43results about How to "Overcome the shortcoming of easy to fall into local optimum" patented technology

The invention discloses a multidimensional collaborative power grid planning method which includes the processes: acquiring geographic environmental information data and calculating comprehensive construction cost data by a rasterized map; building a power grid planning model; and calculating the solution of a power grid planning scheme by an optimal ant colony algorithm according to the acquired data and the power grid planning model. The method includes the steps: comprehensively considering substation locating and sizing, line selection and line corridor selection; and building a multidimensional collaborative power grid planning model considering complicated environmental influence. The multidimensional collaborative power grid planning method can accurately consider the influence of environmental factors on power grid planning. Line construction cost calculation precision is improved, and estimation difficulty is reduced. Catalytic elements are generated according to deviation of the local movement direction and the target movement direction of ants, so that the state transition rule of the ants is improved. The construction cost of lines on a mass raster and a substation is full-automatically and rapidly calculated, and calculation precision and efficiency are remarkably improved.

The invention discloses a manipulator dynamic model identification method based in an improved artificial bee colony algorithm. A manipulator linear dynamic model taking joint friction into consideration is established through utilization of an improved Newton-Euler method. A dynamic parameter identification algorithm is designed through importing of the improved artificial bee colony algorithm. An UR industrial robot is taken as an experiment subject; through design of a stimulation track, joint movement of the industrial robot is stimulated; the joint movement data is sampled; after torque is filtered, parameter identification is carried out through utilization of the improved artificial bee colony algorithm; the dynamic parameter estimation of the UR industrial robot is realized; and the dynamic model is verified according to the torque prediction precision. An experiment shows the accuracy and effectiveness of the industrial robot dynamic model the identified by the invention.

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.

The invention relates to an antenna array sparse construction and a directional diagram comprehensive method based on quantum bat searching. An overall optimal position of a scattered quantum bat group is obtained based on a quantum bat searching mechanism, and the overall optimal position is mapped as a sparse antenna array. On the basis of a constructed sparse antenna array, an overall optimal quantum position and an optimal mapping position of a continuous quantum bat group are obtained based on the quantum bat searching mechanism. Therefore, the optimal excitation amplitude of the antenna array is obtained.

The invention relates to a method for establishing a routing path through a Q learning on-board network based on fuzzy reasoning. The method comprises the following specific steps: (1) performing network initialization; (2) sending a greetings data packet in a broadcasting manner; (3) starting to send a request message through a source network node; (4) calculating the channel grade of an intermediate network node; (5) updating a Q value in a routing request data packet; (6) judging whether a current network node s is a destination network node or not, executing the step (7) if yes and otherwise, executing the step (4); (7) establishing positive routing information; (8) judging whether a routing reply data packet reaches the source network node or not, executing the step (9) if yes and otherwise, executing the step (7); (9) sending the data packet. According to the method, the combination of a fuzzy reasoning technology and a routing technology is realized, and the discount rate in a Q learning method is calculated according to the fuzzy reasoning and can be dynamically adjusted according to the network environment condition of the on-board network, so that the speed of establishing on-board network routing is accelerated.

The invention discloses a SOC estimation method for a lithium battery based on a state transition optimized RBF neural network. The method relates to the technical field of electric automobiles, wherein the method comprises the steps that: (1) offline training sample data are collected, normalization process is conducted on all training samples; (2) a SOC estimation model for a lithium battery based on a RBF neural network is established; (3) a STA optimization algorithm is adopted to optimize the established RBF neural network model; (4) the trained RBF neural network and each parameter are saved, the trained RBF network is used for conducting estimation on the SOC of a lithium iron phosphate battery; by means of the method, the SOC of the lithium battery is accurately estimated, the method has the advantages of high estimation precision, strong reliability, simple estimation model and the like, which can be widely applied in the technical field of power battery of the electric automobiles.

The invention discloses a routing method based on Q learning and a trust model in an Ad Hoc network, and mainly solves the problem of secure routing lookup in the Ad Hoc network. The routing method comprises the implementation steps that 1, a Q value table is generated; 2, the total number of adjacent nodes of each node is calculated; 3, each node evaluates trust values of all the nodes adjacent to the node; 4, trust awards are allocated; 5, instant awards are acquired; 6, aggregation awards are acquired; 7, Q values in the Q value table of the Ad Hoc network nodes are updated; 8, whether or not a current node in a routing request packet is a destination node is judged, if yes, the step 9 is executed, and otherwise, the step 6 is executed; 9, forward routing information is established; and10, data packets are sent. According to the routing method, combination of the trust model, a Q learning algorithm and a routing technology is achieved, a global optimal route can be dynamically looked up according to the environment of the Ad Hoc network, and the security and the stability of the network are effectively improved.

