden in each iteration process, so that interference of non-core knife contacts on approximation error value calculation is reduced, and model overfitting is avoided; an attention mechanism is introduced, feature weights are dynamically distributed, key features are highlighted, a higher weight ratio is given, the learning ability of the model is enhanced, and the training precision is improved; and finally, dynamically adjusting the feature sharing proportion among the tasks by using a cross connection method, ensuring that the unique features of each task are reserved while sharing information, reducing interference among the tasks, refining feature sharing among the tasks and enhancing the generalization ability of the model, thereby improving the calculation precision of the approximation error.

The invention discloses a five-axis numerical control finish machining tool path approximation error calculation method based on an Optuna optimization BiLSTM-TCRA (BiLSTM, Bidirectional Long Short Term Memory Network, a Bidirectional Long Short Term Memory Network, a Temporal-Channel Residual Attention, a Time-Channel Residual Attention mechanism) neural network, and relates to a tool path approximation error calculation method based on an Optuna optimization method of a BiLSTM-TCRA (BiLSTM, Bidirectional Long Short Term Memory Network, a Bidirectional Long Short Term Memory Network, a Time-Channel Residual Attention Mechanism) neural network and a tool path approximation error calculation method based on the BiLSTM-TCRA neural network. The method comprises the following steps: firstly, acquiring core parameters required by calculation of a neural network model, and mapping the parameters to the same scale by using mean variance normalization to eliminate the influence of dimensional difference between different features on the performance of the model; then, each hyper-parameter of the BiLSTM model is optimized by using an Optuna hyper-parameter optimization framework; a TCRA attention mechanism module is introduced to dynamically distribute different time step feature weights, so that the model can pay more attention to effective information which greatly influences an approximation error, the learning ability of the model is enhanced, and the training precision is improved; dropout mechanism sparsity is added to optimize a network structure, and part of neurons are randomly discarded in each iteration process, so that interference of non-core knife contacts on an approximation error value is reduced, and model overfitting is avoided; finally, the effectiveness of the method is verified in combination with actual curved surface model data, and efficient and accurate prediction of the five-axis machining approximation error is achieved.

Diffusion models are machine learning algorithms implemented as neural network-based denoisers that are uniquely trained to generate high-quality data from an input lower-quality data. However, for complex datasets, the samples generated by a diffusion model can still fail to reproduce the quality and diversity of the training data, due to approximation errors made by the finite-capacity network. Diffusion guidance addresses this issue by steering the sampling process away from a less desired model, toward a preferred one. However, current guidance methods rely on a single additional denoiser and manual tuning of guidance weights, which is suboptimal for large, complex models and leads to inefficiencies. The present disclosure employs a mixture of denoisers to guide a diffusion model, which can increase the expressiveness of the guidance and substantially improve sample quality and diversity in the output of the diffusion model.

This invention discloses a floating-point-fractional hybrid anchoring method and computational system for suppressing iteration errors, belonging to the field of high-precision numerical computation technology. It solves the technical problems of pseudo-chaos caused by the accumulation of iteration errors in pure floating-point calculations, the computational power explosion in pure high-precision fractional calculations, and the inability to simultaneously achieve both computational power and precision. This invention completes high-speed iterative computations of nonlinear systems using a general floating-point format. After each floating-point calculation, an optimal rational number approximation algorithm is used to convert the calculation result into a simplified rational number with a denominator not exceeding a preset threshold, eliminating the original errors introduced by floating-point truncation and rounding. The purified rational number is then converted back to floating-point format to participate in the next iteration, relying on a bounded rational number anchoring mechanism to progressively block the error amplification chain. The denominator threshold is set to 10¹²~10¹ based on the effective number of bits of the floating-point number. 5 This invention can adaptively adjust to various scenarios such as embedded terminals, supercomputing, and industrial control, and optimizes the continued fraction algorithm to achieve the globally optimal rational approximation under a given upper limit of the denominator. It requires no dedicated large number hardware and combines the advantages of high-speed, low-load floating-point operations with zero approximation error in rational numbers. It can be stably used in nonlinear computing scenarios that are sensitive to initial values ​​and require long-term stable iterations, such as chaotic simulation, vehicle trajectory prediction, industrial PID closed-loop control, 5G / 6G communication channel simulation, microscale meteorological simulation, and large-scale number theory zero-point verification. It effectively eliminates numerical trajectory drift, reduces equipment computing power consumption, and minimizes the workload of manual calibration and data screening, possessing extremely high engineering and academic application value.

The present invention provides a stable control method for the libration of an electrodynamic tethered satellite. Compared with existing control methods based on optimization theory, this control method has an analytical form, a small computational effort, and can handle the effects of control current saturation nonlinearity and unknown system disturbances. The method comprises: establishing orbital dynamics equations based on the six elements of the vernal equinox; establishing attitude dynamics equations based on in-plane and out-of-plane angles; obtaining control equations describing the libration of the tethered satellite through dimensionless transformation; designing an auxiliary dynamics system to compensate for the saturation nonlinearity of the control current; using a radial basis function neural network to approximate multiple disturbances, such as model perturbations and external interferences, and estimating the weight matrix and approximation error to compensate for the disturbances; designing an analytical control law for the current in the electrodynamic tether; correcting singular terms in the analytical control law using a dynamic scaling generalized inverse method; and controlling the libration process of the electrodynamic tethered satellite system using the analytical control law.

