5 results about "Quasi-Newton method" patented technology

The application belongs to the technical field of concrete strength detection, and relates to a micro core sample method strength conversion method and system for UHPC. The method comprises the following steps: preparing a plurality of different micro core sample samples and pouring a plurality of standard test piece samples matched with the micro core sample samples; performing a compression test on all the micro core sample samples and the standard test piece samples to calculate a measured conversion coefficient; constructing a mathematical model of a predicted conversion coefficient, determining a parameter to be calibrated and a value range of the parameter, constructing an objective function, performing parameter inversion by using a differential evolution algorithm and a limited memory quasi-Newton method, determining a calibrated value of the parameter, and updating the mathematical model of the predicted conversion coefficient; taking a micro core sample from a concrete entity structure on site, performing a compression test on the micro core sample, calculating the predicted conversion coefficient, and then calculating a standard test piece compression strength. The application can accurately establish a conversion relationship between the micro core sample compression strength and the standard test piece compression strength, and improve the strength detection precision of the UHPC entity structure.

This invention discloses a DC motor speed control identification method and system based on an improved quasi-Newton method, belonging to the technical field of DC motor speed control system modeling. First, a time-varying nonlinear DC motor speed control system is constructed, and an identification model of this system is obtained. Then, an identification method flow based on the quasi-Newton method under step-size line search is constructed. The optimal step size is obtained through step-size line search for parameter estimation, and a correction strategy is designed to ensure the positive definiteness of the approximation of the Hessian matrix inverse. This invention avoids the oscillation or slow convergence problems caused by a fixed step size by dynamically adjusting the step-size factor, and simultaneously introduces a correction strategy to forcibly guarantee the positive definiteness of the approximation of the Hessian matrix inverse, thereby obtaining more accurate motor model parameters.

The application discloses a material process parameter self-adaptive optimization method and system based on dynamic Bayesian optimization, and belongs to the field of artificial intelligence assisted material research and development. In view of the problems of high cost and easy falling into local optimization under complex phase change in traditional material research and development, the application constructs an initial set by acquiring material historical process and performance data; a nonlinear proxy model is constructed by using Gaussian process regression and a composite kernel function fusing smooth and rough priori; a function acquisition strategy is dynamically switched according to a prediction variance to balance global exploration and local utilization; within the extracted physical equipment hard constraint boundary, a multi-start quasi-Newton method is used to perform global optimization, and the next round of recommended experimental parameters are output and closed-loop iteration is supported. The application realizes accurate optimization of multi-dimensional parameters with few experimental times, and is mainly used for complex chemical and material process parameter optimization such as polymerization and alloy smelting.

This invention provides a robot trajectory planning method based on second-derivative regularization, comprising: parameterizing the joint space path with path parameters, establishing a joint velocity and acceleration constraint model, and obtaining the original path velocity square distribution and feasible region based on the TOPPRA algorithm; designing a second-derivative regularized objective function in the path velocity square space, including a data fidelity term and a curvature penalty term; constructing a bounded constraint optimization model based on the upper and lower bounds of the feasible region and the values ​​of the first and last ends, and analytically deriving the gradient of the objective function; solving the optimization model using a bounded constraint quasi-Newton method, restoring the obtained optimal path velocity square to a smooth path velocity, recovering the kinematic time series of each joint, and completing the trajectory smoothing planning. This invention can effectively suppress sharp abrupt changes in joint acceleration, achieving robot trajectory planning that balances time efficiency and motion stability.

The application discloses a power flow calculation method and system of AC / DC power system based on quasi-Newton method, establishes a power flow calculation model of conventional DC converter station and flexible DC converter station based on input and output characteristics of AC side and DC side; extends the rectangular coordinate equation of only considering node voltage in traditional power flow calculation to the augmented rectangular coordinate system considering node voltage and current, and establishes a unified power flow calculation model of AC / DC power flow calculation; establishes the direction and step length of the first iteration through Newton method, and obtains the initial value of quasi-Newton iteration; continuously updates the quasi-Newton iteration matrix in the iteration process, so that the calculation of Jacobian matrix and inversion is not required repeatedly; when the power flow solution iteration meets the requirements, the solution of power flow calculation is output. Through the calculation method, the unified iteration of AC / DC power flow can be realized by using the unified power flow model of AC / DC, meanwhile, the quasi-Newton method is adopted to save the time required by each iteration, and has the characteristics of super-linear convergence.