49 results about "Simulated annealing genetic algorithms" patented technology

The invention discloses a method for the calculating ice coating thickness of a transmission line by utilizing video image processing technique, belonging to the technical field of digital video image processing or online monitoring of the transmission line. The method takes digital image intercepted from a video flowing of the transmission line which is transmitted into a surveillance center as the object of study and processes the image by methods of gradation of image, two-dimension image segmentation, filtration, regional mark and the like in advance. In the process of pretreatment, the image is segmented by adopting a new two-dimension varimax based on simulated annealing genetic algorithm, and the image of the transmission line is marked by adopting eight connected region marking method. Finally, by the contrast and calculation of the pixels of the images which are obtained before and after the ice coating of all the transmission leads, an average value is obtained, and the ice coating thickness is further calculated. When the ice coating thickness of any of the transmission leads exceeds the prescriptive safety range, alarm is given, so that deicing measure is adopted in time, thus providing security for the safe running of an electric power system.

The invention relates to a distributed power supply-containing microgrid multi-target optimization scheduling method. For a microgrid containing various distributed power supplies, a multi-target optimization scheduling model based on operating maintenance cost, pollution discharge processing cost and comprehensive benefit cost is established, conversion of the maximal fuzzy membership degree into non-linear simple target optimization is adopted, influences of various distributed power supply characteristics on the microgrid optimization scheduling are comprehensively considered, and according to corresponding operating scheduling strategies, and a self-adaptive simulated annealing genetic algorithm is adopted to obtain the optimal scheme of economic operation of the microgrid under multiple targets. Examples show that a multi-target model can reflect the real operating condition of the microgrid better than a simple target model, and achieves better environmental benefits with small operating economy under a precondition of comprehensively considering an economic property and an environmental protection property.

The invention discloses a selection method for subintervals of near infrared spectral characteristics based on a simulated annealing-genetic algorithm. The method comprises the following steps: pretreating a near infrared spectrum; then dynamically dividing subintervals on the pretreated near infrared spectrum, introducing an Metropolis criterion in the simulated annealing algorithm to gene exchange and gene selection operators, and selecting an optimal character subinterval with the simulated annealing-genetic algorithm; and finally judging the best subinterval division method to be combined with the optimal character subinterval and building a PLS model for the selected optimal character subinterval. In the selection method, high-quality offspring individuals can be generated through improved variation and commutating operators, not only adaptability levels of overall populations are improved, but also enough power for population evolution is provided; and deficiency brought by the total number of the spectrum subintervals manually designated according to the experiences in the process of modeling can be avoided, and spectral models with high precision and strong prediction ability can be rapidly obtained.

The invention provides a lithium battery capacity online prediction method based on K-means clustering and an Elman neural network. The method comprises the following steps: firstly, determining the model of a lithium ion battery to be tested, carrying out cyclic charging and discharging experiment by utilizing a battery with the same model as the battery to be tested, recording a lithium batterydischarging time sequence, carrying out K-means clustering on the lithium battery discharging time sequence, and establishing a data model; and then, introducing a simulated annealing genetic algorithm to optimize initial weight and threshold of the Elman neural network, training the Elman neural network by using the constructed data model, and establishing a lithium ion battery actual capacity prediction system offline. When capacity prediction is carried out online, the collected actual discharging time sequence data of the lithium ion battery to be tested is input into the prediction system, and the actual capacity of the battery is predicted while the normal work of the lithium ion battery is not influenced. According to the invention, online accurate prediction of the actual capacityof the lithium ion battery can be realized.

The present invention discloses a network community division method based on simulated annealing genetic algorithm, and mainly solves problems of poor search capability and low division efficiency in present genetic algorithm. The network community division method based on simulated annealing genetic algorithm comprises the following realizing steps: (1) reading in a network diagram; (2) generating an adjacent matrix according to the network diagram; (3) initializing genetic algorithm parameters; (4) decoding chromosomes and calculating objective function values; (5) selecting chromosomes with relatively large objective function values to form a parental population; (6) crossing and varying chromosomes and generating new chromosomes to form progeny populations; (7) initializing simulated annealing algorithm parameters and implementing a local search; (8) obtaining a next parental population and performing iteration; (9) determining whether or not the iterative algebra reaches the largest algebra Gmax; if the iterative algebra reaches Gmax, then the iteration is terminated and the chromosome with the largest objective function value is output, and division of every node in the output chromosome is a final division result of nodes in the community. The network community division method based on simulated annealing genetic algorithm has advantages of strong search capability and high accuracy.

