160 results about "Nonlinear optimization problem" patented technology

The invention provides an overall architecture for optimal strategy design using both historical data and human expertise. The presently preferred architecture supports the tasks of strategy design and strategy analysis, and provides extensions to influence diagrams, translation of an influence diagram as a nonlinear optimization problem, and use of induction after optimization to derive decision rules.

The invention provides a multi-constraint locus planning method of a spatial free floating mechanical arm. The method can satisfy requirements for dynamic coupling, joint driving capability limitation, arresting opportunity and multiple optimizing indexes of a spatial robot. A driving joint motor realizes an autonomous arresting task to a non-cooperative object. The multi-constraint locus planning method comprises the steps of establishing a multi-member dynamic model of the spatial robot; establishing the locus of each joint of the mechanical arm based on a Bessel curve, converting constraint and optimizing index functions in a joint locus designing process to function of a Bezier curve control point, so that locus programming is finally represented by the Bezier curve control points as a nonlinear optimization problem of the optimizing parameter; converting the locus planning problem of the spatial robot to a multi-constraint and multi-object optimization problem, performing searching on the optimization problem by means of a nonlinear optimization method, obtaining an optimal solution, and finishing multi-constraint locus planning of the spatial free floating mechanical arm.

The invention discloses a combustion optimization control method for a boiler. The combustion optimization control method is characterized by comprising the following steps: sampling a combustion nonlinear system of the boiler to obtain input / output data at the current moment; training the real-time sampled input / output data by an online incremental learning fuzzy neural network, building an online incremental learning predicting model of the combustion nonlinear system of the boiler; performing a nonlinear prediction control algorithm on the online incremental learning predicting model for realizing the optimization and the control of the combustion process of the boiler. According to the combustion optimization control method for the power station boiler of the online incremental learning fuzzy neural network, the nonlinear optimization problem in the predication control algorithm is solved by utilizing a particle swarm optimization algorithm through the online identification of the boiler combustion optimization model; the real-time optimization and control of the boiler combustion process are realized.

The invention discloses a probe position linearization calibration method for on-machine laser measurement. The method comprises steps that, a measurement model of a laser probe moving along with a machine tool is established, a machine tool motion program is generated in an offline mode, the laser probe is driven by the machine tool to carry out multi-angle scanning of a standard ball for ball center fitting, a linear equation group of a standard ball center under multiple machine tool rotation angles and a probe mounting position relationship is acquired, probe mounting position parameters can be acquired through solving the equation group, that a calibration problem is expressed as a nonlinear optimization problem with constraint is not required, and problems of massive calculation and instability existing in nonlinear optimization solution can be avoided.

The invention discloses a day-ahead economic scheduling method for an electric-pneumatic coupled system based on mixed integer second-order cone programming. A function whose all-day energy and spareall-in cost are minimum is used as a target function, and constraint conditions of a power system and a natural gas system, and electric-pneumatic coupling and conversion relations are considered, toestablish an optimization model of electric-pneumatic coupled system day-ahead economic scheduling. On the basis of the model, through second-order cone relaxation of a steady-state airflow equation of a gas pipeline, a mixed integer second-order cone programming model of day-ahead economic scheduling of the electric-pneumatic coupled system is established, and an initial operation scheme is solved and obtained. Aimed at a problem that an initial scheduling result of a natural gas system is not accurate, a calibration model based on a steady-state power flow calculation method of a natural gassystem is established, so as to modify an initial operation scheme. The method realizes accurate solution of a non-convex nonlinear optimization problem, and provides a feasible, economical and accurate optimal operation scheme for electric-pneumatic coupled system day-ahead scheduling.

The invention discloses a real-time tracking method of a multi-channel kernelized correlation filter. The method includes the following steps: a training step of conducting ridge regression on a previous frame of object information to acquire a filtering template; a detection step of detecting the current frame of image with the acquired filtering template and outputting a filtering response; an updating step of real-time updating the filtering template and the appearance of an object. According to the invention, the method uses kernel function to fuse multi-channel characteristics, overcomes the selection limit of the multi-channel characteristics, and transforms the problem of linear optimization of ridge regression to the problem of non-linear optimization of a higher space through the kernel function, such that the filtering template with excellent robustness is constructed, the speed of a tracking machine is greatly increased, and tracking requirements of real world can be met.

