基于自适应模糊神经网络的防车追尾控制器

研究防止汽车追尾优化识别问题,为解决防汽车追尾快速预报,传统方法在于精确计算安全距离,并未给出具体的控制力度,结合自适应模糊神经网络理论,提出让汽车“缓慢”降速的解决方案,能有效解决防汽车追尾的控制力度问题.采用自适应模糊神经网络模型,设计汽车刹车力度控制规则表,进一步设计防汽车追尾控制器.通过仿真结果可以看出,跟传统的纯模糊控制系统相比,自适应模糊神经网络生成的曲面更加平滑,控制效果更好.并且具有自学习与自适应能力,能够自动生成并调整隶属度函数,提高了快速性和实时性,为汽车防追尾控制器设计提供了参考.     

自适应模糊控制理论的研究综述

针对近10年来自适应模糊控制的主要研究成果,从模糊系统、模糊控制、稳定性、模糊逼近和神经网络等方面较详细地概括与分析了自适应模糊控制理论的研究与进展,特别是在Lyapunov稳定性理论下,基于模糊模型的自适应模糊控制与鲁棒控制、滑模控制等传统方法的结合与互补为非线性系统建模与控制提供了强有力的工具.最后对自适应模糊控制新的研究方向进行了展望,模糊建模与自适应控制的研究具有重要的理论和实际意义.     

基于模糊神经网络的积分自适应控制

将模糊神经网络应用于传统线性积分自适应控制,构造了一类模糊神经自适应方法,用于消除非线性系统响应偏差.模糊神经网构成直接非线性自适应控制器.对线性及非线性对象的仿真控制以及与经典自适应控制的比较,表明了模糊神经自适应控制器的有效性.     

一种基于模糊神经网络的双足机器人混杂控制

针对双足机器人控制问题,提出了一种基于模糊神经网络的混杂控制方法.该种方法将模糊神经网络融入了逆系统和H∞控制方法中,一方面将模糊神经网络的构造误差看作系统的干扰,利用H∞控制对干扰进行抑制.另一方面利用模糊神经网络对系统模型进行逼近,为逆系统的构建和H∞控制率的设计提供了有效的系统信息.本文分析了闭环系统的稳定性问题,证明了在采用本文提出的模糊神经网络和自适应算法后可以抑制L2增益.     

现代控制理论在交流传动中的应用*

本文综述了近些年来线性最优控制、自适应控制、变结构控制、微分几何控制、神经网络控制、模糊控制等控制技术在交流传动方面应用研究的主要方法与成果,并提出若干需要解决的问题.     

压电自适应桁架结构智能振动控制

介绍了采用模糊神经网络模型进行振动主动控制的压电自适应桁架结构设计、应用及实验结果. 设计了一种具有自适应结构技术的压电主动构件结构, 并提出了具有5层结构能够自调整隶属函数的模糊神经网络控制模型. 为了验证控制模型的有效性, 搭建了配置压电主动构件的双跨桁架结构试验平台, 通过检测误差信号, 由模糊神经网络控制模型确定主动构件的驱动输出. 试验结果证实了模糊神经网络控制模型在振动抑制方面的有效性.     

电液伺服系统的模糊神经网络鲁棒控制

为解决传统自适应控制模型的在线辨识和控制器的在线设计问题,基于模糊神经网络结构,提出了一种复合式控制方案.该方案通过引入运行监控器,克服了模糊神经网络实时性差的问题,实现了对不确定非线性系统的高精度输出跟踪;同时利用鲁棒反馈控制器,保证了模糊神经网络学习初期闭环系统的稳定性.该方案在电液伺服系统的应用中获得了满意的控制效果.     

机械臂轨迹跟踪控制研究进展

综述了近年来刚性机械臂轨迹跟踪控制研究领域的最新进展.根据应用于机械臂的不同控制算法进行分类,从自适应PID控制、神经网络自适应控制、模糊自适应控制、滑模变结构控制和鲁棒自适应控制5种主要控制方法进行阐述.重点从关节空间出发,论述了各种控制算法在提高机械臂轨迹跟踪性能方面的各自优缺点,并分析了它们之间的相互联系.对机械...     

