基于自适应模糊神经网络的噪声抵消器

讨论了基于自适应模糊神经网络的噪声抵消器的设计方法。自适应模糊神经网络系统具有非线性映射和自学习能力，能够用于噪声信号的非线性建模。它不仅能够获取信号的最佳估计，并互能够克服信号处理中存在的模型和噪声的不确定性、不完备性。该法设计的滤波器效果良好，并可以用于多路信号和复杂信号的噪声消除。     

基于模糊PID控制器的多电机同步控制装置的应用

该文在PID控制的基础上引入了模糊逻辑的概念，提出了一种较新的模糊PID控制算法，并使之应用在多电机同步控制装置中，实验结果表明该控制器能够大大增强系统的动态特性，使多电机良好地以一定速度同步运行。     

模糊神经网络在刀具磨损检测中的应用

建立了一个以模糊推理为基础的模糊神经网络模型，该模型采用了自组织竞争学习与BP算法相结合的混合学习算法。利用该模型进行了刀具磨损识别的实验研究，实验结果表明该模型能够可靠地对刀具磨损进行识别。     

基于模糊综合评判的部件失效区域的确定

应用模糊方法评估部件振动可靠性时，需构建部件振动的模糊共振区域及相应的隶属函数和模糊强度失效区域及相应的隶属函数。针对航空发动机关键部件振动可靠性评估中影响部件模糊共振失效区域及其隶属函数、模糊强度失效区域及其隶属函数确定的众多复杂的、非确定的、时变的因素，将二级模糊综合评判方法应用于航空发动机关键部件模糊共振失效区域及其隶属函数、模糊强度失效区域及其隶属函数的确定，采用层次分析法(AHP法)确定评价指标权重，建立确定模糊共振失效区域及其隶属函数、模糊强度失效区域及其隶属函数的数学模型和流程。结果表明，该方法能够解决航空发动机关键部件模糊共振失效区域及其隶属函数、模糊强度失效区域及其隶属函数确定中一些复杂因素的处理问题。     

宽带振动主动控制的模糊自适应控制方法

由于广泛采用的自适应滤波－X LMS算法只限于线性问题，针对振动主动控制中存在的一些非线性问题，文中利用模糊逻辑系统能够逼近非线性系统的特性，提出一种模糊适应前馈主动控制方法，解决由于传感通道的非线性使得参考信号与外扰间呈非线性函数关系的主动控制问题。针对复合材料梁振动控制的数值仿真计算结果表明，该非线性模糊自适应控制方法的有效性。     

基于模糊贝叶斯网络的多态系统可靠性分析及在液压系统中的应用

为使贝叶斯网络能够对模糊信息和不确定信息进行处理,提出一种新的基于模糊贝叶斯网络的多态系统可靠性分析方法。该方法将模糊集合理论引入到贝叶斯网络可靠性分析中,考虑部件故障状态、部件故障率的模糊性以及部件间故障逻辑关系的不确定性,使贝叶斯网络具有处理模糊信息的能力。该方法采用模糊数描述系统和部件的故障状态,利用模糊子集描述部件的故障率,运用贝叶斯网络的条件概率表描述部件间的不确定联系。该方法应用到载重车液压悬架系统的可靠性分析实例中,分析结果表明该方法在进行系统可靠性分析时能够充分利用系统的模糊信息和不确定信息,从而提高系统可靠性分析的效率。     

基于ANFIS模糊神经网络的微钻头破损监测

使用轴向力和扭矩信号监测微孔钻削过程，提出了基于ANFIS模糊神经网络作为微钻头破损状态监测模型。该模型能够较准确描述钻头破损和信号特征之间的非线性关系，和常用的BP神经网络相比，具有收敛速度快和局部学习能力等优点。实验结果表明：采用ANFIS模糊神经网络对提高微钻头监测的准确性非常有效。     

基于视觉导航的AGV模糊-最优控制研究

介绍了基于视觉导航的路径识别算法，该算法能够较好地计算出自动引导车的相对位置，并通过相对位置信息，运用模糊一最优控制策略来实现自动引导车的路径跟踪控制。不同条件下所做的大量仿真计算均表明，基于视觉导航的模糊最优控制器工作稳定、切换平滑、跟踪控制误差小且有较好的抵抗外界噪声干扰能力。     

