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Fuzzy stochastic neural network model for structural system identification

机译：结构系统辨识的模糊随机神经网络模型

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摘要

This paper presents a dynamic fuzzy stochastic neural network model for nonparametric system identification using ambient vibration data. The model is developed to handle two types of imprecision in the sensed data: fuzzy information and measurement uncertainties. The dimension of the input vector is determined by using the false nearest neighbor approach. A Bayesian information criterion is applied to obtain the optimum number of stochastic neurons in the model. A fuzzy C-means clustering algorithm is employed as a data mining tool to divide the sensed data into clusters with common features. The fuzzy stochastic model is created by combining the fuzzy clusters of input vectors with the radial basis activation functions in the stochastic neural network. A natural gradient method is developed based on the Kullback-Leibler distance criterion for quick convergence of the model training. The model is validated using a power density pseudospectrum approach and a Bayesian hypothesis testing-based metric. The proposed methodology is investigated with numerically simulated data from a Markov Chain model and a two-story planar frame, and experimentally sensed data from ambient vibration data of a benchmark structure.
机译：本文提出了一种利用环境振动数据进行非参数系统辨识的动态模糊随机神经网络模型。开发该模型以处理感测数据中的两种不精确性：模糊信息和测量不确定性。输入向量的维是通过使用伪最近邻方法确定的。应用贝叶斯信息准则来获得模型中随机神经元的最佳数量。采用模糊C均值聚类算法作为数据挖掘工具，将感知到的数据分为具有共同特征的聚类。通过将输入向量的模糊簇与随机神经网络中的径向基激活函数相结合，创建模糊随机模型。基于Kullback-Leibler距离准则，开发了一种自然梯度方法，用于模型训练的快速收敛。使用功率密度伪谱方法和基于贝叶斯假设检验的指标对模型进行验证。拟议的方法进行了调查，从马尔可夫链模型和两层平面框架的数值模拟数据，和从基准结构的环境振动数据实验感测到的数据。

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