A bridge crane neural network modeling method with object age feature film calculation

Abstract

The invention discloses a bridge crane neural network modeling method with object age feature film calculation. The method comprises the following steps: 1) obtaining input and output sampling data inthe operation process of the bridge crane, and establishing an RBF neural network model of the bridge crane; 2) respectively taking the quadratic sum of the difference between the estimated output ofthe position and the swing angle of the bridge crane in the operation process and the actual output sampling data as an objective function; 3) under the inspiration of biological cell membranes and intracellular substance aging phenomena, abstracting a bridge crane neural network modeling method with object age characteristic membrane calculation; 4) setting algorithm operation parameters; And 5)estimating unknown parameters in the bridge crane RBF neural network model by operating an object age feature film calculation optimization method, obtaining unknown parameter estimation values in the model by minimizing an objective function, and substituting the estimation values into the bridge crane RBF neural network model to form a nonlinear model. The modeling method disclosed by the invention has the characteristics of early maturing resistance, high local search precision and fast convergence.

Description

technical field [0001] The invention relates to a bridge crane neural network modeling method with object age characteristic film calculation. Background technique [0002] The bridge crane is a widely used assembly transportation tool, the key to its work is to realize the accurate, fast and non-swing-free delivery of goods. However, since the dimension of the control quantity of the overhead crane is less than the degree of freedom of the controlled quantity, it belongs to a nonlinear underactuated system. When the trolley is moving, it will be disturbed by friction and wind, which will cause the goods to swing, and there is an operation Low efficiency, poor positioning accuracy, low safety factor and other shortcomings. Therefore, the overhead crane must be controlled safely and effectively. To achieve this goal, the key lies in the establishment of a high-precision overhead crane model. [0003] Researchers have achieved a lot of results by using the mechanism modeling...

AI Technical Summary

Problems solved by technology

[0005] For engineering optimization problems with characteristics such as nonlinearity, constraints, and complexity, traditional optimization methods tend to fall into local optimum or even fail to obtain the optimal value, which makes the biologically inspired intelligent optimization method get people's attention

Images