550results about How to "Improve convergence accuracy" patented technology

The present invention provides an order sequencing optimization method for logistics. The process of the method includes the following steps that: since the same type of commodities may be stored in a plurality of different containers, containers from which corresponding commodities in orders should be taken out are determined according to the expiration dates of the commodities in the orders, the number of remaining commodities in the containers where the commodities are located or the distances of the containers before order sequencing optimization is performed; and the orders of the commodities of which the containers are determined are optimally sequenced. According to the order sequencing optimization method for logistics of the invention, an evolutionary computation optimization method is adopted to optimally sequence the orders, so that the orders of the same commodities can be arranged at adjacent positions as much as possible, and therefore, commodities which have taken out can be repeatedly utilized, and the problem of low efficiency caused by frequent transport of the commodities can be avoided; and a constructed learning group concept is utilized to expand the search range of the algorithm, so that optimization calculation can be prevented from being trapped in local optimum, and convergence precision is greatly increased. With the order sequencing optimization method for logistics of the invention adopted, the optimal commodity picking efficiency of the commodities in the orders in a logistics process can be realized.

The invention provides a method for planning the routes of multi-unmanned aerial vehicles based on a particle swarm optimization algorithm. The method comprises the following steps: establishing a three-dimensional map for planning space of the routes of the multi-unmanned aerial vehicles at first; then constructing multi-unmanned aerial vehicle route planning models under the three-dimensional map, wherein the models mainly comprise a barrier model, a route model, an unmanned aerial vehicle state model, a constraint model and a multi-unmanned aerial vehicle route planning mathematic model; and solving the problem of multi-unmanned aerial vehicle route planning under the three-dimensional map by using the particle swarm optimization algorithm. The method provided by the invention improves multi-unmanned aerial vehicle route planning capacity in a complex environment and provides technical support for air traffic management platforms for unmanned aerial vehicles, autonomous flight systems for multi-unmanned aerial vehicles, etc.

The invention discloses a thunder and lightning approach forecasting method based on a particle swarm support vector machine, relates to the technical field of thunder and lightning forecasting and aims at applying a particle swarm support vector machine method in the thunder and lightning approach forecasting. The method comprises the following steps: carrying out relevance analysis and selecting related factors which influence the occurrence of thunder and lightning from the overhead and ground historical information of an MICAPS (Meteorological Information Comprehensive Analysis and Processing System) and the actual thunder and lightning data of a ground station; preprocessing the data and reasonably interpolating missing data aiming at the characteristics that data which prove whether thunder and lightning occur or not in the thunder and lightning data are imbalanced; optimizing the parameters of the support vector machine by a particle swarm optimizing algorithm; establishing a training sample set, training the support vector machine and establishing a thunder and lightning approach forecasting model; inputting a test data set into the trained forecasting model, so as to judge whether thunder and lightning occur or not. The method has the advantages of high precision and strong generalization capability.

The invention provides a self-adaptive genetic particle swarm hybrid algorithm optimization method. The self-adaptive genetic particle swarm hybrid algorithm optimization method includes: calculatingthe density and the radius of a center region of a parent population in a genetic algorithm, and distinguishing whether the parent population is in the overall centralized distribution, the local centralized distribution or the uniform distribution; performing a selection operation of the genetic algorithm, and selecting a parent individual to be evolved; establishing computational formulas of thecrossover probability and the mutation probability according to the three distributions of the parent population; performing crossover and mutation operations according to the established crossover and mutation probability formulas so as to achieve chromosome recombination and gene mutation, and forming an offspring individual; selecting a part of individuals with high fitness from a part of offspring individuals to perform the particle swarm algorithm to form offspring particles, and combining the offspring individuals and the offspring particles into an offspring population and saving the optimal individual thereof. The invention adaptively adjusts crossover probability mutation probability parameter values in the genetic particle swarm hybrid algorithm, so that the convergence speed and the convergence precision are greatly improved.

The invention provides a method for performing polarization state tracking and balancing on a received signal in a coherent optical communication system. The method is based on linear Kalman filtering, and comprises the following steps: performing depolarization on an electrical signal input into a filter according to a state vector predicted value to obtain a Kalman measurement predicated value; finding a point which is closest to the measurement predicated value on a circle formed by the rotation of an ideal constellation point for serving as a Kalman practical measured value; subtracting the measurement predicated value from the practical measured value to obtain a measurement allowance, and inputting the measurement allowance into a Kalman updating process; and putting an updated state vector into a next iteration. A highest polarization state rotating speed which can be tracked is about 100 times those of a constant modulus algorithm and a multi-modulus algorithm; the depolarization cost is lowered; the calculation complexity is low; and the calculation amount is small. Moreover, the method is suitable for phase shift keying (PSK) and quadrature amplitude modulation (QAM) polarization multiplexed signals of each order.