基于混沌退火算法的非线性系统Fuzzy控制规则自调整

非线性系统模糊控制实际应用中存在控制精度和自适应能力问题，模糊控制规则的自调整和自寻优，是提高和改善模糊控制器性能重要手段。带有修正因子的控制规则能够根据误差E和误差变化EC自动产生规则，利用全局寻优的混沌模拟退火算法对修正因子优化，优化模糊规则；并引入综合考虑误差、误差绝对值时间、控制能量、超调诸因素的性能函数。非线性系统仿真实例说明该方法响应上升时间快，超调小提高了模糊控制器性能。     

电热致动式微位移流体驱动器的控制分析

该文介绍了一种新型流体微位移驱动器。该微驱动器克服了传统微驱动器的缺点，能够同时满足大驱动力、大行程、和微位移这三个条件。同时采用一种参数在线自校正模糊控制器对该微驱动器进行控制，取得了较好的控制效果。     

Pattern classification by a neurofuzzy network: application to vibration monitoring

An innovative neurofuzzy network is proposed herein for pattern classification applications, specifically for vibration monitoring. A fuzzy set interpretation is incorporated into the network design to handle imprecise information. A neural network architecture is used to automatically deduce fuzzy if-then rules based on a hybrid supervised learning scheme. The neurofuzzy classifier proposed is equipped with a one-pass, on-line, and incremental learning algorithm. This network can be considered a self-organized classifier with the ability to adaptively learn new information without forgetting old knowledge. The classification performance of the proposed neurofuzzy network is validated on the Fisher's Iris data, which is a well-known benchmark data set. For the generalization capability, the neurofuzzy network can achieve 97.33% correct classification. In addition, to demonstrate the efficiency and effectiveness of the proposed neurofuzzy paradigm, numerical simulations have been performed using the Westland data set. The Westland data set consists of vibration data collected from a US Navy CH-46E helicopter test stand. Using a simple fast Fourier transform technique for feature extraction, the proposed neurofuzzy network has shown promising results. Using various torque levels for training and testing, the network achieved 100% correct classification.     

An on-line modified least-mean-square algorithm for training neurofuzzy controllers

The problem hindering the use of data-driven modelling methods for training controllers on-line is the lack of control over the amount by which the plant is excited. As the operating schedule determines the information available on-line, the knowledge of the process may degrade if the setpoint remains constant for an extended period. This paper proposes an identification algorithm that alleviates "learning interference" by incorporating fuzzy theory into the normalized least-mean-square update rule. The ability of the proposed methodology to achieve faster learning is examined by employing the algorithm to train a neurofuzzy feedforward controller for controlling a liquid level process. Since the proposed identification strategy has similarities with the normalized least-mean-square update rule and the recursive least-square estimator, the on-line learning rates of these algorithms are also compared.     

脉冲 MIG 焊模糊控制器的优化设计

针对脉冲ＭＩＧ焊接采用的模糊控制器进行了探索性研究。鉴于语言变量的模糊状态划分对控制器的控制性能有着重要影响，本文没有沿用常规的七档划分法，而是通过对不同划分策略下的控制特性进行计算机仿真，从而确定了合适的划分策略，使模糊控制器的设计得以优化。     

模糊PID控制器在开关电源充电机控制系统中的应用

研究了一种分段的模糊PID控制器在开关电源充电机控制系统中的应用;阐述了控制器设计、各充电环节动态分析和建模;对模糊PID控制和传统PID控制进行了对比;展示了模糊控制在充电机控制系统中的良好应用前景。     

浮法玻璃熔窑燃烧系统的模糊控制

设计了一个名义窑温参数自整定的FUZZY-PI控制系统，详细给出了该控制系统中关键的模糊控制器离线部分的设计过程与工程实现的参数自整定算法，并设计了基于克勒格积分器的一种非线性比例积分器替代线性积分器应用于文中的模糊控制系统。     

