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5. 二型模糊小波脑情感学习网络在非线性系统辨识中的研究

二型模糊小波脑情感学习网络在非线性系统辨识中的研究

来源 :模糊系统与数学 | 被引量 : 0次 | 上传用户：shl405567051

【摘 要】

：

针对现有模糊神经网络在辨识具有时变的非线性系统存在辨识精度不高,收敛速度较慢等缺点,提出了一种二型小波模糊脑情感学习网络(T2FWBELN)模型,它结合了模糊逻辑和脑情感学习网络的优点,并在网络结构中使用了小波函数.与其他算法相比,该算法在非线性系统辨识中有着更高的逼近能力.同时,采用模糊C均值算法生成模糊规则,并使用梯度下降法对T2FWBELN的各种参数进行在线调整,降低了参数调整时间.为了进一步验证该模型的有效性和优越性,仿真了两个不确定非线性系统辨识的例子,一个是Mackey-Glass时间序列预测

【作 者】

：

桑景福 陈向坚 王平心 

【机 构】

：

江苏科技大学计算机学院,江苏镇江 212003;江苏科技大学理学院,江苏镇江 212003

【出 处】

：

模糊系统与数学

【发表日期】

：

2022年1期

【关键词】

：

非线性系统辨识 脑情感学习网络 二型模糊集 

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论文部分内容阅读

针对现有模糊神经网络在辨识具有时变的非线性系统存在辨识精度不高,收敛速度较慢等缺点,提出了一种二型小波模糊脑情感学习网络(T2FWBELN)模型,它结合了模糊逻辑和脑情感学习网络的优点,并在网络结构中使用了小波函数.与其他算法相比,该算法在非线性系统辨识中有着更高的逼近能力.同时,采用模糊C均值算法生成模糊规则,并使用梯度下降法对T2FWBELN的各种参数进行在线调整,降低了参数调整时间.为了进一步验证该模型的有效性和优越性,仿真了两个不确定非线性系统辨识的例子,一个是Mackey-Glass时间序列预测,一个是带有噪声的动态系统辨识.测试结果表明,所提出的模型在处理非线性系统辨识中拥有更高的精度.

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