The invention discloses a Web API recommendation method based on topic model clustering. The method comprises the following steps: calculating semantic weight information of words according to context information so as to obtain a document-word semantic weight information matrix D; counting word co-occurrence information so that SPPMI matrix information is calculated; based on the obtained word frequency information matrix D of the words of the Mashup service document and the context SPPMI matrix M of the words, acquiring a word embedding information matrix by decomposing the M, combinding the two kinds of information , and calculating theme information of service; taking the obtained Mashup service theme features as spectral clustering input for clustering, segmenting a graph formed by all data points, wherein the sum of edge weights between different subgraphs after graph segmentation is made as low as possible, the sum of edge weights in the subgraphs is made as high as possible, and the clustering purpose is achieved; and combining GBDT and FM methods to predict and recommend the Web API service. Web API recommendation is effectively realized.

The invention discloses a transfer function model parameter recognition method and device based on an improved particle swarm algorithm. A coevolution idea and a Gaussian disturbance strategy are introduced into a basic particle swarm optimization algorithm, a hybrid algorithm is formed with a bat algorithm under a coevolution framework, and a Gaussian disturbance term is added in the optimizationprocess to form a hybrid coevolution Gaussian particle swarm optimization algorithm; sampling the input and output of the to-be-identified object model and the estimation model, and solving the standard deviation between the actual output value of the system and the output value of the estimation model at the moment k; feeding back the standard deviation to an HCGPSO algorithm to obtain an optimal result of the current parameter; and replacing the original value with the optimal value of the current model parameter, updating the estimation model, and sequentially iterating until the requirement of an output recognition criterion is met, thereby realizing parameter recognition of the transfer function model.

The invention relates to a combined running control method considering energy abandoning cost constraint for a sending-end power grid unit. The method comprises the following steps of 1) acquiring basic data; 2) building a unit combination optimization model, wherein the unit combination optimization model simultaneously considers energy abandoning constraint, peak adjustment constraint and frequency modulation constraint, and minimum sum of running cost, environmental cost and energy abandoning cost is used as a target function; and 3) solving the unit combination optimization model by an improved hybrid particle swarm optimization to obtain optimal output, and controlling a combined running sate of the unit according to the optimal output. Compared with the prior art, the method has theadvantages that the energy abandoning problem can be controlled from a running period, and the running efficiency is improved.

The invention provides a photovoltaic containing power distribution network voltage drop detection compensation method using UPQC implementation; the method comprises the following steps: 1, collecting voltage signals; 2, conditioning acquisition signals; 3, calculating a voltage drop compensation order; 4, using a multi-agent chaotic particle swarm optimization algorithm to set control parameters of a PI control unit; 5, using the set optimal PI control parameters to form and output PWM control signals; 6, outputting a compensation voltage so as to apply voltage compensation for the power distribution network. The method uses the multi-agent chaotic particle swarm optimization algorithm to set the PI control parameters of the UPQC, determines and uses the optimal PI control parameters to realize PI control, thus applying voltage compensation on the power distribution network, improving the UPQC voltage drop detection and compensation application precision and accuracy, and providing a novel method using the UPQC to manage the electric energy quality of the photovoltaic containing power distribution network.

A fast image segmentation algorithm based on artificial bee colony optimization fuzzy clustering is proposed and optimizes the sensitivity of a traditional FCM algorithm to the initialization of clustering centers by using the intelligent behavior of bee colony in nature. The algorithm starts with bees looking for food sources. An improved fitness function value which is described in the description is used to express the nectar content of the food source, according to the greedy algorithm, the old and new food sources are selected. After the bee finishes searching, the information is transmitted to the follower bee. The bee chooses a food source according to the probability P related to the nectar amount, and at the same time, the bee searches the neighborhood near the food source. When the nectar amount is not improved after limited searches in the vicinity of a food source, the nectar source is abandoned, and the bees associated with the food source are replaced by scouting bees toindependently and randomly search for the nectar source, and the location of each food source represents a possible solution of the optimal clustering center of the image to be segmented.

