结合神经网络理论与变结构控制理论，提出一种基于神经网络的变结构滑模控制方案。
To combine neural network theory with varying structure control theory, a neural-network-based varying structure sliding mode control scheme is presented.
提出基于T - S模糊神经网络的变结构控制方法。
A variable structure control method based on T-S fuzzy neural network (FNN) is brought forward.
针对开架式水下机器人运动的精确控制问题，提出一种水下机器人变结构神经网络控制方法。
Aiming at the problem of how to control an open-frame underwater vehicle precisely, a new method of control based on variable structure neural network was proposed.
本文提出一种基于T -S模型的变结构模糊神经网络直接逆模型控制器，并将其应用于移动机器人的运动控制中。
A direct inverse model controller of fuzzy neural network with changeable structure based on t s inference is presented in this paper and it is used to the motion control of mobile robot.
推导了逆变点焊过程控制模型，并构建了逆变点焊模糊神经网络恒电流控制系统结构。
A controlling model of inverter spot-welding process and a fuzzy neural network configuration about inverter spot-welding with constant current control were built in this paper.
本文主要内容包括对CMAC神经网络泛化性能的研究及其在对不确定非线性系统进行积分变结构控制（IVSC）中的应用。
The main content in this paper include generalization performance research of CMAC NN and the application in Integral Variable Structure Control (IVSC) for uncertain nonlinear systems.
应用该模型对线性结构和非线性结构在变阻尼控制和外荷载激励下结构的响应进行了数值仿真，表明所提的动态递归神经网络可以达到较高的预测精度。
Simulations on linear and nonlinear structures demonstrate that RDRNN is very effective on predicting the response of a structure subject to semi-active control and external excitation.
应用一种变结构神经网络算法对初始化的模糊规则进行调整，提高模糊控制规则的自学习和自适应能力。
A kind of variable structure neural network algorithm is adopted to adjust fuzzy rules, and improves the ability of self-studying and self-adjusting in fuzzy control rules.
本文主要将自适应控制、变结构控制与神经网络相结合，提出性能更优越的神经网络自适应变结构控制。
In this paper, adaptive control, sliding mode variable structure control and neural network are combined, a superior neural network adaptive variable structure control is introduced.
首先设计一个线性观测器，随后用神经网络对其具有不确定性的部分进行补偿，并用神经网络实现对变结构控制器参数的在线调整。
A linear observer is firstly designed. Then a neural network is used for compensating uncertainty. The parameter of VSC is adjusted on line by a neural network.
首先设计一个线性观测器，随后用神经网络对其具有不确定性的部分进行补偿，并用神经网络实现对变结构控制器参数的在线调整。
A linear observer is firstly designed. Then a neural network is used for compensating uncertainty. The parameter of VSC is adjusted on line by a neural network.
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