The invention provides a ship track active-disturbance-rejection control method based on the slip form of a multi-modal nonsingular terminal. The method includes the steps of constructing an expectedship heading equation according to navigational parameters of a ship, deducing a discretization nonlinear structural model of a two-order differentiator, processing expected ship heading signals in the expected ship heading equation to obtain expected ship heading differential signals required for controlling the rudder angle by the control law, obtaining the output rudder angle on the basis of the switchable error feedback control law, constructing an expanded state observer in combination with the error feedback control law, tracking the output deviation according to the expanded state observer, estimating the heading angle and inner and outer disturbance errors controlled by the control law, and feeding the heading angle and the inner and outer disturbance errors back into the control law. By improving the expanded state observer, a linear function and a nonlinear function can be switched in real time; the switchable segmented slip form face is designed; through the segmented slip form face, the switchable error feedback control law is designed, and the under-actuated ship track control is well realized.

The invention discloses a photovoltaic power station power generation amount predication method based on an adaptive mutation particle swarm and a BP network. The method comprises the steps of acquiring and preprocessing a data sample, then constructing a BP neural network structure, optimizing the network by means of an adaptive mutation particle swarm algorithm, and introducing an optimal individual which is obtained after optimization into the BP neural network for predication. According to the method of the invention, the adaptive mutation particle swarm algorithm and the BP neural network are combined for performing real-time power generation amount predication on the photovoltaic power generation system; namely power generation state at next time period is predicated through power generation amount information of a former time period. According to the method of the invention, a relatively high global convergence capability of the adaptive mutation particle swarm algorithm is used for optimizing the initial weight and threshold of the network, thereby improving convergence speed and precision in power generation amount predication.

The invention discloses an improved fuzzy neural network bus intelligent scheduling method based on a chaos theory, and belongs to the field of intelligent transportation. According to the improved particle swarm bus intelligent scheduling method based on the chaos theory, advantages and complementarity of various algorithms are fully utilized, a series of improvement measures are also introduced, such as conjugate gradient optimization, and inertia factor and constraint factor of the particle swarm algorithm etc., the mechanism and the search performance are researched from the theoretical and practical perspectives, problems of poor global search capability and premature convergence of the conventional optimization algorithm are fundamentally solved, the diversity of population can be obviously increased, the global search capability is obviously improved, the problem of fuzzy information can be effectively dealt with, the convergence speed is fast, and a new high-efficiency method is provided for bus intelligent scheduling.

The invention discloses a robot scene self-adaptive pose estimation method based on RGB-D camera. scene two-dimensional color image information of adjacent frames and space depth information corresponding to pixels of the two-dimensional color image are acquired based on a RGB-D camera; when the two-dimensional color image feature points are sufficient, ORB operators are adopted to extract features, the matching strategy provided by the invention is adopted to carry out accurate matching, and three-dimensional pose estimation is solved based on the pose estimation algorithm of the matching feature points; when the feature points are insufficient, the improved ICP algorithm provided by the invention is adopted to solve the three-dimensional pose estimation; then, a complete switching criterion is designed to fuse the two pose estimation methods; finally, pose estimation obtained through the two methods is optimized through a light beam adjustment algorithm, and finally smooth and accurate three-dimensional pose estimation is obtained. The three-dimensional pose estimation algorithm has the outstanding advantages of high robustness, high precision, small calculation amount, adaptability to different scenes and the like.

The invention discloses a method for cloud manufacturing resource optimization configuration based on an improved whale algorithm, and the method comprises the steps: building a problem model, and defining a fitness function; setting improved whale algorithm parameters, and generating an initial population; Calculating fitness values of all individuals in the population, obtaining a current optimal resource allocation scheme and converting the current optimal resource allocation scheme into whale individual position vectors; Introducing a parameter p, and judging whether p is less than or equal to 0.5; If not, performing spiral motion iteration updating to complete population updating; If yes, whether the value A (1) of the coefficient vector of the improved whale algorithm is met or not is judged; If yes, performing shrinkage encircling iteration updating; If not, performing random search predation iteration updating; Obtaining a current optimal resource configuration scheme; Adding 1to the number of iterations, and judging whether the current number of iterations is smaller than the maximum number of iterations; If yes, repeating the operation; And if not, outputting the currentoptimal resource configuration scheme. The whale algorithm is improved, so that the algorithm convergence speed is higher, the optimal solution is easier to achieve, and a new method is provided forsolving the problem of resource allocation.

