Dynamic Fuzzy Wavelet Neural Network Model for Structural System Identification

Abstract

A new dynamic time-delay fuzzy wavelet neural network model is presented for nonparametric identification of structures using the nonlinear autoregressive moving average with exogenous inputs approach. The model is based on the integration of four different computing concepts: dynamic time delay neural network, wavelet, fuzzy logic, and the reconstructed state space concept from the chaos theory. Noise in the signals is removed using the discrete wavelet packet transform method. In order to preserve the dynamics of time series, the reconstructed state space concept from the chaos theory is employed to construct the input vector. In addition to denoising, wavelets are employed in combination with two soft computing techniques, neural networks and fuzzy logic, to create a new pattern recognition model to capture the characteristics of the time series sensor data accurately and efficiently. The model balances the global and local influences of the training data and incorporates the imprecision existing in the sensor data effectively. Experimental results on a five-story steel frame are employed to validate the computational model and demonstrate its accuracy and efficiency.