68results about How to "Avoid local optimum problems" patented technology

The present invention relates to the technical field of electrical engineering, and more particularly, to a method based on CEEMD-VMD-GA. The short-term wind speed prediction method based on ORELM model firstly obtains the historical wind speed data and pretreats the data, then decomposes the historical wind speed data into a series of discrete modes with specific sparse attributes by using the complementary empirical mode decomposition and variational mode decomposition. Then genetic algorithm is used to optimize the outlier robust limit learning machine prediction model to predict all the subsequences in one step. Finally, all the predicted values of the sub-sequences are superposed and the actual predicted results are obtained. The invention utilizes two-layer decomposition of complementary empirical mode decomposition and variational mode decomposition to reduce non-stationarity and non-linearity of wind speed series, Genetic algorithm is used to optimize the outlier robust limit learning machine to form a hybrid model for single-step prediction, which reduces the influence of complex characteristics of wind speed series on prediction results, improves the accuracy of short-term wind speed prediction, and solves the problem of local optimization of neural network.

The invention discloses an electric vehicle power battery SOC intelligent detection device which is characterized in that the intelligent detection device includes a battery parameter acquisition platform and a battery SOC estimation system, the battery parameter acquisition platform collects real-time parameters of voltage, current and temperature of an electric vehicle power battery group, the battery SOC estimation system estimates an SOC value through the collected parameters, and a battery SOC system is a nonlinear, time-delay and multivariable coupling complex real-time system with a high requirement. According to the device, a problem that a conventional detection device can not obtain an ideal effect of the intelligent detection of the electric vehicle power battery SOC.

The invention discloses a resource allocation optimization method based on reinforcement learning. The method comprises the following steps: acquiring a bandwidth value of a downlink, acquiring the number of physical resource blocks which can be called within a single transmission time interval according to the bandwidth value, and acquiring the number of user services to be transmitted, the characteristics of the user services to be transmitted on an nth physical resource block at the current t moment, and Characteristics of the whole downlink at the t-1 moment; judging whether the bandwidthutilization rate of the downlink needs to be improved; and if the bandwidth utilization rate of the downlink needs to be improved, whether the fairness of the downlink needs to be improved or not, orthe trade-off of the bandwidth utilization rate and the fairness of the downlink needs to be realized, and if the bandwidth utilization rate of the downlink needs to be improved, inputting the characteristics into the trained bandwidth utilization rate reinforcement learning model to obtain the metric value of the ith user service on the nth resource block. The technical problem that the scheduling performance is affected due to the fact that an existing algorithm only considers the local optimal solution condition caused by optimal allocation of a single resource block can be solved.

The invention discloses a parking AGV path planning method based on an improved ant colony algorithm. The method comprises the following steps of: 1) modeling a parking environment, 2) initializing system parameters, 3) improving a pheromone updating strategy, 4) improving heuristic information strength, and 5) using an improved ant colony algorithm for path planning on the parking AGV. Accordingto the method, a time optimal path from a starting point to a target point is provided for searching of the parking AGV, and an AGV system is ensured to accurately and rapidly complete vehicle storing, taking and parking tasks in a short time.

The invention discloses a PSS4B parameter setting method based on a hybrid particle swarm optimization algorithm. First, a PSS4B double-input model is transformed into a single-input model with speed deviation being an input signal, and then a phase angle of a transfer function of the single-input model is solved, namely phase-frequency characteristics of PSS4B; different optimization variables are selectively set targeting a three-frequency-band two-order lead-lag phase compensation link time constant for a conventional self-excited excitation system and a three-machine excitation system with large uncompensated phase-frequency characteristic lag, and a PSS4B parameter optimization model is established; and the hybrid particle swarm optimization algorithm is utilized to perform PSS4B parameter setting, particles in a specified number are selected according to hybrid probability and placed into a hybrid pool in every iteration, every two particles are hybridized to generate filial particles in the same number, and the filial particles are used for replacing parental particles to improve optimizing capacity. Through the method, phase compensation requirements of different types of excitation systems can be met efficiently and conveniently.

The invention discloses a fault line selection method for a power distribution network containing distributed generation based on a deep belief network. The method comprises the steps of acquiring zero-sequence current and voltage signals of the head end of each feeder line under different single-phase grounding fault conditions; extracting steady-state and transient-state characteristic quantities contained in the zero-sequence current and voltage signals; performing data and polarity pretreatment on the original characteristic quantity; and performing fault line selection and positioning byadopting a deep belief network algorithm. According to the invention, the deep learning theory is applied to small-current grounding fault positioning containing the distributed generation, better fault tolerance is achieved, the accuracy of small-current grounding fault line selection can be improved, and the reliability and the safety of the operation of the power distribution network are improved at the same time.