The invention relates to a multi-single-arm manipulator output consistence active disturbance rejection controller structure with pre-defined performance and a design method thereof, in particular toa multi-single-arm manipulator output consistence active disturbance rejection controller, and belongs to the technical field of industrial process control. The active disturbance rejection technologyand the inversion technique are used for designing the multi-single-arm manipulator output consistence active disturbance rejection controller, the characteristic that extension state observers do not rely on a system model is utilized, and the influence of disturbance can be estimated and compensated in real time, so that the designed controller has the anti-disturbing property; tracking differentiators are designed to simplify the phenomenon of complex signal derivation of "explosion"; a manipulator system is restrained by a driving torque, the input saturation characteristic is considered,and the amplitude limiting problem of the driving torque is solved by an auxiliary system; the convergence speed and accuracy of the output consistence error are improved by using a pre-defined performance function; and according to the designed scheme, the unknown dynamic of the system can be effectively estimated, the derivation is simplified, and the control precision is improved.

The invention provides a multi-unmanned aerial vehicle (UAV) track planning method based on a culture ant colony search mechanism, which includes the following steps: (1) carrying out mesh generationon a standard space according to a grid method; (2) building a multi-UAV track planning model, including the number of UAVs, the start and end points and a threat model; (3) initializing the start point and the end point; (4) initializing an ant colony algorithm, including: initializing an ant colony and calculating a heuristic factor and a guide factor; and (5) assigning all ants to an initial node, and updating taboo knowledge; selecting next node for transfer according to the taboo knowledge and the state transfer probability until there is no optional node or a destination node is selected, updating historical knowledge, and updating pheromones according to the historical knowledge; and outputting a shortest path if the maximum number of iterations is achieved, and continuing the process until U multi-UAV optimal multi-path tracks are obtained. The problem that it is difficult to find the optimal flight tracks of unmanned aerial vehicles due to slow search and heavy computing burden is solved, and multi-UAV track planning is realized.

The invention provides a high water-cut oilfield well position determining method based on an evolutionary algorithm. The method comprises the steps of building an oil reservoir model of a predetermined oil area, and dividing the oil reservoir model into grids; obtaining the oil reservoir data, and obtaining a surplus oil potential enrichment area in a history matching mode according to the oil reservoir data; computing a well position constraint value for representing the surplus oil distribution and injected water waves and volume in the surplus oil potential enrichment area to obtain the well position advantage area; carrying out iteration on an oil well in the advantage area through the evolutionary algorithm to compute a fitness function, and evaluating the fitness function through a Kriging algorithm, wherein the evolutionary algorithm is adjusted according to the fitness function, and the Kriging algorithm considers the multi-dimensional space, the nugget effect and the local weight; obtaining a maximum fitness function value according to the iteration computing of the evolutionary algorithm so as to determine the well position. The improved evolutionary algorithm and the Kringing algorithm serve as the optimization tools, a dynamic potential index is built to reduce the searching space of the evolutionary algorithm, and the optimization computing convergence precision and speed are greatly improved.

The invention relates to a multi-objective decision engine parameter optimization method capable of enabling the minimum transmitting power, the minimum bit error rate and the maximum data rate to a cognitive radio system to be optimal at the same time. The method comprises steps of establishing a multi-objective decision engine model, calculating a multi-objective quantum ant colony algorithm path initial value, initializing a quantum information element of a multi-objective quantum ant colony algorithm, carrying out non dominated path sorting and the calculation of path congestion, sorting paths with the same non dominated path sorting rank, selecting a path with a non dominated path sorting rank of 1 and adding the path into an elite path set, calculating the path congestion, and selecting a path mapping to obtain the needed system parameter. According to the method, a discrete multiple-objective decision engine parameter optimization problem is solved, and the multi-objective quantum ant colony algorithm with non dominated path sorting is designed as a solution strategy, and the convergence precision is raised. The minimum transmitting power, the minimum bit error rate and the maximum data rate are considered at the same time, and the applicability is broadened.

The invention discloses a four-rotor UAV flight control method based on a fractional order power switching law. A whole controller is of a backstepping control structure, a second-order nonlinear system of a quadrotor UAV is split into two subsystems, control laws that satisfy Lyapunov's stability theory are constructed, and the two subsystems are in series connection through a virtual intermediate control variable to form the complete controller, so that the controller can well adapt to the nonlinearity of the system and have good integrity. At the same time, in order to enhance the anti-disturbance capability and robustness of the controller, in the second backstepping design, the controlled variable is subjected to the sliding mode control design, which introduces the high disturbance resistance and high robustness of the sliding mode control. .

