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Adaptive Fuzzy Modeling of Hovering Submarine Based on On-line Clustering

机译:基于在线聚类的悬停潜艇的自适应模糊建模

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A fuzzy modeling approach based on on-line potential clustering is presented for a family of complex MIMO systems with severe nonlinearity and strong coupling such as a hovering submarine. The structure of the fuzzy model is defined as special group of If-Then rules with real constant consequents which is expressed as a feed-forward fuzzy neural network identified by on-line clustering and first order gradient algorithm. Using this method, the model structure and parameters can be achieved and updated rapidly and accurately, and the obtained rules of the model can be added, modified and deleted automatically according to the new information. In addition, the modeling approach using a structure with the fuzzy rules with constant consequents simplifies the modeling process compared to methods using the T-S model. Results of the simulation of hovering submarine demonstrate the effectiveness of this approach.
机译:基于在线潜在聚类的模糊建模方法是针对具有严重非线性和强耦合的复杂MIMO系统系列,例如悬停潜艇。模糊模型的结构被定义为具有实际恒定后果的IF-DON规则的特殊组,其表示为由在线聚类和第一阶梯度算法识别的前馈模糊神经网络。使用此方法,可以快速且准确地实现和更新模型结构和参数,并且可以根据新信息自动添加,修改和删除所获得的模型规则。另外,与使用T-S模型的方法相比,使用具有恒定后果的模糊规则的结构的建模方法简化了建模过程。悬停潜水艇的模拟结果证明了这种方法的有效性。

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