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Overhead crane neural network modeling method based on RNA genetic algorithm of hairpin mutation operation

A neural network modeling and neural network model technology, applied in the field of bridge crane neural network modeling, can solve the problem of weak local optimization ability and so on

Inactive Publication Date: 2016-02-03
ZHEJIANG UNIV
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  • Claims
  • Application Information

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Problems solved by technology

The genetic algorithm has strong global optimization ability, but weak local optimization ability, and is prone to premature convergence

Method used

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  • Overhead crane neural network modeling method based on RNA genetic algorithm of hairpin mutation operation
  • Overhead crane neural network modeling method based on RNA genetic algorithm of hairpin mutation operation
  • Overhead crane neural network modeling method based on RNA genetic algorithm of hairpin mutation operation

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Embodiment

[0109] Below, the present invention is further explained with the "three-dimensional bridge crane experimental platform" of a certain university robotics and automation institute as an example.

[0110] In this embodiment, the bridge crane neural network modeling method of hairpin mutation operation RNA genetic algorithm comprises the following steps:

[0111] Step 1: Through the "three-dimensional bridge crane experimental platform" (when only x, θ x As a state quantity, the platform can be simplified as a two-dimensional bridge crane system in the x direction, see figure 2 (a) Schematic diagram of the crane system) Obtain the horizontal direction control input f of the two-dimensional bridge crane system x , the position x in the horizontal direction and the swing angle θ x Output sampled data. The parameters of the bridge crane are set as trolley mass M = 6.5kg, load mass The length of the sling is fixed l=1m, the acceleration of gravity g=9.8m / s 2 . In the process ...

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Abstract

The invention discloses an overhead crane neural network modeling method based on an RNA genetic algorithm of a hairpin mutation operation, and belongs to the field of intelligent modeling. An overhead crane is a highly non-linear under-actuated complicated system, and the establishment of a high-precision overhead crane system model is a crucial foundation for achieving the purpose of effective control. The invention, for the problem in overhead crane modeling precision, provides overhead crane nonlinear regression models based on a position and an angle of an RBF (Radial Basis Function) neural network. Under the inspiration of a hairpin structure, the RNA genetic algorithm of the hairpin mutation operation is abstracted, and the RBF center of the RBF neural network for the position and the swing angle of the overhead crane is optimized by running the RNA genetic algorithm of the hairpin mutation operation, so that a neural network model of the overhead crane is obtained. The modeling method has the characteristic of high modeling precision, and is also suitable for modeling other complicated nonlinear systems.

Description

technical field [0001] The invention relates to the technical field of intelligent optimization modeling, in particular to a bridge crane neural network modeling method of hairpin mutation operation RNA genetic algorithm. Background technique [0002] Cranes are large-scale engineering handling equipment that play a pivotal role in national economic construction. Among all types of cranes, bridge cranes are the most representative. The main task of the bridge crane is to realize the fast, accurate and non-residual delivery of goods. Due to the under-actuated characteristics of the crane system, the movement and interference of the trolley will cause the load to swing and reduce the working efficiency of the crane system. At the same time, it may cause the load to collide with the operator or other objects and cause losses. Therefore, the overhead crane must be effectively controlled. To achieve this purpose, the establishment of a high-precision overhead crane system mode...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06N3/02
Inventor 朱笑花王宁
Owner ZHEJIANG UNIV
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