The invention discloses a multi-objective optimized overall workshop layout method based on a multi-population genetic algorithm. The method comprises the following steps: firstly, a multi-row linearworkshop layout mathematical model is established, and a functional area layout problem is converted into a combined optimization mathematical model problem; secondly, based on the optimization objective of minimum total material handling cost and maximum area utilization ratio of workshop layout, a precise workshop layout model is established by taking account of constraints including horizontaland vertical placement of main streets and functional areas, adaptive row spacing and the like of the manufacturing workshop, and multiple optimization objectives are converted into a single evaluation function with a weighting method; finally, solving is performed with the multi-population genetic algorithm, immigration operators are linked with populations in the solving process, information exchange and co-evolution of multi-population are achieved, different crossover and mutation probability parameters are set for different populations by crossover and mutation probability control formulae, and different search purposes are guaranteed. The total logistics handling cost of the workshop can be effectively reduced, and the utilization rate of the workshop area is increased.

The invention discloses a multi-dimensional optimization method of an integrated energy system based on an improved whale algorithm. The method models the integrated energy system, takes the minimum overall operation cost as an objective function, takes the balance conditions of electricity, heat and cold as a constraint condition, and adopts the improved whale algorithm to obtain the global optimal solution of the objective function. The improved whale algorithm is based on the traditional whale algorithm by introducing adaptive weights and Cauchy mutation. On the one hand, the invention improves the global searching ability of the whale algorithm and avoids falling into local optimization; On the other hand, the local searching ability is improved, the convergence speed and precision areimproved, and the stability is good.

The invention discloses a self-adaption fuzzy neural compensating nonsingular terminal sliding mode control method of a micro gyroscope. The method mainly involves a nonsingular terminal sliding mode controller and a fuzzy neural network compensating controller; the designed nonsingular terminal sliding mode enables that a system can reach a sliding mode surface and a balance point within a limited time from any initial state, and therefore, the convergence rate and steady stacking precision of the system are improved; meanwhile, the fuzzy neural network compensates the parametric modeling error of the micro gyroscope and outside disturbance effect on line, in order to improve the tracking performance; the fuzzy neural network is practiced on line, the self-adaption learning algorithm of weight of the fuzzy neural is on the basis of the lyapunov stability theorem, which ensures the tracking performance and the stability of the whole control system. The simulation result shows that the method is able to improve the problem of trace tracking of the micro gyroscope and can also effectively inhibit the parameter uncertainty and the influence from the outside disturbance, and as a result, the robust tracking can be realized.

The invention provides an LSSVM (least squares support vector machine) wind speed forecasting method based on integration of GA (genetic algorithm) and PSO (particle swarm optimization). The method comprises the following steps: finite wind speed samples are divided into a training set and a testing set, and normalization processing is performed; GA and LSSVM related parameters are initialized; chromosome coding is performed, and initial population is generated randomly; the fitness corresponding to each chromosome is calculated, if requirements are met, the PSO in the fifth step is started directly, and if the requirements are not met, selection, crossover and mutation operation of the GA are performed; optimum parameter combination obtained with the GA is used for initializing the PSO related parameters; the optimum position fitness value of each particle is compared with the optimum position fitness value of the swarm; the final optimum parameter combination is output, and an optimized LSSVM model is obtained; a forecast wind speed time history spectrum is obtained. The LSSVM wind speed forecasting method based on integration of GA and PSO has the characteristics of high optimization precision, high convergence precision, fewer iterations, high success rate and the like.

The invention relates to an antenna array spare construction method based on a quantum glowworm search mechanism. The antenna array spare construction method comprises the following steps: establishing an antenna spare array model, and determining a key parameter, which corresponds to the quantum glowworm search mechanism, of the antenna spare array model; substituting a quantum glowworm position into a fitness function to obtain a fitness value of the quantum glowworm position, and confirming a local optimal position and a global optimal position in a glowworm group; updating the fluorescein value and the learning neighborhood of each quantum glowworm; updating a quantum glowworm quantum position and the quantum glowworm position; updating the dynamic decision domain radius of the quantum glowworm; calculating the fitness value under a new quantum glowworm position, and confirming the local optimal position and the global optimal position in a quantum glowworm group again; and if a maximum iteration is achieved, outputting the global optimal position, and mapping to obtain a spare antenna array form.