The invention relates to a track profile irregularity amplitude estimation method employing optimal belief rules based inference. According to the method, the mapping relationship between parametric variable input and yield output is modeled by a belief rule base. A corresponding change relationship between vibration frequency domain characteristic data of different measurement points and a track profile irregularity amplitude is described by building the belief rule base. By a sequence linear programming method, an initial belief rules based (BRB) model is optimized through limited historical data; and the effects on the model caused by subjective factors are reduced. According to the sequence linear programming (SLP) method, a nonlinear optimization problem of an original model is converted into a step-by-step linear optimization problem; and various parameters of the optimization model can be relatively simply and rapidly calculated, so that the track profile irregularity amplitude can be accurately and rapidly estimated through belief inference under the condition of given vibration frequency domain characteristic. According to the track profile irregularity amplitude estimation method, the estimation accuracy and the calculation efficiency of the model are improved; and the method has the advantage of being relatively efficient on a track profile irregularity system which needs to be monitored in real time.

A predictive-control approach allows an electricity provider to monitor and proactively manage peak and off-peak residential intra-day electricity usage in an emerging smart energy grid using time-dependent dynamic pricing incentives. The daily load is modeled as time-shifted, but cost-differentiated and substitutable, copies of the continuously-consumed electricity resource, and a consumer-choice prediction model is constructed to forecast the corresponding intra-day shares of total daily load according to this model. This is embedded within an optimization framework for managing the daily electricity usage. A series of transformations are employed, including the reformulation-linearization technique (RLT) to obtain a Mixed-Integer Programming (MIP) model representation of the resulting nonlinear optimization problem. In addition, various regulatory and pricing constraints are incorporated in conjunction with the specified profit and capacity utilization objectives.

An optimization and management system using a global linearization approach and mixed integer linear programming to perform the dispatch and to schedule a river system for ten days forward while subject to real world, hourly physical, biological, environmental, and recreational constraints. The optimizer system uses a combination of slack variables and stitching methods to approximate a highly nonlinear optimization problem, thereby generating realtime generation schedules to promote to the management authority.

The invention provides an image reconstruction method based on multi-viewing-angle multi-spectral tomography. The image reconstruction method comprises the steps of: utilizing a plurality of pieces of absorption spectrum information corresponding to gas molecules so that laser which can cover an absorption spectrum range passes through a measured gas region from a plurality of viewing angles, and acquiring corresponding projection measurement data; constructing an equation set for solving measured gas parameter values according to a coefficient matrix and projection measurement data; converting a problem of solving a non-homogeneous linear equation set into a problem of solving nonlinear optimization; and solving the nonlinear optimization problem by adopting an iteration method to obtain parameter distribution, and further acquiring a gas parameter distribution image. Compared with a single-spectrum imaging system, the image reconstruction method has the effects of being capable of significantly improving the precision of parameter distribution image reconstruction, being high in resolution and good in reliability, and having wide application prospect.

The invention discloses a time optimal rapid three-dimensional obstacle avoidance path planning method, particularly relates to an unmanned aerial vehicle obstacle avoidance path planning method, andbelongs to the field of unmanned aerial vehicle path planning. According to the invention, the method comprises the steps: establishing an unmanned optimal control model which takes the acceleration in each direction as a control variable and contains the flight time sum through considering the maximum acceleration and obstacle constraint conditions; loosening an original non-convex nonlinear optimization problem into a second-order cone programming problem; and finally, solving a series of second-order cone programming problems through iteration to obtain a solution of an original problem andobtain an optimal change strategy in the speed direction. In other words, the obstacle avoidance path planning with the optimal time is realized by coordinating the flight time and the flight speed direction, the online planning of the obstacle avoidance path and the related control quantity can be realized, and the task response capability of the unmanned aerial vehicle can be further improved through the optimized flight path with the optimal time.

The invention discloses a coordinated planning method for energy storage, distributed power supply and distribution network based on Benders decoupling. On the basis of establishing a coordinated planning model of distribution network considering energy storage and distributed power supply, according to the idea of ​​Benders decoupling, Decompose the model into the main problem and sub-problems, and decompose the sub-problems again to form decomposition sub-problems and relaxation sub-problems, solve the decomposed sub-problems to obtain the values ​​of the continuous variables of the sub-problems, and solve the relaxed sub-problems to obtain the values ​​of the discrete variables of the sub-problems value, and form an effective cut set to feed back to the main problem. After iterative solution, the overall optimal solution that takes into account both the main problem and the sub-problem is obtained. The invention decomposes and re-decomposes the mixed integer nonlinear optimization problem containing a large number of integer variables and continuous variables, reduces the scale of the problem, and solves the difficult problem of large-scale planning considering multi-stages and multi-variables in distribution network planning. At the same time, coordinated planning is carried out for the optimal configuration of energy storage and distributed power sources and the reconstruction of the grid structure to achieve efficient solutions.

