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In this paper, a discrete adaptive ILC is presented for a time-varying nonlinear system with nonparametric uncertainties. A novel estimation of nonparametric uncertainties is constructed just using the past I/O data, and the uncertainties are completely compensated such that the output tracking error is only affected by external disturbance. As a main contribution of this paper, all the discussions...
Gaussian-Hopfield neural network algorithm (GHNNs) is the most commonly used method of solving the identification problem of nonlinear systems, but learning rule (LMS rule) is easy to fall into local optimum. Genetic algorithm (GA) has globally optimal ability and can solve the locally optimal problem well. This paper puts forward GA-GHNNs algorithm and uses GA algorithm to solve the optimum parameters...
Reinforcement learning (RL) is a popular learning paradigm to adaptive learning control of nonlinear systems, and is able to work without an explicit model. However, learning from scratch, i.e., without any a priori knowledge, is a daunting undertaking, which results in long training time and instability of learning process with large continuous state space. For physical systems, one must consider...
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