基于T-S模糊自适应扰动粒子群优化和神经网络的优化算法

Wang Jianfang, L. Weihua
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引用次数: 1

摘要

解决机械设备的模糊和非线性特性。将自适应扰动粒子群的扩展T-S模糊模型与BP神经网络算法相结合,提出了一种新的计算智能方法。首先对T-S模糊模型进行修正,然后利用扩展后的T-S模型对PSO参数进行调整。其次,采用改进的粒子群算法对神经网络进行训练;最后,对网络模型进行了优化验证,试验结果表明,该网络模型保证了轮盘的性能,同时轮盘结构得到了明显的优化,是一种可行的结构优化方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization Algorithm Based on T-S Fuzzy Model of Self-Adaptive Disturbed Particle Swarm Optimization and Neural Network
To solve fuzzy and non-linear features of mechanical equipment. A new computational intelligence method was proposed by combing based on extended T-S fuzzy model of self-adaptive disturbed PSO and BP neural network algorithm. Firstly, the T-S fuzzy model is modified, and then uses the extended T-S model to adjust the PSO parameter. Secondly, the neural network is trained by the modified PSO algorithm. Finally, a wheel disc model is optimized to check that network model, the test results show that it guarantees the performance of the wheel disc, meanwhile the wheel disc structure is obviously optimized, and the algorithm in the paper is a method of viable structure optimization.
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