The invention discloses a new photovoltaic power prediction method based on AFSA-Elman. Firstly, the method decomposes the collected original power into low frequency trend component and high frequency detail component by wavelet decomposition, then forecasts the low frequency trend component and high frequency detail component with the collected weather data as the input of the model respectively, and finally reconstructs the corresponding forecasted value to obtain the final power forecasted value. An artificial fish swarm algorithm is used to optimize the weights and thresholds of Elman neural network, and the optimized Elman is applied to the short-term prediction of photovoltaic output power. The method can predict the output power of the next time according to the historical weatherand the output power of the photovoltaic station and the weather condition in a short period of time, and the prediction model can utilize the autocorrelation existing in the historical data without using a plurality of complex and tedious physical formulas for modeling.

The invention provides a novel codebook design method based on an ant colony clustering and genetic algorithm. The method includes the following steps: step one, training data are distributed in two-dimensional spaces of different dimensions randomly by using ants of different numbers, and an LF algorithm is adopted to perform generation of initial clustering; step two, clustering correction performed by the initial clustering according to the dimension of a codebook guarantees that the clustering number is the same as initial setup codebook dimensions; step three, under the premise that initial population is obtained successfully, individual selection, intersection and mutation operation are performed according to the basic procedure of a genetic algorithm until iteration is stopped and an optimal individual meeting requirements is obtained. The novel codebook design method based on the ant colony clustering and genetic algorithm overcomes the defect that correlation of initial selection and final design results is strong in an LBG algorithm, meanwhile, prevents similar LBG algorithm from getting into the inferior position of local optimum, and is suitable for the fields of large quantity processing, voice communication, mode recognition, internet protocol (IP) telephony and the like.

The invention discloses a taboo searching method for selection of galvanic skin response signal features, which includes the following steps: sequence backward algorithm is adopted to form N-1 rows and a two-dimensional table L of N lines, wherein the N represents total number of dimensions of selected features, each line represents one feature, each row is called one space, an nth space selects n features, and n is larger than or equal to 1 and less than or equal to N-1; a value of each element in the table is expressed by 0 or 1, 0 represents that the element is not selected when feature selection is performed, and 1 represents that the element is selected; the selected features in each space are solved by adopting taboo searching algorithm to obtain a table S formed by solution of each space; and a feature with the largest fitness function in each space serves as a final feature selection result. The taboo searching method for the selection of galvanic skin response signal features not only obtains effective features of skin electric signals with large contribution to emotion recognition, but also overcomes the shortcoming that a basic taboo searching algorithm is easy to get into local optimum.

The invention discloses a simulated annealing optimization neural network-based visibility sensor and a detection method thereof, wherein the visibility sensor comprises a microprocessor module and apower supply circuit for supplying power for each electricity utilization module in the visibility sensor; the input end of the microprocessor module is connected with a visibility detection circuit;the visibility detection circuit comprises a particle concentration sensor and a temperature and humidity sensor; the microprocessor module, the particle concentration sensor and the temperature and humidity sensor are all connected with the output end of the power supply circuit. The detection method comprises the following steps of 1, performing data acquiring and transmitting; 2, carrying out data preprocessing; and 3, carrying out data processing to obtain a visibility detection value. The device is novel and reasonable in design, convenient to implement, low in cost and high in visibilitydetection precision, can be well applied to visibility detection, is high in practicability, good in use effect and convenient to popularize and use.

The invention discloses a hybrid reservoir group flood control optimization scheduling scheme generation method based on an energy criterion. The method comprises the following steps: selecting a hybrid reservoir group and a corresponding downstream protection object; performing particle swarm coding; setting constraint conditions of each reservoir in the series-parallel reservoir group; calculating the fitness value of each particle in the first generation particle swarm; carrying out iterative circulation and updating particles by adopting a particle swarm algorithm based on an attenuation cosine curve inertia weight; and calculating the fitness value of each particle in the new-generation particle swarm until an optimization condition is met, and outputting a flood control optimization scheduling scheme of the hybrid reservoir group. According to the method, the particle swarm optimization algorithm based on the attenuation cosine curve inertia weight is adopted, the defect that an existing particle swarm optimization algorithm is prone to falling into local optimum is overcome, the global and local search capacity is enhanced, the reservoir group flood control optimization scheduling scheme is finally obtained, under the condition that downstream safety is guaranteed, the flow called by a flood control reservoir group is greatly reduced, and the flood control risk is greatly reduced.