The invention relates to a kinematic calibration method for a Stewart parallel robot. According to the kinematic calibration method for the Stewart parallel robot, model simplification is achieved according to a coordinate system establishment rule to obtain a minimum to-be-identified parameter set, and then a nonlinear kinematic model of the parallel robot is obtained; any fixed measurement equipment and any three fixed target point are adopted, and the posture of a robot fixing platform relative to a measurement coordinate system, the positions of the target points relative to a movable robot platform coordinate system, and the like are used as to-be-identified parameters, so that a simple measurement process is achieved, and operation is easy; the nonlinear kinematic model of the parallel robot is combined with a measurement process model to obtain a nonlinear identification model of a whole calibration system; and the resolution of a nonlinear equation system is converted into theresolution of a nonlinear optimization problem. By the adoption of the kinematic calibration method for the Stewart parallel robot, model establishment is achieved through the complete minimum parameter set, model calibration is easier, and true parameters of the robot are directly resolved on the basis of a nonlinear optimization idea; and the kinematic calibration method has the advantages thatoperation is easy, user interfaces are friendly, and the practicability is high.

The invention provides a cramer-rao bound based waveform optimizing method for an MIMO radar in a clutter background, and belongs to the field of signal handling. The method comprises the steps of deducing CRB with parameters to be estimated according to a built signal model in the clutter environment; describing the optimal problem according to CRB based related rules. According to the method, a novel optimizing method based on diagonal loading is proposed to solve the obtained high non-linear optimization problem; the nonlinear optimization problem is converted into semi-define programming problem, so that the waveform covariance matrix can be efficiently optimized to achieve the minimized cramer-rao bound, and as a result, the system parameter estimation performances can be improved; compared with non-related waveform method, the method has the advantage that the parameter estimation performance for the MIMO radar system is obviously improved.

The invention discloses a city bus departure timetable optimization method. Passenger flow demand and traffic conditions along a bus line are extracted according to historical card swiping data acquired by a bus on-board AFC card swiping system, city bus departure timetable optimization is abstracted into a problem of nonlinear optimization based on the passenger flow demand and traffic conditions, and a city bus timetable is optimized by adopting a genetic algorithm. The passenger flow demand and traffic conditions along a bus line can be extracted only according to the card swiping data of the bus on-board AFC card swiping system, and a city bus timetable is optimized by adopting the genetic algorithm. Therefore, manual participation is reduced greatly, the waste of manpower and time is reduced effectively, and the operation efficiency of buses and the level of service can be improved.

An optimization and management system using a global linearization approach and mixed integer linear programming to perform the dispatch and to schedule a river system for ten days forward while subject to real world, hourly physical, biological, environmental, and recreational constraints. The optimizer system uses a combination of slack variables and stitching methods to approximate a highly nonlinear optimization problem, thereby generating realtime generation schedules to promote to the management authority.

The invention discloses a MIMO radar waveform optimization method, used to improve parameter estimation performance of a MIMO radar system under a condition that target prior information exists in a clutter scene. Implement method thereof comprises: firstly, establishing a MIMO radar signal model, and deriving Cramer-Rao bound (CRB) of an unknown parameter based on the model; based on a Trace-Opt criterion, minimizing trace of the CRB, establishing a waveform optimization model; and based on a diagonal loading (DL) method, relaxing a nonlinear optimization problem to a semi-definite programming (SDP) problem, to obtain efficient solution of the optimization problem, so as to improve parameter estimation performance of the system. Compared with an uncorrelated transmitted waveform and a non-prior scene, the method can obviously improve system parameter estimation performance.

The invention discloses a monocular visual mileometer positioning method. The method comprises the following steps that an input image of a present frame is read; a parallax based method is used to initialize a visual mileometer, a subsequent motion estimation step is started after success of initialization; a nonlinear optimization problem by taking pose as an optimization variable is formed fora last frame on the basis of gray scale invariance, and an initial inter-frame pose is obtained; a local map is constructed on the basis of the initial pose, and a density based tracking strategy is used to complete projective point characteristic matching and sub-pixel position optimization of the local map; and a more accurate constraint relation is obtained by tracking of the local map, the relation further optimizes the pose and map points, and a final positioning result is output. The monocular visual mileometer positioning method can reach higher positioning precision, and can reach therobustness and instantaneity needed by unmanned aerial vehicle navigation applications.