The invention discloses a self-organizing TS-type fuzzy network modeling method applied to infrared flame recognition, comprising the following steps: (1) collecting time-domain signal data of different flames and interference sources, and preprocessing the time-domain signal data to obtain frequency-domain signal data; (2) extracting the characteristic information from the time-domain and frequency-domain signal data of the waveform, obtaining the characteristic vector of the flame, and composing a sample set; (3) dividing the sample set into training set, verification set and test set; (4) building TS-RBF fuzzy neural network; (5) setting TS-RBF fuzzy neural network parameters initial value, using the training set of samples to TS-RBF fuzzy neural network training, structure, parameter learning; (6) using verification sets to test the training TS- Verification and Model Selection of RBF Fuzzy Neural Network; (7) inputting the test set into the trained TS-RBF fuzzy neural network, theresults as the final evaluation of the model.

The invention provides optimization processing for a search target task of multiple unmanned aerial vehicles, wherein the optimization processing comprises the steps: enabling the prior probability distribution of a search target of single unmanned aerial vehicles to serve as a prior search graph of a search region, carrying out iterative updating of the prior search graph, and obtaining updated search graphs of the single unmanned aerial vehicles; calculating the communication probability between the unmanned aerial vehicles, determining the communicability between any two unmanned aerial vehicles based on the communication probability, and determining an unmanned aerial vehicle communication network; fusing the search graphs among the unmanned aerial vehicles based on the communication probability among the unmanned aerial vehicles to obtain a fused search graph; updating the fused search graph in combination with the target motion to obtain a fused updated search graph; and enablingthe single unmanned aerial vehicles to update the search graph based on fusion, optimizing the positions of the unmanned aerial vehicles and guiding the unmanned aerial vehicles to fly. The method comprehensively considers and optimizes the communication performance and the search performance in the search task of the multiple unmanned aerial vehicles, and is more suitable for the search of a moving target, especially the target search task in the actual complex marine environment.

The invention discloses a remote sensing mass image automatic screening method based on an optimized genetic algorithm, and the method comprises the steps: automatically screening an image solution meeting the demands through employing the genetic algorithm according to the requirements of a user for the coverage area, the resolution coverage rate, the cloud amount coverage rate and the timelinesscoverage rate; firstly, obtaining an initial image set, and then conducting coarse screening; then, modeling in a binary coding mode, and generating an image solution set through initialization; andthen calculating a comprehensive score, and calculating a probability according to the score for selection; solving a new image solution set for the selected image solution set through crossover variation; circulating the process, and selecting a final solution when a circulation ending condition is reached. According to the method, the problem of local optimization caused by a traditional greedyalgorithm is solved, the problem that the solving overlapping rate is too high or the coverage rate is low is avoided, weight configuration can be carried out on preferences in multiple aspects such as the coverage area, the cloud coverage rate and the timeliness coverage rate, and different requirements of different users for final screening results are met.

The invention provides a method and system for realizing global optimization of AODV routing, and a medium. The method comprises an updating process of an initiating node Q table and a process of calculating approximate global optimization routing by a destination node, wherein the updating process of the initiating node Q table is as follows: a distribution value function is introduced into an initiating node to optimize an updating strategy of a Q value, and a weight parameter is calculated by establishing a trajectory prediction model by combining Kalman filtering with a Gaussian filteringalgorithm, so the updating process of the initiating node Q table is completed; the process of calculating the approximate global optimization route by the destination node is as follows: the destination node returns the route with the maximum average Q value in the cache within a certain time to the source node. According to the invention, the robustness and the information transmission success rate of the network are improved, and the end-to-end delay of the network is reduced.

The invention discloses a method for accurately obtaining the relationship between the output current and the output power of a fuel cell system. The method comprises the steps: selecting a proper curve function equation to be fitted according to an output current output power characteristic curve obtained by the calibration experiment; combining an experiment and simulink fuel cell modeling simulation to obtain an output current and an output power value; and performing parameter solution of a to-be-fitted curve on the output current and the output power by using a particle swarm algorithm to obtain a relation curve of the output current and the output power of the fuel cell system. The method has the advantages that the reliability of data and models is improved, output current and output power values under different working conditions can be obtained by changing experimental conditions in simulation, the method is used for multi-working-condition research, the adaptability is improved, and the research cost is reduced. A particle swarm algorithm is adopted to solve parameters of a to-be-fitted curve, so the calculation efficiency and the curve precision are improved, and the accuracy of output power control by utilizing the curve is further improved.

The invention discloses an end-to-end indoor mobile robot autonomous navigation method in a non-structural environment. The end-to-end indoor mobile robot autonomous navigation method comprises the following steps of 1) acquiring a plane layoutdiagram in a strange building, 2) carrying out topologization on the layout diagram, and creating an undirected graph, 3) generating a shortest node path by using the undirected graph and combining with the target room, 4) inputting the first visual angle image of the robot into an anti-collision navigation module, and generating an anti-collision road point, (5) generating an anti-collision path through cubic splines, and tracking through model predictive control, and (6) judging whether the target point is reached or not, if so, ending navigation, otherwise, returning to the step (3). By adopting the autonomous navigation method of the robot, autonomous navigation can be realized only by using a single monocular RGB camera as visual input in a complex medical environment. The method has the advantages of simplicity, flexibility, economy, practicability, high accuracy and strong robustness.

