| 基于径向基函数神经网络的开关磁阻电机建模 |
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| 引用本文： | 纪良文,蒋静坪,何峰. 基于径向基函数神经网络的开关磁阻电机建模[J]. 电工技术学报, 2001, 16(4): 7-11 |
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| 作者姓名： | 纪良文 蒋静坪 何峰 |
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| 作者单位： | 1. 浙江大学电气工程学院
2. 深圳市华为电气股份有限公司 |
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| 基金项目： | 浙江省重点科技计划项目 (0 0 110 612 7) |
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| 摘 要： | 基于径向基函数神经网络的局部逼近理论 ,利用高斯基函数 ,在分析测量数据和开关磁阻电机非线性磁特性的基础上 ,建立了开关磁阻电机的模型。通过与样机实测数据比较 ,验证了模型的有效性。与传统的局部线性化方法及BP神经网络比较 ,本文所建模型有更好的泛化能力和更快的速度 ,比较准确地反映了开关磁阻电机的磁特性 ,这对于开关磁阻电机的实时在线控制具有重要意义
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| 关 键 词： | 开关磁阻电机 径向基函数神经网络 建模 |
| 修稿时间： | 2001-02-27 |
Modeling of Switched Reluctance Motors Based on Radial Basis Function Neural Networks |
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| Ji Liangwen Jiang Jingping. Modeling of Switched Reluctance Motors Based on Radial Basis Function Neural Networks[J]. Transactions of China Electrotechnical Society, 2001, 16(4): 7-11 |
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| Authors: | Ji Liangwen Jiang Jingping |
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| Affiliation: | Ji Liangwen Jiang Jingping(Zhejiang University 310027 China) He Feng(Shenzhen Huawei Technologies Co.Ltd. 518129 C hina) |
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| Abstract: | The paper analysis nonlinear characteristics of swi tc hed reluctance motors (SRM). The modeling of SRM based on radial basis function (RBF) neural networks is designed with Gaussian function. Experimental results v alidates the proposed model. The simulated results show that the proposed model has better capability of generalization and more quickly velocity, and represent more correctly the characteristics of SRM compared with traditional method of l ocal linearization or BP networks, which is critical to real-time control for S RM. |
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| Keywords: | Switched reluctance motors Radial basis function neu ral network Modeling |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