The invention belongs to the signal processing field, and relates to an MIMO-OFDM (Orthogonal Frequency Division Multiplexing) radar waveform design method for improving STAP detection performance. Under the constraint of a constant modulus, and based on an output SINR maximization criterion, the invention introduces the optimization problem of maximizing detection performance through designing emission waveforms. In order to solve a complex nonlinear optimization problem, the invention converts the nonlinear optimization problem into a semi-definite programming problem for obtaining an efficient solution based on a DL method, so as to maximize an output SINR, and thereby maximize system detection performance. The simulation result shows that compared with non-relevant emission waveforms, the emission waveforms obtained by the method can substantially increase an output SINR, thereby improving system detection performance. The method can obtain obviously improved detection performance compared with utilizing non-relevant waveforms.

The invention discloses a laser monocular vision fusion positioning mapping method in a dynamic scene, and the method comprises the steps: 1, detecting dynamic obstacle information according to point cloud data of a current frame and pose priori information predicted by a visual odometer of a previous frame; 2, forming an image mask on monocular vision 2 with mutually calibrated external parameters, extracting ORB feature points on the image mask and matching the ORB feature points with ORB feature points of a previous frame, estimating the depth of the ORB feature points, obtaining a relative pose through pose calculation, and outputting key frame information meeting requirements; 3, inserting the key frame into the common view, updating the connection relation between the key frame and other key frames, the growth tree and the bag-of-word model according to the common view degree of map points, generating new map points, finding the adjacent key frame according to the essential map of the current key frame, constructing a nonlinear optimization problem, and optimizing the pose and the map points of the key frame; 4, judging whether the similarity between the image data of each key frame and the image data of the current key frame reaches a threshold value or not, if yes, judging that loopback occurs, replacing or filling map points where conflicts exist between the current key frame and the loopback key frame, then connecting the current key frame and the loopback key frame on the essential map, updating the essential map, and finally, carrying out global BA to obtain optimized key frame poses, feature point maps and point cloud maps. According to the invention, the SLAM purpose can be executed in a dynamic scene.

The invention discloses a phased array beam dwell scheduling method based on a scheduling benefit and a genetic algorithm, belongs to the field of phased array radar resource management, and particularly relates to a method for implementing adaptive beam dwell scheduling by utilizing a task priority and a scheduling strategy. According to the invention, firstly, on the basis of a target threat model, target attribute information is expressed with a threat degree function; secondly, a two-dimensional priority list thought is utilized, a two-dimensional priority list is utilized for twice to integrate three parameters of a working mode priority, a deadline and a threat degree into a comprehensive priority in a nonlinear weighting mode, and uniqueness of the comprehensive priority is ensured;then the comprehensive priority is combined with a validity function of task scheduling to form a new scheduling benefit and accordingly, a scheduling optimization model is established, wherein a pulse interleaving technology and time and energy constraint conditions are considered; and finally, such nonlinear optimization problem is solved by the genetic algorithm. According to the phased arraybeam dwell scheduling method based on the new scheduling benefit, which is disclosed by the invention, a loss rate of a task can be effectively reduced, a hit value ratio and a correction value rate are improved, and meanwhile, an execution threat rate and a time utilization rate are also promoted.

The invention belongs to the field of signal processing and relates to a MIMO-OFDM-STAP steady waveform design method based on partial clutter priori knowledge.By introducing parameter uncertainty into the waveform optimization problem explicitly, sensitivity is relieved.The method comprises the steps of 1, building a MIMO-OFDM-STAP model to obtain an expression of received data; 2, obtaining optimized output SINR through objective function deduction; 3, establishing a steady waveform optimization model under the restraint of a constant modulus and total transmitting power; 4, bringing forward an iterative algorithm, and converting each non-linear optimization problem in iteration into a semi-definite programming problem capable of being efficiently solved based on the diagonal loading (DL) technique, so as to achieve optimal design of a steady waveform.

The invention provides a high-precision wireless sensor network positioning algorithm based on nonlinearity optimization. The method comprises the following steps of: (1) enabling unknown nodes to obtain minimum hops of anchor nodes by a flooding way; (2) approximating a distance ratio of the unknown nodes to different anchor nodes by using the minimum hops ratio, and abstracting three cases in which no intersection point exists, only one intersection point exists and more than one intersection point exists when equations are established as uniform nonlinearity optimization positioning molds; and (3) solving nonlinearity optimization problems, determining positions of the unknown nodes and processing different cases according to the solutions of the nonlinearity optimization molds in the step (2): a) using a unique solution as a coordinate of the unknown nodes when the unique solution exists; and b) using centroids of a plurality of solutions as the coordinates of the unknown nodes when the plurality of solutions exist. Based on the nonlinearity optimization, the wireless sensor network positioning algorithm has higher positioning precision; and the positioning precision is further improved by reasonably deploying the anchor nodes.

