Self-organizing feature selection fuzzy neural network-based terminal sliding mode control for uncertain nonlinear systems.

A composite terminal sliding mode controller (CTSMC) for a kind of uncertain nonlinear system (UNS) is developed in this study. The primary aim of the design is to enhance the control performance of the CTSMC by learning its unknown parameters using a newly fuzzy neural network (FNN). Firstly, the stability and convergence of CTSMC for UNS with known parameters are demonstrated.

An Improved Deep Reinforcement Learning Method for Dispatch Optimization Strategy of Modern Power Systems.

As a promising information theory, reinforcement learning has gained much attention. This paper researches a wind-storage cooperative decision-making strategy based on dueling double deep Q-network (D3QN). Firstly, a new wind-storage cooperative model is proposed.

Adaptive Global Sliding-Mode Control for Dynamic Systems Using Double Hidden Layer Recurrent Neural Network Structure.

In this paper, a full-regulated neural network (NN) with a double hidden layer recurrent neural network (DHLRNN) structure is designed, and an adaptive global sliding-mode controller based on the DHLRNN is proposed for a class of dynamic systems. Theoretical guidance and adaptive adjustment mechanism are established to set up the base width and central vector of the Gaussian function in the DHLRNN structure, where six sets of parameters can be adaptively stabilized to their best values according to different inputs. The new DHLRNN can improve the accuracy and generalization ability of the network, reduce the number of network weights, and accelerate the network training speed due to the strong fitting and presentation ability of two-layer activation functions compared with a general NN with a single hidden layer.