模糊PID控制器的设计

本文着重对基本模糊控制器、模糊PID控制器的设计和在仿真软件MATLAB中的具体实现方法作了比较详尽的论述，在仿真软件MATLAB中完成对基本模糊控制器和模糊PID控制设计工作，并对相应的控制系统进行仿真，对时间常数对控制系统的控制效果的影响作了演示，得到其响应曲线，并与PID控制方法进行比较，从而得出模糊PID控制器对系统进行控制优于一般模糊控制器控制方法。     

基于模糊小波神经网络的加工过程自适应控制

为了克服模糊控制动态响应慢和鲁棒性差的缺点,将模糊控制的定性知识表达能力与小波分析优异的局部控制性能和神经网络的定量学习能力相结合,提出了一种模糊小波神经网络自适应控制器,并将其应用于加工过程控制。对变切削深度的铣削加工过程控制的仿真结果表明,基于模糊小波神经网络的加工过程自适应控制,其控制效果优于一般的模糊控制和神经网络控制,具有很好的动、静态性能。该自适应控制器能有效防止刀具损坏和提高加工效率,是一种有效的加工过程控制方法。     

一种基于模糊边界层的转台伺服系统滑模控制器设计

针对飞行模拟转台位置伺服系统中存在的非线性摩擦环节,设计了一种补偿摩擦的模糊边界层滑模变结构控制器.在常规的准滑模变结构控制中引入模糊控制,利用模糊控制器来动态调整滑模边界层的厚度.通过MATLAB仿真,结果表明该控制器较好地协调了常规固定边界层滑模控制鲁棒性与平滑控制抖振之间的矛盾,有效地抑制了摩擦力矩的影响,保证了系统的快速性和鲁棒性,实现了高精度的位置跟踪.     

Type-2 fuzzy model based controller design for neutralization processes

In this study, an inverse controller based on a type-2 fuzzy model control design strategy is introduced and this main controller is embedded within an internal model control structure. Then, the overall proposed control structure is implemented in a pH neutralization experimental setup. The inverse fuzzy control signal generation is handled as an optimization problem and solved at each sampling time in an online manner. Although, inverse fuzzy model controllers may produce perfect control in perfect model match case and/or non-existence of disturbances, this open loop control would not be sufficient in the case of modeling mismatches or disturbances. Therefore, an internal model control structure is proposed to compensate these errors in order to overcome this deficiency where the basic controller is an inverse type-2 fuzzy model. This feature improves the closed-loop performance to disturbance rejection as shown through the real-time control of the pH neutralization process. Experimental results demonstrate the superiority of the inverse type-2 fuzzy model controller structure compared to the inverse type-1 fuzzy model controller and conventional control structures.     

New modifying self-organizing fuzzy controller for robotic motion control

The precise motion control of robotic manipulators is important in improving productivity and quality. However, robotic manipulators are multivariable nonlinear dynamic systems. Designing a model-based controller for robotic system control is difficult because its mathematical model is hard to accurately establish. This study proposed a self-organizing fuzzy controller (SOFC) to control a robotic system and evaluate its control performance. The SOFC continually updates the learning strategy in the form of fuzzy rules during the control process. The learning rate and the weighting distribution value of the controller are hard to regulate, so its fuzzy control rules may be modified to such an extent that the system response generally causes oscillatory phenomena. Two fuzzy logic controllers were designed according to the system output error and the error change, and introduced to the SOFC to determine the appropriate parameters of the learning rate and the weighting distribution, in order to eliminate this oscillation. This new modifying self-organizing fuzzy controller (NMSOFC) can effectively improve the control performance of the system, reduce the time consumed to establish a suitable fuzzy rule table, and support practical and convenient fuzzy controller applications. To confirm the applicability of the proposed intelligent controllers, this work retrofitted an old robot for a control system to evaluate the feasibility of motion control. Experiment results indicate the NMSOFC has better control performance in reducing the tracking errors of the joint-space trajectories and the positions, and requires less computational time than does the traditional fuzzy controller.