基于Choquet积分-OWA的自适应神经模糊推理系统及其应用

针对已有的自适应神经模糊推理系统(ANFIS)在模糊规则后件表达上的缺陷和常见的模糊推理系统存在的主要问题,提出基于Choquet积分OWA的模糊推理系统(AggFIS),在模糊规则的后件表达、模糊算子的普适性和输入及规则的权重等方面有很大优势,它试图建立能够充分体现模糊逻辑本质和人类思维模式的模糊推理系统.根据模糊神经网的基本原理将AggFIS与前馈神经网络相结合,得到基于Choquet积分-OWA的自适应神经模糊推理系统(Agg-ANFIS),并将该模型应用于交通服务水平评价问题.实验结果证明,基于Choquet积分OWA的自适应神经模糊推理系统具有很好的非线性映射功能,它的本质是一类通用逼近器,为解决复杂系统的建模、分析及预测问题提供了有效的途径.     

潜艇垂直面运动自适应神经网络模糊控制仿真

神经网络控制和模糊控制技术的广泛应用为潜艇自动舵控制器的设计提供了新的思路.而模糊规则的提取和隶属函数的学习是模糊推理系统设计中重要而困难的问题,自适应神经网络模糊推理系统(ANFIS)结合模糊控制和神经网络控制的优点,基于sugeno模糊模型采用反向传播法和最小二乘法调整模糊推理系统的参数,并自动产生模糊规则.利用方法对潜艇乖直面运动自动舵控制器进行了设计和仿真.从仿真结果来看,自适应神经网络模糊控制器能较好的实现对潜艇垂直面运动的操纵控制,是一种很好的控制方法.     

控制中的一种神经网络结构

本文介绍通过利用神经网络计算算法怎样将神经网络两个重要的计算特点用于自适应控制。以单自由度机械手为例,对神经形态控制方法和模型参考自适应控制方法进行比较,对很大规模的系统,利用神经网络作为自适应控制提供定速,比传统的方法优越。     

New recursive-least-squares algorithms for nonlinear active controlof sound and vibration using neural networks

In recent years, a few articles describing the use of neural networks for nonlinear active control of sound and vibration were published. Using a control structure with two multilayer feedforward neural networks (one as a nonlinear controller and one as a nonlinear plant model), steepest descent algorithms based on two distinct gradient approaches were introduced for the training of the controller network. The two gradient approaches were sometimes called the filtered-x approach and the adjoint approach. Some recursive-least-squares algorithms were also introduced, using the adjoint approach. In this paper, an heuristic procedure is introduced for the development of recursive-least-squares algorithms based on the filtered-x and the adjoint gradient approaches. This leads to the development of new recursive-least-squares algorithms for the training of the controller neural network in the two networks structure. These new algorithms produce a better convergence performance than previously published algorithms. Differences in the performance of algorithms using the filtered-x and the adjoint gradient approaches are discussed in the paper. The computational load of the algorithms discussed in the paper is evaluated for multichannel systems of nonlinear active control. Simulation results are presented to compare the convergence performance of the algorithms, showing the convergence gain provided by the new algorithms.     

The discovery of experts'' decision rules from qualitative bankruptcy data using genetic algorithms

Numerous studies on bankruptcy prediction have widely applied data mining techniques to finding out the useful knowledge automatically from financial databases, while few studies have proposed qualitative data mining approaches capable of eliciting and representing experts' problem-solving knowledge from experts' qualitative decisions. In an actual risk assessment process, the discovery of bankruptcy prediction knowledge from experts is still regarded as an important task because experts' predictions depend on their subjectivity. This paper proposes a genetic algorithm-based data mining method for discovering bankruptcy decision rules from experts' qualitative decisions. The results of the experiment show that the genetic algorithm generates the rules which have the higher accuracy and larger coverage than inductive learning methods and neural networks. They also indicate that considerable agreement is achieved between the GA method and experts' problem-solving knowledge. This means that the proposed method is a suitable tool for eliciting and representing experts' decision rules and thus it provides effective decision supports for solving bankruptcy prediction problems.     

