Abstract Aiming at the problem of uncertainty and static instability underwater robot system, a motion control algorithm based on fuzzy neural network optimized by genetic is proposed in this paper. The makes use characteristics which can predict approximate target value well complementarity function to network, uses optimize parameters membership function, so as reduce computation network. exp...

In this paper we present an improved neural network to solve strictly convex quadratic programming(QP) problem. The proposed model is derived based on a piecewise equation correspond to optimality condition of convex (QP) problem and has a lower structure complexity respect to the other existing neural network model for solving such problems. In theoretical aspect, stability and global converge...

This letter addresses the problem of event-triggered control (ETC) discrete-time linear time-invariant (LTI) systems stabilized by neural network controllers. The event-triggering mechanism (ETM) is proposed to update only a portion layers required maintain stability and satisfactory performance feedback system, thus reducing computational cost associated with evaluation network. Sufficient con...

This paper presents our Recurrent Control Neural Network (RCNN), which is a model-based approach for a data-efficient modelling and control of reinforcement learning problems in discrete time. Its architecture is based on a recurrent neural network (RNN), which is extended by an additional control network. The latter has the particular task to learn the optimal policy. This method has the advan...