13results about How to "Improve convergence speed" patented technology

The invention provides a control method and a control device of an underwater robot. The control method of the underwater robot comprises the following steps: establishing an underwater robot system model; performing obstacle avoidance path planning by using a sampling algorithm to obtain a discrete geometric path point set connecting the starting point and the ending point; a full-state reference sequence matched with the control method underwater robot system model is generated based on the control method discrete geometric path point set; constructing a cost function for trajectory optimization based on the control method underwater robot system model and the control method full-state reference sequence; generating an initial control sequence and an initial state sequence according to the deviation between the current state of the underwater robot in the control method and the full-state reference sequence in the control method; and performing iterative optimization by taking the control method cost function as an optimization target and taking the initial control sequence and the initial state sequence of the control method as hot start initial values to obtain a control instruction sequence of the underwater robot of the control method.

The invention relates to the technical field of active noise control, and provides a binaural cooperative active noise control method based on a combined cost function. The objective of the invention is to solve the problems of asynchronous convergence rates of left and right ears and inconsistent steady-state errors caused by factors such as physiological structure difference of left and right ear canals, inconsistent wearing fitness of earphones and asymmetrical directivity of loudspeakers. According to the method, a combined cost function including an overall error term, an instantaneous error term and an accumulative error term is constructed, the contribution proportion of each error term is automatically adjusted through a dynamic weight adjustment mechanism, and the weight of a control filter is updated based on a gradient descent method, so that the convergence speed and the steady-state error of a left sound channel and a right sound channel tend to be consistent. The consistency and balance of the binaural active noise control performance can be improved, and the method can be suitable for application scenes such as active noise reduction earphones and intelligent cabins.

The application discloses a biological environment text named entity recognition method, medium, equipment and product, relates to the biological environment entity recognition technical field, and the method comprises the following steps: acquiring a biological environment text, converting the biological environment text into training samples and test samples; inputting the training samples into a fine-tuning language model, optimizing the fine-tuning language model by using a proximal policy optimization algorithm, calculating a reward function by using the cosine similarity of the embedding vector of the input sample, calculating an advantage function by using a dynamic discount factor and a dynamic balance factor, and obtaining a trained language model; inputting the test sample into the trained language model, testing the trained language model, and using the tested language model to perform named entity recognition on the biological environment text. The application can optimize the update stability of the model and the adjustment of the model parameters.

This invention provides a method for predicting the dominant frequency of blasting vibration by integrating parameter fluctuation processing and the Dream Optimization Algorithm (DOA), belonging to the field of underground engineering safety control technology. The system includes a data acquisition and uncertainty modeling module, a data preprocessing and feature construction module, a hyperparameter optimization module, and a prediction model training and result output module. The method obtains relevant parameters through on-site investigation and monitoring, and generates an extended sample set using a combination of probabilistic perturbation modeling and fuzzy triangular modeling. The data is preprocessed and features are selected. The DOA algorithm is used to globally search the hyperparameters of the Support Vector Regression (SVR) model, and the optimal hyperparameter combination is selected by combining a robustness fitness function. The SVR model is trained, and prediction results and uncertainty prediction intervals are generated. This invention can explicitly characterize the uncertainty of geological parameters, achieve efficient global optimization of hyperparameters, improve prediction accuracy, robustness, and generalization ability, and is applicable to different geological conditions and blasting scenarios. It can be extended to various blasting dynamic response prediction tasks.

The invention discloses a path planning method and system based on an improved self-adaptive ant colony algorithm, and the method comprises the following steps: initializing, loading traveling salesman problem instance data of a plurality of places based on basic parameters and dynamic parameters of the improved self-adaptive ant colony algorithm, constructing a distance matrix between the places based on Euclidean distance, and performing path planning on the distance matrix. Initializing a symmetric pheromone matrix; iterative optimization is carried out, each ant starts from a randomly selected initial site, a complete path is constructed step by step through an improved state transition probability rule, the path length of each ant is calculated, and the current iterative optimal path and the length thereof are recorded; iteration is terminated, and when the number of iterations reaches the preset value, the algorithm stops running; according to the method, statistics is carried out on results of multiple rounds of operation, the average optimal distance of the multiple rounds of operation and the minimum distance in the multiple rounds of operation are obtained, namely, the optimal planning path is obtained, a self-adaptive volatilization mechanism is introduced, the pheromone volatilization rate is dynamically adjusted according to the number of iterations, and the local optimum problem is solved.

The application discloses a pipeline leakage signal denoising method based on improved WOA-VMD combined with SVD, relates to the technical field of signal processing, and acquires a pipeline leakage signal of a pipeline defect position; a penalty factor alpha and a mode number K in a VMD algorithm are used as optimization targets of a whale optimization algorithm (WOA) algorithm; the VMD algorithm is set according to the optimal penalty factor alpha and the optimal mode number K obtained by the WOA algorithm, and VDM decomposition is carried out on the pipeline leakage signal to obtain K IMF components; the K IMF components are identified by using a correlation coefficient method, and effective IMF components are identified; SVD decomposition and denoising processing are respectively carried out on each effective IMF component by using an SVD algorithm, and noise signals in each effective IMF component are removed; and each effective IMF component after the denoising processing is synthesized to obtain a pipeline leakage signal after denoising. The application combines the WOA, VMD and SVD algorithms, forms an integrated signal processing framework, and has a significant improvement in performance compared with a single method.