The invention relates to a method for real-time power prediction of photovoltaic power station based on a SAGA-FCM-LSSVM model, which includes collecting power generated in corresponding period of time of photovoltaic power station and corresponding meteorological parameters on meteorological station, and obtaining meteorological data; power parameter samples of the daily weather being pretreated;based on four statistical indexes and simulated annealing genetic algorithm, the fuzzy C-mean clustering algorithm clustering the samples from the first day of the history day to the day before the forecast day. According to the meteorological eigenvalue of each cluster sample set, the center point of each cluster meteorological eigenvalue is calculated, and the classification of the forecast date is judged by Euclidean distance. The least square support vector machine is trained by using the same kind of parameter samples as the predicted date, and the training model is obtained. The meteorological parameters and power values of the first 2 hours of the time to be predicted are input into the training model for real-time prediction of the power generation at each time of the time to be predicted. The invention can predict the output power value of the photovoltaic power station at each time in real time.

The invention discloses a photovoltaic array parameter identification method based on measured data. The method includes the steps that a feasible solution range of parameters to be acquired are determined in advance through a conventional parameter identification method; parameter identification is conducted on the measured data of a photovoltaic array through a genetic algorithm and a simulated annealing genetic algorithm. The parameter identification method is high in practicality. By compressing a region of search, identification efficiency of the genetic algorithm is effectively improved, and complexity of the algorithm is reduced; meanwhile, the measured parameters are identified by combining the genetic algorithm and the simulated annealing genetic algorithm, and therefore accuracy of identification results is effectively improved.

The invention discloses a cognitive network power distribution method based on interference temperature. The method comprises the following steps of: establishing a multi-cognitive-user cell model; determining an interference temperature limit of a main user; determining a signal to noise ratio requirement of a receiver of each cognitive user; determining conditions of power value measurement required when the main user works normally; determining system effectiveness and constraint conditions; and adopting a simulated annealing genetic algorithm to estimate the optimal power distribution. With the adoption of the method provided by the invention, the optimal distribution of emission power is realized under in a multi-cognitive-user condition; and resources can be sufficiently shared under the precondition that the cognitive users do not interfere with the main user.

The invention discloses a hydrologic frequency linear parameter estimation method, which combines a simulated annealing-genetic algorithm (SAGA) and a maximum likelihood (ML) method to establish an SAGA-ML method, namely an expression for solving a minimal value of an opposite number of a likelihood function is taken as a target function, a parameter numeric area is estimated by a moments method, and is taken as a constraint condition, and then the SAGA is applied to perform parameter estimation. Essentially different from the thought of the conventional ML method, the SAGA-ML method carries out parameter optimization through a genetic algorithm. Monte Carlo experiments verify that the SAGA-ML method has good accuracy in aspects of parameter estimation and different frequency design value estimation; simultaneously, the method is not limited to linear type, parameter number and the constraint condition, can avoid the conditions that the likelihood function has no solution and the like when the conventional ML method is applied; and the solving process is simple, convenient and quick, so that the ML method become an effective method theoretically and practically.

The invention discloses a metal-semiconductor contact nonlinear transmission line model and parameter fitting method and relates to the semiconductor device structure performance detection and representation field. The metal-semiconductor contact nonlinear transmission line model and parameter fitting method comprises 1, establishing a nonlinear transmission line model; 2, measuring an I-V curve of parallel double rectangular electrodes on semiconductor thin film materials; 3, obtaining an R-V curve through a relational expression that R is equal to dV / dI; 4, calculating a theoretical R-V curve of a testing structure through the nonlinear transmission line model; 5, fitting metal-semiconductor contact physical parameters through a simulated annealing inheritance algorithm. The metal-semiconductor contact nonlinear transmission line model and parameter fitting method has the advantages of establishing the nonlinear transmission line model and a corresponding numerical algorithm, quantitatively extracting the metal-semiconductor contact relevant physical parameters and providing a new method for precise representation and analysis of nonlinear metal-conductor contact.

