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| Decentralized control of building structure based on neural network algorithm |
| Received:August 07, 2020 Revised:September 08, 2020 |
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| DOI：10.7511/jslx20200807001 |
| KeyWord:decentralized control earthquake action neural network radical basis function |
| Author | Institution |
| 汪权 |
合肥工业大学 土木与水利工程学院, 合肥 ;土木工程防灾减灾安徽省工程技术研究中心, 合肥 |
| 王文 |
合肥工业大学 土木与水利工程学院, 合肥 |
| 韩新节 |
合肥工业大学 土木与水利工程学院, 合肥 |
| 韩强强 |
合肥工业大学 土木与水利工程学院, 合肥 |
| 周超杰 |
合肥工业大学 土木与水利工程学院, 合肥 |
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| Abstract: |
| Aiming at decentralized vibration control of structures under an earthquake, a neural network algorithm is introduced to study the decentralized neural network control strategy of structural vibration, so as to solve the coupling problem of individual subsystems in the decentralized control and reduce the training cost of the neural network algorithm.Employing the Radial Basis Function (RBF) neural network model, an RBF neural network controller is formed on the basis of the newrb function.And a 20-layer Benchmark structure model is respectively tested by centralized control and multi-condition subsystems-division decentralized control, the data of which is later processed by numerical simulation analysis.The simulation analysis shows that the decentralized RBF neural network vibration control strategy for the coupling of individual subsystem herein takes into account the information sharing between the subsystems, which can effectively control the vibration response of the structure and rationalize the training frequency required for the subsystems to achieve the ideal training result.Compared with that in BP network, the required frequency is significantly reduced. |
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