The invention relates to a wind power prediction method. The method comprises the following steps: S1, decomposing an original wind power time sequence according to local mean decomposition; S2, constructing a respective training data set and a respective test data set for each PF component and margin; S3, establishing a wind power prediction model of an improved differential algorithm optimization extreme learning machine for each PF component and margin; S4, inputting the training samples of the plurality of PF components and the allowance in the training data set into a wind power prediction model of an improved differential algorithm optimization extreme learning machine one by one for training to obtain respective corresponding wind power prediction sub-models; S5, inputting the testdata into the corresponding wind power prediction sub-models for prediction; and S6, performing combination superposition processing on the prediction output value of each wind power prediction sub-model. According to the wind power prediction method, the influence of strong nonlinearity of the original wind power data on the prediction result is effectively reduced, and the wind power predictionresult with higher precision is obtained.

The invention relates to an ant colony graph matching method based on a G-W distance. The method comprises the following steps: selecting feature points of a to-be-matched graph; generating an H matrix under a G-W distance through the sampling information data of the feature points; establishing a secondary convex optimization model for the minimum difference degree of the G-W distance between thefeature points, and proposing the hypothesis of the model in combination with the ant colony; stipulating physical constraint conditions of the ant colony in a G-W distance minimum difference degreeoptimization process, so that the ant colony meets a one-to-one correspondence matching principle; accumulating distances among city nodes traversed by ants; optimizing the quadratic convex optimization problem under the G-W distance, and solving a shortest path and city nodes on the path; and defining the shortest path as the overall difference degree between the two manifold curved surfaces, andcalculating the matching relationship between the feature points by utilizing the relationship between the city nodes on the shortest path and the serial numbers of the feature points to complete graph matching. The method is the optimal solution closest to the real theory, and the dependence of traditional numerical iteration optimization on the initial solution is overcome.

The invention discloses a 5G communication adaptive transmission method based on long-short-term multi-threshold channel state prediction. The method comprises the following steps: 1, collecting historical channel statistical property samples and channel state samples corresponding to the historical channel statistical property samples through 5G radio equipment; 2, constructing a neural network based on LSTM; 3, updating neural network parameters of the LSTM through an adaptive gradient descent algorithm; 4, proposing a multi-threshold algorithm to optimize the network; 5, dynamically selecting a redundant block according to the predicted channel state to improve the reliability of data transmission. According to the invention, the channel prediction takes time correlation into consideration while fitting the transmitting signal model to the receiving signal model, so that the prediction precision can be improved, the proportion of redundant blocks can be dynamically adjusted according to the channel state, and the reliability of data transmission in 5G communication is improved.

The invention relates to a pulmonary nodule benign and malignant identification method, and particularly relates to a pulmonary nodule benign and malignant identification method based on support vector machine sample reduction. The method comprises the steps that an original sample set S0 of malignant and benign pulmonary nodules is acquired; sample reduction is carried out for the original sample set S0 of malignant and benign pulmonary nodules, so as to acquire a final train set S2 of malignant and benign pulmonary nodules of a support vector machine; support vector machine train is carried out on the final train set S2 after reduction, so as to acquire a final classification decision function; and support vector machine prediction is carried out on an unknown pulmonary nodule sample xi', so as to acquire a pulmonary nodule benign and malignant identification result. According to the invention, the method of support vector machine sample reduction is provided to improve the train speed of the support vector machine; a space storage requirement is reduced; the pulmonary nodule benign and malignant identification time is reduced; and the diagnosis efficiency and the objective consistency of doctors are improved.

The invention discloses an improved ShuffleNet-based radar signal RD image interference identification method and system, and the method comprises the steps: constructing a radar signal RD image data set under an interference condition, and dividing the radar signal RD image data set into a training sample and a test sample; constructing an attention-enhanced ShuffleNet recognition model, and setting a loss function of a classification task of the attention-enhanced ShuffleNet recognition model; training a ShuffleNet recognition model of which the attention is enhanced by utilizing the training sample; and inputting a test sample into the trained attention-enhanced ShuffleNet recognition model, predicting and outputting a test sample category and a confidence score, and completing radar signal RD image interference recognition. The attention enhancement module is introduced, interaction between feature channels is enhanced from the channel information interaction level, the feature discrimination is improved, and therefore the RD image recognition precision of the image radar signal under interference is improved.

The invention relates to a gear reducer optimization method based on an FOA (FruitFly Optimization Algorithm). The method comprises the steps of taking a two-stage gear reducer as a design research object, and establishing a mathematic model with a minimum volume of the two-stage gear reducer as an optimization target; introducing a correction factor described in the specification into the FOA in combination with the FOA to obtain a corrective FOA; and then optimizing the mathematic model established in step S1. The gear reducer optimization method based on the FOA has the advantages of rapid convergence speed and effective avoidance of a local optimum problem on the premise of guaranteeing safety and reliability.