The invention discloses a fault diagnosis method of a proton exchange membrane fuel cell for tramcars, comprising the following steps: collecting various types of data output during the actual operation of a fuel cell tramcar as original preparation data, and carrying out normalization and dimension reduction on the original preparation data to obtain original data; screening the original data based on fuzzy c-means clustering of a simulated annealing genetic algorithm to obtain sample data of fault diagnosis; and inputting the sample data to an established deep confidence fault diagnosis network based on an SMOTE algorithm, and outputting an optimal fault diagnosis accuracy and a classified fuel cell fault result. Accurate fault diagnosis data of a proton exchange membrane fuel cell for tramcars can be obtained. By processing the original data, a globally optimal solution can be found, more accurate sample data can be obtained, and the accuracy of model classification can be improved.By constructing the fault diagnosis network, unbalanced data can be processed, and the classification accuracy is greatly improved.

The invention discloses a mechanical arm path planning method based on a simulated annealing genetic algorithm. The method comprises the following steps that step 1: a Lagrange method is adopted to conduct position level, velocity level kinetics and kinetics modeling on a space six-degree-of-degree mechanical arm; step 2: regarding the disturbance generated on a base and the constraint of all joint corners of the mechanical arm, due to the collision of the tail end of the mechanical arm within very short time, an fitness optimizing function for mechanical arm tracks is constructed; step 3, a five-level polynomial is adopted to conduct fitting on mechanical arm paths, and the simulated annealing genetic algorithm is utilized to solve the optimal track so as to meet a better fitness function. According to a mechanical arm capturing task, under the circumstance of ensuring the minimum base disturbance and the constraint of all the joint corners, the simulated annealing genetic algorithm is utilized to solve the optimal solution, and the convergence rate and the solving accuracy are increased.

The invention discloses an adaptive simulated annealing genetic algorithm used for sleep electroencephalogram staging feature selection. Sleep staging is performed through electroencephalogram signals, a large number of feature parameters require to be extracted out of the electroencephalogram signals, and the relatively optimal feature parameter combination is selected out through screening to be used for establishing a sleep electroencephalogram mathematical model. In the present simulated annealing genetic algorithm, the high overall search capacity of the genetic algorithm and the high local search capacity of the simulated annealing algorithm are reserved so as to enhance the probability of generating excellent individuals. In the simulated annealing operation of the present algorithm performed on the individuals in the iterative process, the mechanism for randomly generating new solutions in the neighborhood of the current optimal solution has the fatal flaw. The algorithm aims at the flaw and solves the disadvantages that the neighborhood new solution generation mechanism of the conventional simulated annealing genetic algorithm has low iterative efficiency and is greatly affected by the neighborhood range and can realize adaptive adjustment of crossover probability and mutation probability, and the fitness function can be designed by using the weighing method.

The invention discloses an intelligent reservoir optimal operation method. According to the intelligent reservoir optimal operation method, based on the analysis on the defects of a traditional simulated annealing genetic algorithm in the aspect of reservoir optimal operation, improvement is conducted on the algorithm by introducing the ecological niche technology, adopting the self-adaptive crossover and mutation strategy and adopting the optimization saving strategy in the selection process, and a reservoir optimal operation nonlinear mathematical model with the maximum generating capacity as the target is solved according to the specific condition of a reservoir.

The invention discloses an accurate modeling method of an electromechanical actuation system friction pair; the method employs a Stribeck friction model and a simulation annealing heredity algorithm,and belongs to the electromechanical system modeling technical field; the method comprises the following steps: 1, online test; 2, model selection; 3, target function selection; 4, iteration search identification. The iteration search identification step comprises the following 7 substeps: 1, random generation of initialization population; 2, individual fitness calculation; 3, employing a random traversal sampling method to form a new generation population; 4, simulation annealing selection operation; 5, simulation annealing intersect operation; 6, simulation annealing mutation operation; 7, iteration operation termination determination. Compared with the prior art, the electromechanical actuation system modeling method is faster in convergence speed and higher in modeling precision.

