提出利用直接自适应模糊神经网络控制一类不确定非线性混沌系统新方法。
A novel direct adaptive fuzzy neural networks (FNNs) controller for a class of uncertain nonlinear chaotic system is presented.
基于模糊神经网络算法研究了非线性系统的噪声消除问题，设计了一类非线性自适应逆噪声消除控制器。
Based on Fuzzy Neural Network, the noise canceling problem of the nonlinear system was studied. A type of nonlinear adaptive noise controller was proposed.
本论文以实现具有类人反应的服务机器人为目标，研究了服务机器人的神经网络力控制器的实现问题。
This thesis investigates the problem of the realization of a neural network force controller for a service robot, which aims at a human-like service robot.
针对信息科学和控制理论中经常涉及的一类泛函极值问题，提出基于连续回归神经网络的求解方法。
In this paper, the continuous time recurrent neural network is proposed to solve the functional minimization problem, which is often involved in estimation and control.
提出了一类基于神经元状态估计器的自适应广义极点配置控制，研究了该控制系统的网络结构和权值学习方法。
This paper presents a class adaptive pole assignment control of servo systems based on neural state estimation and develops the system structure and the weight learning algorithms.
本文研究一类非线性神经网络自适应控制系统，提出一种基于双误差——辨识误差和跟踪误差的新控制方案。
A class of nonlinear neural network adaptive control systems is studied and a new design concept based on double errors was proposed in this paper.
提出了新颖的最优模糊聚类神经网络模型对机械手运动轨迹进行控制。
This paper presents a novel framework for trajectory tracking of robotic manipulators based on the optimal fuzzy clusting neural network system.
针对一类开环稳定的非线性系统，提出了一种基于模糊神经网络的非线性内模控制方案。
An internal model control strategy is proposed to a class of open loop stable nonlinear systems described by fuzzy neural networks.
基于递归神经网络给出了仅含一个非线性环节的一类非线性系统的自适应控制方案。
A scheme of adaptive control based on recurrent neural network is presented for a class of nonlinear systems only with a nonlinear part.
针对一类具有特殊模型的非线性系统本文提出了一种新型神经网络预测控制算法。
A novel neural network predictive control algorithm is proposed for a class of nonlinear system with special model.
本文研究一类离散神经网络中的混沌及控制混沌问题。
In this paper the chaos and controlling chaos in a class of discrete neural networks are studied.
文章针对一类非线性系统，研究了一种基于回馈递推法的自适应神经网络控制方法。
An adaptive back stepping control method based on neural networks is presented for a class of nonlinear systems.
研究了一类具有未知常数控制增益的耦合大系统的直接自适应神经网络控制问题。
The problem of direct adaptive neural network control for a class of interconnected systems with unknown constant control gains is studied in this paper.
针对一类不确定的离散时间非线性非最小相位动态系统，提出了一种基于神经网络和多模型的直接自适应控制方法。
A direct adaptive control approach is proposed for a class of uncertain discrete time nonlinear non-minimum phase dynamical systems.
研究了一类采样数据非线性系统的动态神经网络稳定自适应控制方法。
A stable adaptive control approach using dynamic neural networks has been developed for a class of multi input multi output MIMO sampled data nonlinear systems with unknown dynamic nonlinearities.
基于人工神经网络与模糊控制理论，对非线性系统提出了一种自组织模糊神经网络模型，并推导出一类新型学习算法。
Based on a neural network and the fuzzy control theory, this paper presents a self-organizing fuzzy-neural network for nonlinear systems, and develops a new learning algorithm.
一类多时滞不确定非线性系统，基于模型的模糊控制和神经网络控制相结合的混合控制方法。
A mixed control method combining fuzzy model-based control and neural network control is presented for a class of uncertain nonlinear system with multiple time delays.
针对一类多输入多输出非仿射非线性系统，基于神经网络设计了一种自适应控制方案。
A novel adaptive control method based neural network for a general class of MIMO non-affine nonlinear system was proposed, which are implicit function with respect to control input.
本文主要针对非线性算子及耦合均在输入端的一类非线性多变量系统，研究如何采用神经网络实现其在线解耦控制。
Based on the class of nonlinear multi-variable system with the nonlinear operator and couple appearing in the input of system, the online decoupling control with NN is studied in this thesis.
本文主要针对非线性算子及耦合均在输入端的一类非线性多变量系统，研究如何采用神经网络实现其在线解耦控制。
Based on the class of nonlinear multi-variable system with the nonlinear operator and couple appearing in the input of system, the online decoupling control with NN is studied in this thesis.
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