The invention discloses a method and a device for assessing the machining accuracy of ultra-large type curved surface parts. The method comprises the steps of performing data preprocessing to obtain relatively-evenly sampled data sets on the curved surfaces of the parts, initially positioning the sampled data sets according to the transformation relation between a measurement coordinate system and a theoretical coordinate system of a theoretical curved surface model so as to obtain initial iteration vectors roughly matched with the curved surfaces; utilizing the sampled data sets and the initial iteration vectors to solve the nonlinear optimization problem and obtain optimal coordinate transformation enabling the sampled data sets and the theoretical curved surface model to be matched; performing error analysis based on the sampled data sets and the optimal coordinate transformation and analyzing the machining accuracy of the ultra-large type curved surface parts. By adopting the method and the device for assessing the machining accuracy of the ultra-large type curved surface parts, assessment of the machining accuracy of the ultra-large type curved surface parts including steel safety casings of nuclear power plants and the like can be quickly, efficiently and accurately achieved.

The invention discloses an improved binary particle swarm optimization algorithm-based power distribution network reconfiguration method. According to the method, load equalization is adopted as an objective; a power distribution network reconfiguration problem is expressed as a nonlinear optimization problem with minimizing a load equalization index as an objective function; the topological structure model of a power distribution network is simplified according to the structure features of the open-loop operation of the power distribution network; a binary particle swarm optimization algorithm is improved; and therefore, the radial structure of the power distribution network can be ensured, at the same time, the number of iterations is greatly reduced. With the method of the invention adopted, a power distribution network reconfiguration problem in load equalization can be effectively solved. The method has fast calculation speed and excellent convergence.

The invention discloses a key parameter robust identification method in a lead acid battery model empirical formula. The method comprises: first of all, carrying out a plurality of charging experiments on a lead acid battery, and measuring the end voltage, the charging current and the SOC value of the battery during a charging process; establishing a nonlinear optimization problem of robust parameter identification by taking the data obtained from recording in the experiments; solving the problem by using Newton's method of nucleus width adjusting, and obtaining a local optimal solution (usually a global optimal solution ) of the optimization problem, after the optimal solution is obtained, obtaining the key parameters in a model, and accordingly obtaining the empirical formula of the lead acid battery model. The key parameters in the lead acid model empirical formula are obtained by solving the optimization problem which is based on minimum information entropy loss, and poor measurements can be automatically eliminated in the solving process, so that a result with higher robustness can be obtained, the efficiency and precision are also quite high, and the availability of the method is high.

A protein conformation optimization method based on simple space abstract convexity lower bound estimation comprises the following steps that according to a coarsness energy model, a Rosetta Score 3 is adopted as an optimized objective function, and an energy calculation model is converted into a dihedral angle optimized space energy model; through feature vector extraction, a high-dimensional dihedral angle optimization problem is converted into an actually operable Descartes space optimization problem; based on Karmarker projective transformation, a Descartes space energy model is converted into a nonlinear optimization problem constrained by unit simplex, and an abstract convexity lower bound supporting face is constructed in this way, and is updated; fragment assembly and a Monte Carlo algorithm are combined to obtain a series of metastable state conformation; finally, high-resolution protein conformation is obtained through a Refinement service provided by a Rosetta sever. The method is high in sampling efficiency, low in complexity and high in prediction precision.

The invention belongs to the technical field of passive detection, and relates to a high-position passive time difference positioning method based on motion multistation. The method comprises the steps: building a time difference positioning model, and respectively determining the coordinate position of a main station and the coordinate positions of auxiliary stations; solving the time difference according to a formula that a distance difference is the product of light velocity and the time difference: d<i1>=c*tau<i1>, wherein i=2, 3, ...,M; forming a time difference observation matrix through the coordinate position of the main station, the coordinate positions of all auxiliary stations and the solved time difference, wherein the impact on the positioning precision is considered, and the noise follows the Gaussian distribution (mean value = 0, and variance is sigma2). According to the invention, an intelligent search strategy in a CS algorithm is introduced to solve a nonlinear optimization problem in a time difference positioning system, thereby solving the problems that the calculation burden of the time difference observation matrix is large and the calculation complexity is high. The method can achieve the high-precision estimation of the position of a radiation source, can quickly approach to a global optimal solution without depending on a priori condition of the initial target position, and is not liable to cause the positioning fuzziness and a no-solution condition.