Adjoint operator algorithms for faster learning in dynamical neural networks
Advances in neural information processing systems 2
Fuzzy Control
Intelligent optimal control with dynamic neural networks
Neural Networks
Learning state space trajectories in recurrent neural networks
Neural Computation
IEEE Transactions on Evolutionary Computation
Forecasting time series with genetic fuzzy predictor ensemble
IEEE Transactions on Fuzzy Systems
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
Training trajectories by continuous recurrent multilayer networks
IEEE Transactions on Neural Networks
Trajectory priming with dynamic fuzzy networks in nonlinear optimal control
IEEE Transactions on Neural Networks
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This paper proposes a Time Delay Dynamic Fuzzy Network (TDDFN) that can be used for tracking and prediction of chaotic time series. TDDFN considered here has unconstrained connectivity and dynamical elements in its fuzzy processing units with time delay state feedbacks. The minimization of a quadratic performance index is considered for trajectory tracking applications. Gradient with respect to model parameters are calculated based on adjoint sensitivity analysis. The computational complexity is significantly less than direct method, but it requires a backward integration capability. For updating model parameters, Broyden-Fletcher-Golfarb-Shanno (BFGS) algorithm that is one of the approximate second order algorithms is used. The TDDFN network is able to predict the Mackey-Glass chaotic time series and gives good results for the nonlinear system identification.