The invention discloses an image registering method in a real number coding based clonal selection algorithm, and mainly solves the problem that the image registering accuracy is not high due to the fact that function optimization tends to local optimization in the prior art. The method comprises the steps that (1) a reference image and a floating image are input; (2) an antibody population is initialized in a real number coding method; (3) a normalized mutual information function is constructed and serves as an objective function; (4) the affinity of population antibodies is calculated; (5) selection, clone and variation are carried out on the antibodies; (6) a memory antibody set is formed; (7) the antibodies are memorized; (8) whether a termination condition is reached is determined, if no, the step (4) is returned to, and otherwise, a step (9) is turned to; and (9) an image is registered by utilizing the optimal antibody, and a result is output. The mutual information function is optimized in the real number coding based clonal selection algorithm, the problem that function optimization tends to local optimization in the prior art is solved, and the image registering accuracy is improved.

The invention provides a chrominance space transformation method. According to the method, a re-assigned particle swarm optimization (RPSO) algorithm is used for correcting weights and threshold values of a back propagation (BP) neural network, the learning ability and the searching speed of the neural network are improved, and the generalization ability of the BP neural network is improved; a PSO algorithm is improved, the defect that the algorithm is trapped into local optimization easily is overcome, and the searching speed is accelerated; red-green-blue (RGB) color space values are converted into device-independent Lab color space values through the improved RPSO-BP neural network, and the average conversion accuracy and the conversion speed are improved.

The present invention discloses a visual tracking method based on quantum particle swarm optimization. The method comprises the steps of performing random propagation on an individual optimal state of particles of the previous frame by utilizing state transition distribution in a current image; carrying out quantum particle swarm optimization iteration on the particles after the random propagation; calculating fitness values of the particles by utilizing appearance models based on mixture Gaussian; updating the individual optimal state and a group optimal state of the particles according to the fitness values; and performing convergence theorem where an observed value corresponding to the group optimal state serves as a tracking result of the current image if convergence conditions are met while quantum particle swarm optimization iteration continues if convergence conditions are not met. The visual tracking method provided by the present invention helps to realize effective visual tracking and has excellent robustness.

The invention discloses a photovoltaic inverter low voltage ride through detection method and system. The method comprises a voltage signal acquisition step, a Clarke step of a photovoltaic inverter,a microcontroller detection step of the photovoltaic inverter, specifically, setting PI control parameters of a phase-locked loop of an inverter by adopting a multi-agent chaotic particle swarm optimization algorithm, controlling the phase-locked loop by adopting the PI control parameters and repetitive control when a global optimal point of the system is reached, and detecting low-voltage ride through of the inverter, a step of calculating a symmetrical three-phase voltage drop value, subtracting a reference voltage udref from a d-axis component u'd obtained by Park transformation to obtain acorresponding value, and performing dq / abc inverse transformation of the corresponding value to obtain symmetrical three-phase voltage drop values uac, ubc and ucc, and an output step. The method isadvantaged in that tracking control of the phase-locked loop is carried out by using PI control and repetitive control in the photovoltaic inverter, PI control parameters are set by using an intelligent agent chaotic particle swarm optimization method, so detection accuracy and robustness are improved.

The invention discloses an image segmentation method based on genetic rough set C-mean clustering, which mainly solves the problem that the conventional method has poor robustness, easily falls into local optimum and loses too much local information. The method comprises the implementation steps of: (1) inputting a to-be-segmented image; (2) extracting image texture features; (3) generating clustering object data; (4) initializing population; (5) updating membership; (6) dividing the clustering object data; (7) updating the population; (8) calculating an individual fitness value; (9) evolving the population; (10) judging whether a termination condition is satisfied; (11) generating an optimal individual; (12) marking; (13) generating segmented images. In the method, the texture features of each pixel of the image are extracted, and the texture features are marked through the C-mean clustering method based on the genetic algorithm and the thought of rough set so as to divide the pixels, thus, stability of image segmentation is improved, and more accurate image segmentation result is obtained.