This invention belongs to the field of sliding mode control technology and relates to a sliding mode control method based on a variable exponent double power-law reaching law. Addressing the slow convergence speed of traditional double power-law reaching laws, this invention establishes a mathematical model of a permanent magnet synchronous motor (PMSM): the voltage equation of the PMSM in the three-phase coordinate system is converted into a voltage equation with the rotor coordinate axis as the reference frame; an adaptive variable gain double power-law reaching law is designed; a sliding mode disturbance observer is designed based on the adaptive variable gain double power-law reaching law to observe sliding mode disturbances; a speed loop controller is designed based on the novel variable gain reaching law and sliding mode disturbances, and the PMSM system is controlled through the speed loop controller. Compared with the traditional double power-law reaching law, the variable gain double power-law reaching law of this invention has a superior convergence speed across the entire domain, and the proposed variable gain control strategy does not cause chattering, thereby improving the stability of control performance.

The application provides a strategy generation method for combined heat and power based on a generalized Nash equilibrium algorithm, comprising the following steps: constructing a combined heat and power system under related constraints; wherein the upper limit of the thermal comfort of each consumer and the relationship between the consumer utility and energy consumption are considered; the problem to be solved by each company is determined by considering the decision information of all energy companies; wherein the objective function of the combined heat and power system model is determined according to the upper limit and the lower limit of the production efficiency of each company, the production plan of the company which needs to follow the power balance and the thermal balance, and the problem to be solved by each company; when the global decision information of other companies is obtained, the decision of each company is obtained by solving based on the edge-based distributed generalized Nash equilibrium algorithm under the global decision information, including the demand and the price of thermal energy and electric energy in each round of auction.

The present application relates to a flowered stereo calibration box and a camera calibration method based on an OCAM algorithm, wherein the flowered stereo calibration box comprises five checkerboards, each checkerboard is a regular row and column distributed black and white checkerboard, the bottom checkerboard is a square, the four side checkerboards are rectangles, the four side checkerboards are connected with one bottom side of the bottom checkerboard respectively, the four side checkerboards are equal to the included angle of the bottom checkerboard, and the four side checkerboards are divergent and inclined upward with the bottom checkerboard as the center; the checkerboard small grid of the bottom checkerboard is provided with a closed black frame, the black frame does not intersect with the checkerboard small grid; the three sides of the four side checkerboards which do not intersect with the bottom side of the bottom checkerboard are provided with black frames, the black frames are connected with the checkerboard small grid of the side checkerboard; and the bottom checkerboard is provided with four circular calibration points. The present application reduces the requirement for image quality, and improves the operation precision and operation efficiency of panoramic camera calibration.

The invention relates to a DDCPN-based time delay precision balance optimization method in vehicle infrastructure cooperative infrastructure perception, which belongs to the field of Internet of Vehicles and comprises the following steps: constructing an Internet of Vehicles system consisting of a plurality of vehicles and a single RSU; establishing an optimization problem to minimize the total delay of all task vehicles completing the perception task within the duration; modeling the optimization problem as a Markov decision process MDP; and task unloading and resource allocation optimization are carried out based on a discrete drive continuous policy network DDCPN algorithm. The algorithm has good convergence efficiency. Simulation results show that the algorithm has a faster convergence rate under the constraint conditions of meeting fast reasoning precision, energy consumption, computing resource utilization rate and the like, effectively reduces task execution delay, and better adapts to a dynamic and complex vehicle-mounted network environment.

The application belongs to the technical field of nonlinear system control, and discloses a method and system for introducing composite learning into nonlinear system output feedback adaptive control. The application directly reuses the existing K-filter in standard output feedback backstepping control to construct an extended prediction error, and does not need to establish an additional observer or a state estimation model (such as a series-parallel estimation model, a fuzzy observer, etc.) in parallel with the K-filter. This functional reuse design eliminates the calculation overhead of the additional dynamic system, reduces the storage requirement and real-time calculation burden of the controller, and significantly reduces the hardware implementation cost and system debugging complexity. Meanwhile, through the composite driving of the tracking error and the extended prediction error, the cumulative information is continuously introduced by using the historical memory of the regression quantity, so that the adaptive and self-learning ability of the control system is significantly enhanced, and the parameter estimation convergence and the system response performance are improved.

The invention relates to a cloud computing task scheduling method and system oriented to energy consumption and service quality optimization, and belongs to the technical field of cloud computing. According to the method, the tasks and the resources in the cloud computing environment are comprehensively sorted by establishing a dual evaluation model of the task priority and the virtual machine priority. The method comprises the following steps: firstly, calculating the load and processing capacity of a virtual machine, and determining the priority of the virtual machine in combination with the power unit price of a data center; and then calculating the task priority according to the task scale, the execution time and the complexity. Based on the two priority results, a multi-objective optimization mechanism is adopted to carry out comprehensive modeling on the task complete time, the energy consumption and the SLA default rate, and a global fitness function is constructed. And finally, performing global search and dynamic optimization on the scheduling scheme by utilizing a swarm intelligence algorithm, so that the system realizes the lowest energy consumption and the maximum task execution efficiency on the premise of meeting the service quality requirement.

The application discloses a free-form surface adaptive measurement device and method based on Fourier spot analysis, in particular to a free-form surface adaptive zero-position interferometer device for large surface shape deviation, and belongs to the field of optical detection. The device comprises a light source system, an adaptive compensation system, an interference system and a surface to be measured. The liquid crystal spatial light modulator is used for guiding aberration compensation of the adaptive element through analysis of Fourier transform spots of the system, and the dependence on an interference pattern is broken, so that the measurement range of the traditional adaptive interferometer is greatly expanded, and destructive modification of a traditional interferometer light path is not needed.