The invention belongs to the technical field of laser interference precision measurement, and provides a full geometric error displacement calculation method based on non-approximate simplification of a light beam track. All optical machine geometric errors can be completely calculated, the real refraction condition can be completely calculated, no approximate error is introduced in the simplification process, and the displacement calculation process conforms to the real light beam track condition; according to the method provided by the invention, all reflection phenomena in the interferometer are simplified, parameter integration is carried out on a reflecting surface and a receiving surface of a reflected light beam, an originally complicated multi-beam calculation process is optimized, the number of parameter items required for completely describing geometric errors is reduced, and the calculation efficiency is improved. And meanwhile, the light beams which do not influence the calculation of the total optical path difference are deleted, so that the calculation efficiency is high.

The invention discloses an uplink outage probability analysis method and system for an unmanned aerial vehicle cooperating with a wolfberry picking trolley, and the method comprises the steps: constructing a joint effect model in a non-static multi-input multi-output scene, and determining an unmanned aerial vehicle and wolfberry picking trolley uplink cooperative communication scheme for achieving the maximum channel gain; deducing to obtain an uplink outage probability closed expression containing transmitting power, interference statistical characteristics and mobility parameters by adopting an interference statistic calculation method based on Laplacian transformation; based on the obtained uplink outage probability closed expression, an improved Gaussian approximation method is adopted, and an uplink outage probability approximate expression is obtained through a dynamic truncation criterion; and based on the obtained uplink outage probability approximate expression, obtaining the total error upper bound of approximate errors by using Gaussian approximation errors and space average errors, and realizing efficient and accurate evaluation and guaranteeing the reliability of agricultural automation communication through dynamic interference modeling and improved Gaussian approximation.

The invention discloses a large model similar channel sharing weight lightening method and device, and belongs to the field of artificial intelligence. The method comprises the steps of performing K-Means clustering on a Q / K projector weight matrix of a self-attention layer of a Transform architecture large model according to columns, and generating a cluster representative vector and a cluster label list to replace an original weight matrix to complete compression; restoring the approximate weight matrix based on the representative vector and the cluster label, and constructing an error matrix of the original matrix and the approximate matrix; performing singular value decomposition on the error matrix and retaining a key singular value to obtain two low-dimensional matrixes; during reasoning, multiplying the two low-dimensional matrixes to obtain an approximate error matrix, and adding the approximate error matrix with the approximate weight matrix to obtain a final reasoning weight; the total storage size after compression is calculated through a unified formula, and accurate quantification of storage overhead is achieved. According to the method, the performance degradation under low-bit compression is effectively relieved, the resource demand of model deployment is reduced, and the method is adaptive to various mainstream large models and different-dimension weight matrixes.

The present disclosure relates to a network node and a user device, as well as to methods for execution on a network node, a user device or on integrated circuitry for determining of a common timing advance component for a non-terrestrial network. In particular, for a validity interval, coefficients of a polynomial are determined which are approximating a timing advance component between the network node and a satellite, so as to minimize an approximation error, wherein the approximation error is specified in a plurality of time instances of the validity interval relative to a reference time instance. The coefficients are provided from a network node to user device and used to approximate the timing advance.

The invention discloses a chromatin contact atlas-based topological correlation domain division method, which comprises the following steps of: acquiring and preprocessing chromatin contact atlas data, and constructing a normalized contact matrix; according to the normalized contact matrix, calculating single-segment Infomap entropy values of all possible division intervals; solving an Infomap entropy value of a minimum part in each state through dynamic programming recursion, and calculating a complete Infomap entropy value under each step number according to the Infomap entropy value of the minimum part in each state; selecting the step number corresponding to the minimum integral Infomap entropy value, and obtaining the optimal topological correlation structure domain division through backtracking; an approximation error of the partition structure is calculated to assess partition accuracy. According to the method, a machine learning model does not need to be trained, whole genome chromatin contact information can be fully utilized, optimal division of TAD is automatically achieved, the division accuracy and repeatability are improved, meanwhile, the calculation complexity and cost are reduced, and an efficient and reliable tool is provided for chromatin three-dimensional structure and gene regulation and control research.

The invention provides a parameterized capacitor extraction method and device, equipment and a storage medium. The method comprises the following steps: parameterizing layout data containing a two-dimensional capacitor structure; inputting the parameterized layout data into a capacitance extraction model to obtain a predicted parasitic capacitance parameter; and storing the predicted parasitic capacitance parameter in a parasitic capacitance library. According to the method, the change rule of the stray capacitance in the circuit can be accurately captured through the deep symbol learning model, and compared with a traditional physical modeling method, stray capacitance parameters can be more accurately extracted, and approximation errors in a traditional method are reduced. Capacitance extraction is carried out by using the deep learning model, so that a tedious physical model calculation process can be avoided, and the calculation efficiency of parasitic capacitance extraction is remarkably improved. Especially for a complex integrated circuit design, a capacitance extraction task can be completed in a short time.

The application discloses an underactuated unmanned ship route tracking control system based on extreme learning, which comprises an establishing module I, an establishing module II, an establishing module III, an identifying module and a control module. Based on the identified unmodeled dynamics, parameter disturbance, external environment disturbance and the rest dynamic unknown items, and the longitudinal sight line guidance law, a robust adaptive route tracking control method based on extreme learning is adopted, the output weight based on the single hidden layer feedforward network SLFN approximation and the corresponding estimated error adaptive rate are designed to approximate the unknown items and reduce the approximation error, the identification precision is improved, the longitudinal controller and the bow controller are designed to compensate for the unmodeled dynamics, parameter disturbance, external environment disturbance and the rest dynamic unknown items, so that the unmanned ship is asymptotically stable at the dynamics level, the tracking control of the unmanned ship sailing along the expected path is realized, the tracking precision is improved, and the global asymptotic stability of the whole closed loop system is improved.