The invention discloses an electric power system economic dispatching method based on a quantum beetle antennae algorithm. The method comprises the following steps: (1) initializing the position and initial value of beetle antennae; (2) introducing a search mechanism of quantum behaviors into the beetle antennae algorithm, and establishing Delta quantum potential wells around the beetle antennae position to form a quantum beetle antennae algorithm; (3) establishing an economic dispatching model of the power system by taking the minimum total power generation power value of the generator set as a target function; and (4) optimizing the system economic dispatching model on the basis of a quantum beetle antennae algorithm under constraint conditions, and calculating the combination of the minimum power generation cost and the minimum pollutant emission of the generator set by using an optimal solution. According to the method, a search mechanism of quantum behaviors is introduced into the beetle antennae algorithm, the quantum Delta potential well is established around the beetle antennae position, the probability and uncertainty of the quantum theory are utilized, the convergence speed is effectively increased, the defect that a traditional search method is prone to falling into local optimum is overcome, and the accuracy of a search result is improved.

The invention relates to a lower limb movement recognition method and system based on surface electromyogram signals, and the method comprises the following steps: 1, collecting the surface electromyogram signals in different movement modes, carrying out the preprocessing, and extracting the time domain, frequency domain and nonlinear features; 2, performing hybrid optimization on penalty parameters and kernel parameters of the kernel extreme learning machine by using an artificial bee colony algorithm-sparrow search algorithm to obtain an optimal kernel extreme learning machine classifier; and step 3, carrying out identification by using the optimized classifier. The method and the system are beneficial to improving the accuracy of lower limb motion mode recognition.

The invention discloses a two-stage power distribution network planning method based on a genetic algorithm, and belongs to the technical field of power distribution network frame planning. The implementation method comprises the following steps of optimizing a solution obtained by the first-stage genetic algorithm by adopting a second-stage dynamic adjustment iteration mode; optimizing an optimization scheme obtained by the first-stage genetic algorithm; obtaining effective information by fully utilizing a genetic algorithm; grading a to-be-planned line according to the occurrence frequency of the to-be-planned line in the initial scheme; providing correct criteria for edge adding and deleting operation in the second stage, regarding connected to-be-planned line lines as sub-trees, carrying out unified operation in the dynamic adjustment process such that excellent sub-trees can be found and stored in the algorithm in the second-stage dynamic adjustment process, the searching efficiency is improved, and a power distribution network planning scheme with lower cost is obtained. According to the method, the defect that an existing power distribution network planning method is prone to falling into local optimum can be overcome, and optimal power distribution network planning meeting constraint conditions is achieved on the basis that the search efficiency is improved.

The invention discloses a multidimensional collaborative power grid planning method which includes the processes: acquiring geographic environmental information data and calculating comprehensive construction cost data by a rasterized map; building a power grid planning model; and calculating the solution of a power grid planning scheme by an optimal ant colony algorithm according to the acquired data and the power grid planning model. The method includes the steps: comprehensively considering substation locating and sizing, line selection and line corridor selection; and building a multidimensional collaborative power grid planning model considering complicated environmental influence. The multidimensional collaborative power grid planning method can accurately consider the influence of environmental factors on power grid planning. Line construction cost calculation precision is improved, and estimation difficulty is reduced. Catalytic elements are generated according to deviation of the local movement direction and the target movement direction of ants, so that the state transition rule of the ants is improved. The construction cost of lines on a mass raster and a substation is full-automatically and rapidly calculated, and calculation precision and efficiency are remarkably improved.

The invention discloses an energy criterion-based method for generating a flood control optimization dispatch plan for a mixed reservoir group, the method comprising: selecting a mixed reservoir group and corresponding downstream protection objects; performing particle swarm coding; and setting each reservoir in the mixed reservoir group Constraint conditions; Calculate the fitness value of each particle in the first generation of particle swarm; Use the particle swarm algorithm based on the inertia weight of the decaying cosine curve to iteratively cycle and update the particles; Calculate the fitness value of each particle in the new generation of particle swarm until When the optimization conditions are satisfied, the flood control optimal dispatching scheme of the mixed reservoir group is output. The present invention adopts the particle swarm algorithm based on the inertia weight of the decaying cosine curve, which overcomes the disadvantage that the existing particle swarm algorithm is easy to fall into the local optimum, and enhances the global and local search capabilities. Under the condition of ensuring the safety of the downstream, the flow of the flood control reservoir group is greatly reduced, and the risk of flood control is greatly reduced.

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.