Fuzzy logic controlled neural network learning
Information Sciences—Applications: An International Journal
Design of an adaptive control system for DC servo motor
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Application of Neural Networks to Adaptive Control of Nonlinear Systems
Application of Neural Networks to Adaptive Control of Nonlinear Systems
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
MATLAB Supplement to Fuzzy and Neural Approaches in Engineering,
MATLAB Supplement to Fuzzy and Neural Approaches in Engineering,
An accelerated learning algorithm for multilayer perceptron networks
IEEE Transactions on Neural Networks
ANN inverse analysis based on stochastic small-sample training set simulation
Engineering Applications of Artificial Intelligence
Damage identification for beams using ANN based on statistical property of structural responses
Computers and Structures
An intelligent neural system for predicting structural response subject to earthquakes
Advances in Engineering Software
Simulating the seismic response of embankments via artificial neural networks
Advances in Engineering Software
A hybrid Particle Swarm Optimization - Simplex algorithm (PSOS) for structural damage identification
Advances in Engineering Software
Structure damage diagnosis using neural network and feature fusion
Engineering Applications of Artificial Intelligence
A new procedure for damage assessment of prestressed concrete beams using artificial neural network
Advances in Artificial Neural Systems
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In this research, we explore the structural damage detection using frequency response functions (FRFs) as input data to the back-propagation neural network (BPNN). Such method is non-model based and thus could have advantage in many practical applications. Neural network based damage detection generally consists of a training phase and a recognition phase. Error back-propagation algorithm incorporating gradient method can be applied to train the neural network, whereas the training efficiency heavily depends on the learning rate. While various training algorithms, such as the dynamic steepest descent (DSD) algorithm and the fuzzy steepest descent (FSD) algorithm, have shown promising features (such as improving the learning convergence speed), their performance is hinged upon the proper selection of certain control parameters and control strategy. In this paper, a tunable steepest descent (TSD) algorithm using heuristics approach, which improves the convergence speed significantly without sacrificing the algorithm simplicity and the computational effort, is investigated. A series of numerical examples demonstrate that the proposed algorithm outperforms both the DSD and FSD algorithms. With this as basis, we implement the neural network to the FRF based structural damage detection. The analysis results on a cantilevered beam show that, in all considered damage cases (i.e., trained damage cases and unseen damage cases, single damage cases and multiple-damage cases), the neural network can assess damage conditions with very good accuracy.