The invention discloses a best polarity search method for power consumption of a three-value FPRM circuit. The best polarity search method comprises the following steps: at first, expressing a three-value FPRM circuit by use of the three-value FPRM logic function under p polarity, decomposing multi-input operation contained in the three-value FPRM logic function to obtain multiple two-input module 3 addition doors and multiple two-input module 3 multiplication doors under the p polarity, using power consumption generated by the two-input module 3 addition doors and the two-input module 3 multiplication doors as the power consumption of the three-value FPRM circuit under the p polarity, establishing a power consumption estimation model of the three-value FPRM circuit, and finally, using a genetic simulated annealing algorithm to carry out best polarity search on the power consumption of the three-value FPRM circuit to optimize the power consumption of the three-value FPRM circuit. The best polarity search method has the advantages of achieving the best polarity search of the power consumption of the three-value FPRM circuit to optimize the power consumption of the three-value FPRM circuit; 13 MCNC Benchmark circuits are randomly adopted to carry out simulation verification, compared with 0 polarity, in the best polarity of the power consumption searched by the best polarity search method disclosed by the invention, the number of the module 3 addition doors is saved for 57.6% on average, the number of the two-input module 3 multiplication doors is saved for 46.25% on average, and the power consumption is saved for 73.98%.

The invention provides a respiratory movement prediction method which comprises the following steps of constructing a BP neural network, and optimizing the BP neural network by fusing a simulated annealing genetic algorithm, optimizing a weighting error of the BP neural network, acquiring respiratory movement data and processing the respiratory movement data, training the BP neural network with the optimized weighting error by using the processed respiratory movement data to obtain a strong regression device, and predicting the respiratory movement state of the human body by using a strong regression device. The respiratory movement state of the patient can be predicted with high precision, and the actual tumor is prevented from exceeding the target area, so that the patient can be better subjected to radioactive therapy.

The invention relates to a simulated annealing genetic algorithm based reactive power optimization method of an AC / DC system. The reactive power optimization method comprises the following steps of (1) building an AC / DC system model, and calculating the power flow of the AC / DC system, wherein the AC / DC system comprises an AC side and a DC side of which powers are transferred through a converter; and (2) building a reactive power optimization model according to a target function expressed by a usage cost value of network loss, and figuring out the reactive power optimization model according to a set constraint condition. The reactive power optimization method has the advantages that a simulated annealing genetic algorithm is introduced into reactive optimization and voltage control analysis of the AC / DC system; and through the combination of the characteristics of two algorithms, the respective advantage of the two algorithms is absorbed, the global optimal solution can be found with large probability, and meanwhile, the convergence rate is higher.

The invention provides a flexible workshop dynamic scheduling method and device. The method comprises the steps that: at least one alternative scheme of a corresponding relation between each second process and machinable equipment is generated if a quality inspection result of a first process is unqualified in a scheduling process of an original scheme, wherein the second process is a process which is not started in the original scheme when the quality inspection result of the first process is unqualified; on the basis of a situation evaluation algorithm, an optimal scheme corresponding to each second process is determined, wherein the optimal scheme is one of at least one alternative scheme corresponding to each second process; and according to the optimal schemes corresponding to the second processes, the processing sequence of the second processes is determined through a simulated annealing genetic algorithm to obtain a target scheme. According to the invention, the problem that theoperation plan does not conform to the actual production can be solved, and guidance is provided for the workshop scheduling personnel to carry out flexible operation workshop dynamic scheduling witha quality inspection process.

The present invention discloses an intelligent method for optimizing water resources allocation. The method comprises the following specific steps: 1 collecting data; 2 preprocessing the data; 3 using a gray hierarchical model to process an standard value of each data and obtaining the weight W; 4 using a fuzzy comprehensive evaluation method to obtain a membership degree matrix, and obtaining a comprehensive evaluation result b according to the membership degree matrix; 5 taking b and each data in step 2 as input data, and taking configuration results in each area as output; 6 according to the actual requirement, obtaining a fuzzy comprehensive evaluation result b1 and taking the b1 as neural network input so as to obtain a stimulated allocation result; and 7 sending the stimulated allocation result into a fuzzy comprehensive evaluation method to obtain the score b2, using a simulated annealing genetic algorithm to fine tune b2 until the planning requirement is satisfied, and obtaining a final water resources allocation result. According to the method disclosed by the present invention, water resources can be more accurately and reasonably allocated, and iteration and optimization can be carried out on the previous allocation result, so that the scientific and rationality of the whole water resources allocation process are strengthened.

The invention provides a generative adversarial network oversampling method and device based on a simulated annealing genetic algorithm, and the method comprises the steps: determining the corresponding relation between sample data and optimal filial generation sample data through the adversarial learning capability of a generative adversarial artificial neural network; specifically, determining the optimal filial generation sample data according to a preset individual fitness condition; determining network parameters of a generative adversarial artificial neural network according to the optimal filial generation sample data; determining the corresponding relation according to the network parameters; acquiring target sample data; and determining optimal filial generation target sample datacorresponding to the target sample data through the corresponding relationship. A plurality of adversarial learning targets are used simultaneously to train a generative network, so the limitation ofa single adversarial learning target is overcome; whether the generative network is updated or not is selected by using a simulated annealing algorithm, so the model is prevented from falling into alocal optimal solution, and the model is converged to global optimum.

