An Optimal Design Method for Flexibility Factors

Abstract

The invention relates to the technical field of flexible factor optimization, in particular to a calculation method of a novel BP neural network and a flexible factor optimal design method thereof. A calculation method of a new type of BP neural network, including steps to increase the momentum item and adjust the learning rate of the network, a method based on the calculation method of the new type of BP neural network for optimal design of flexible factors, including steps of simulation modeling and analysis, data Acquisition and database establishment, database import and actual effect verification. The computing power and generalization performance of the new BP neural network are verified by using multiple sets of sample pairs different from the training set as test samples. The vibration information in the test set samples is used as the input of the BP neural network, and then through the trained BP neural network. The neural network model calculates the vibration information of the rigid body structure with the addition of flexibility factors, and finally obtains relatively good calculation results.

Description

technical field [0001] The invention relates to the technical field of flexibility factor optimization, in particular to a method for optimizing design of flexibility factors based on a calculation method of a novel BP neural network. Background technique [0002] Artificial neural network (Artifical Neural Network, ANN), referred to as neural network, its fundamental purpose is to realize the intelligence of the computer, so that the computer can quickly learn and memorize new knowledge and new laws. Its main research is how to allow computers to simulate and realize human self-learning and thinking ability, and to mine real objects and potential connections between data from limited samples. Neural network is an important branch in the field of artificial intelligence. He mainly deduces and reveals the hidden relationship between variables in unknown data samples by learning and storing potential data relationships, inference rules, probability distributions and other inf...

AI Technical Summary

Problems solved by technology

[0009] (1) The number of training times lacks on-site theoretical basis, and too many training times will reduce the learning efficiency of the algorithm and affect the network convergence speed;

[0010] (2) It is easy to form multiple minimum values, and the error surface is easy to generate flat areas

[0011] (3) There is no theoretical basis for the number of neurons in the hidden layer. At present, it mainly depends on the designer's experience or repeated operation and parameter adjustment, which affects work efficiency.

This design method often lacks actual data and experiments as the basis and support

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