Globally stable adaptive robust tracking control using RBF neural networks as feedforward compensators

In previous adaptive neural network control schemes, neural networks are usually used as feedback compensators. So, only semi-globally uniformly ultimate boundedness of closed-loop systems can be guaranteed, and no methods are given to determine the neural network approximation domain. However, in this paper, it is showed that if neural networks are used as feedforward compensators instead of feedback ones, then we can ensure the globally uniformly ultimate boundedness of closed-loop systems and determine the neural network approximation domain via the bound of known reference signals. It should be pointed out that this domain is very important for designing the neural network structure, for example, it directly determines the choice of the centers of radial basis function neural networks. Simulation examples are given to illustrate the effectiveness of the proposed control approaches.     

基于神经网络的分类决策树构造

目前基于符号处理的方法是解决分类规则提取问题的主要方法,而基于神经网络的连接主义方法则用的不多,其主要原因在于虽然神经网络的分类精度高,但难于提取其所隐含的分类规则与知识.针对这个问题,结合神经网络的具体特点,该文提出了一种基于神经网络的构造分类决策树的新方法.该方法通过神经网络训练建立各属性与分类结果之间的关系,进而通过提取各属性与分类结果之间的导数关系来建立分类决策树.给出了具体的决策树构造算法.同时为了提高神经网络所隐含关系的提取效果,提出了关系强化约束的概念并建立了具体的模型.实际应用结果证明了算法的有效性.     

Intelligent control

A framework for intelligent control is presented and different approaches to intelligent control are reviewed in light of this framework. The topics discussed include knowledge-based control, fuzzy control, neural networks, fault diagnosis, single loop control and distributed control. The key ideas behind these approaches are outlined and it is indicated how they may be used to make control systems with significantly improved capabilities.     

Modeling and control of non-linear systems using soft computing techniques

This work is an attempt to illustrate the utility and effectiveness of soft computing approaches in handling the modeling and control of complex systems. Soft computing research is concerned with the integration of artificial intelligent tools (neural networks, fuzzy technology, evolutionary algorithms, …) in a complementary hybrid framework for solving real world problems. There are several approaches to integrate neural networks and fuzzy logic to form a neuro-fuzzy system. The present work will concentrate on the pioneering neuro-fuzzy system, Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is first used to model non-linear knee-joint dynamics from recorded clinical data. The established model is then used to predict the behavior of the underlying system and for the design and evaluation of various intelligent control strategies.     

A robotic system based on neural network controllers

In this paper, a control algorithm based on neural networks is presented. This control algorithm has been applied to a robot arm which has a highly nonlinear structure. The model based approaches for robot control (such as the computed torque technique) require high computational time and can result in a poor control performance, if the specific model-structure selected does not properly reflect all the dynamics. The control technique proposed here has provided satisfactory results. A decentralised model has been assumed here where a controller is associated with each joint and a separate neural network is used to adjust the parameters of each controller. Neural networks have been used to adjust the parameters of the controllers, being the outputs of the neural networks, the control parameters.     

神经网络硬件研究的现状与趋势

本文综述了目前神经网络硬件发展的状况，并给出了各种不同硬件网络实现的基本原理、性能指标和将来的发展趋势。     

Artificial neural network based robot control: An overview

The current thrust of research in robotics is to build robots which can operate in dynamic and/or partially known environments. The ability of learning endows the robot with a form of autonomous intelligence to handle such situations. This paper focuses on the intersection of the fields of robot control and learning methods as represented by artificial neural networks. An in-depth overview of the application of neural networks to the problem of robot control is presented. Some typical neural network architectures are discussed first. The important issues involved in the study of robotics are then highlighted. This paper concentrates on the neural network applications to the motion control of robots involved in both non-contact and contact tasks. The current state of research in this area is surveyed and the strengths and weakness of the present approaches are emphasized. The paper concludes by indentifying areas which need future research work.