The invention discloses a spot welding robot operation space smooth path planning method for a curved surface workpiece. In the method, a spot welding robot kinematic model module, a motion constraint condition module, an inter-welding-spot shortest smooth obstacle avoidance path planning module and an optimal welding spot welding sequence planning module are included. The method is characterized by modeling through a three-dimensional grid method curved surface workpiece profile and welding spot distribution, adopting an improved A-star algorithm and a uniform B spline curve subdivision algorithm for smooth processing to generate an inter-welding-spot shortest smooth obstacle avoidance path, and applying a multi-target elite simulated annealing genetic algorithm to obtain an optimal welding sequence; and according to the collision-free motion constraint conditions of an electrode holder coordinate system and a welding spot coordinate system and the safe distance constraint conditions of the electrode holder coordinate system and a curved surface workpiece profile, solving through inverse kinematics to obtain a joint space path corresponding to the current welding path. The method has an application reference value in the actual industry, the planning and debugging time of an engineer can be shortened, and the working efficiency of a robot can also be improved.

The invention discloses a stroke control method for energy-saving operation of an elevator, which utilizes a simulated annealing-genetic algorithm to solve energy-saving operation control parameters of the elevator, adopts a five-stage S-shaped speed curve to control the stroke of the mine elevator, and can meet the operation requirements of safety, reliability, comfort and the like. A simulated annealing-genetic algorithm is utilized to calculate the minimum value of the primary improvement energy consumption, so that the S curve travel parameter when the energy consumption is minimum is solved; and the elevator is controlled by using the S curve stroke parameter when the energy consumption of one-time lifting is minimum, so that a remarkable energy-saving effect is achieved. According to the method, on the basis of S-stroke control, the energy consumption is analyzed to obtain the related quantity of the energy consumption, then the simulated annealing-genetic algorithm is utilized to solve the globally optimal solution of the established energy consumption target function, so that the maximum speed and the acceleration in the S-stroke control are determined, the whole S-shaped speed curve is determined, all the advantages of the S-stroke control are achieved, and the method is suitable for the S-stroke control. And the purpose of saving energy consumption can be achieved.

The invention discloses an optimized distribution method of detection points on a body wall board of a large airplane. The method comprises the following steps: (1) building a wall board deformation process simulation finite element model based on movable traction of a numerical control locator without considering the dead weight of the wall board, and selecting a part of finite element node sets on a bulkhead for serving as initial detection point sets to be selected; (2) introducing each deviation source into the wall board deformation process simulation finite element model by taking the moving degrees of freedom of the numerical control locator in directions X, Y and Z to obtain corresponding wall board deformation modes; (3) building a wall board deformation mathematic model formed by overlapping wall board deformation modes, acquiring a Fisher information matrix comprising wall board deformation information from the wall board deformation mathematic model by using a least square method and an optimal moment estimation method, and selecting a required number of optimal detection point sets from the initial detection point sets to be selected by using an adaptive simulated annealing genetic algorithm with the determinant of a maximized Fisher information matrix as a criterion.

The invention provides a high-frequency oscillation rhythm detection method intelligent algorithm optimized fuzzy clustering to detect high-frequency oscillation rhythms on the basis of the fuzzy clustering method. The method comprises basic steps as follows: selecting a mean singular value (MSV), a wire length fl, a power ratio and a frequency spectrum centroid as features of an epilepsy electroencephalogram (EEG), and constructing the features into a feature vector to be input as a clustering algorithm; optimizing a fuzzy clustering algorithm by a simulated annealing genetic algorithm in anintelligent algorithm to obtain an optimized parameter vc; obtaining an optimized result according to the optimized parameter vc; and selecting medians and interquartile ranges to analyze statisticalfeatures of each class to detect the high-frequency oscillation rhythms. The method has the beneficial effects as follows: the detection precision of high-frequency oscillation rhythms of epilepsy EEGis improved and doctors are helped to perform epilepsy diagnosis and epileptogenic focus excision.