共查询到18条相似文献,搜索用时 453 毫秒
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针对递归模糊神经网络(Recurrent fuzzy neural network, RFNN)的递归量难以自适应的问题, 提出一种基于小波变换–模糊马尔科夫链(Wavelet transform fuzzy Markov chain, WTFMC)算法的RFNN模型.首先, 在时间维度上记录隐含层神经元的模糊隶属度, 并采用小波变换将该时间序列进行分解, 通过模糊马尔科夫链对子序列的未来时段进行预测, 之后将各预测量合并后代入递归函数中得到具有自适应性的递归量.其次, 利用梯度下降算法更新RFNN的参数来保证神经网络的精度.最后, 通过非线性系统建模中几个基准问题和实际污水处理中关键水质参数的预测实验, 证明了该神经网络模型的可行性和有效性. 相似文献
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污水处理过程具有非线性、时变、大滞后的特点,尤其在雨天暴雨等特殊天气下,污水的入水波动会对控制器造成严重干扰。文中提出一种基于模糊神经网络模型的自适应广义预测控制算法,实现对污水处理过程中溶解氧浓度的实时控制。该算法利用反馈线性化思想实现自适应广义预测控制器的设计,在证明其李雅普诺夫稳定的同时,得到修正系统的受控自回归积分滑动平均模型参数自适应规则,动态调整模型参数使系统跟踪误差达到最小。仿真实验结果表明,该算法能够稳定、快速地控制溶解氧浓度,具有较强的抗干扰能力和鲁棒性,该控制算法特别适合用于污水处理过程的特殊天气(如雨天和暴雨天)中。 相似文献
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针对城市污水处理过程时滞导致难以稳定控制的问题, 提出一种自适应滑模控制方法(Adaptive sliding mode control, ASMC). 首先, 分析推流时滞对城市污水处理生化反应过程的影响, 建立时滞影响下的城市污水处理运行控制模型; 其次, 设计一种基于模糊神经网络的预估补偿模型, 完成滞后变量的准确预测, 实现控制模型中变量时刻的统一; 最后, 设计一种具有自适应开关增益系数的滑模控制器(Sliding mode control, SMC), 实现溶解氧和硝态氮的稳定控制. 将提出的自适应滑模控制方法应用于城市污水处理过程基准仿真平台, 实验结果显示该方法能够实现城市污水处理运行过程稳定控制. 相似文献
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针对目前非线性动态偏最小二乘(PLS)建模方法在拟合较强非线性化工过程时存在的问题, 提出一种基于稳定学习的递归神经网络动态PLS建模方法. 该算法将递归神经网络与Hammerstein模型相结合, 对外部PLS提取的特征向量进行内部建模, 具有逼近较强非线性化工过程的能力, 改善了模型的适用范围. 此外, 采用带有稳定学习的参数更新算法对模型参数进行在线修正, 改善了模型的预测精度和自适应能力. 将此方法应用于氧化铝生产过程铝酸钠溶液组分浓度建模实验, 仿真结果表明, 本方法是可行有效的. 相似文献
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本文结合现场的实际过程数据,首先应用能量平衡建立了强制循环蒸发过程的动态模型.针对该过程的多变量、非线性以及强耦合特性,在常规增量式PID控制器的基础上提出基于神经网络与多模型切换的非线性自适应解耦PID控制策略.该控制器是由线性自适应解耦PID控制器和基于神经网络的非线性自适应解耦PID控制器以及切换机构组成.其中线性自适应解耦PID控制器可以保证系统的稳定,而基于神经网络的非线性自适应解耦PID控制器则可以有效地提高系统的性能.上述过程的PID参数是通过广义预测的方法得到,最后通过仿真表明,上述控制方法不仅消除了回路间的耦合,在稳定生产的同时提高了蒸发的效率. 相似文献
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基于NARX网络的无刷直流电机自适应逆控制 总被引:1,自引:0,他引:1
针对无刷直流电机(bnmhless DC motor,BLDCM)非线性的特点,引入了一种基于神经网络的自适应逆控制方法.该方案中,用非线性自回归(NARX)动态网络做为模型辨识器和控制器.辨识器采用了BP(back propagation)算法在线调整参数,并获取被控时象精确的Jacobian信息,再由实时递归学习算法(RTRL)实现对控制器的在线整定.仿真结果表明,方法具有响应速度较快、无超调的优点,且具备较强的自适应性和鲁棒性. 相似文献
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本文借鉴免疫识别原理,结合递归模糊神经网络(RFNN),提出了一种新的基于免疫化递归模糊神经网络的复杂系统辨识方法.其基本思想是,将复杂系统模型分解为可变部分与不变部分,不变模型描述系统平均动态行为,可变模型描述不确定性造成的系统实际行为对平均行为的偏差.以RFNN的隶属度函数神经元为构件,用它的各种组合构造不同的RFNN模型覆盖系统的可变模型空间,应用时采用免疫遗传算法在线筛选合适构件构造可变模型,识别系统扰动.仿真结果表明该方法能有效完成复杂不确定系统的快速在线识别. 相似文献
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针对污水处理过程溶解氧浓度的控制问题,提出一种直接自适应动态神经网络控制方法(direct adaptive dynamic neural network control,DADNNC).构建的控制系统主要包括神经网络控制器和补偿控制器.神经网络控制器由自组织模糊神经网络实现系统状态与控制量之间的映射;提出一种基于规则无用率的结构修剪算法,并给出结构调整后网络收敛的理论证明.同时,为保证系统稳定,设计补偿控制器减小网络逼近误差,参数调整由Layapunov理论给出.国际基准仿真平台上的实验表明,与固定结构神经网络控制器、PID和模型预测控制等已有控制方法相比,DADNNC方法具有更高的控制精度和更强的适应能力. 相似文献
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Ching-Hung Lee 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》2004,34(2):1075-1088
In this paper, an adaptive parallel control architecture to stabilize a class of nonlinear systems which are nonminimum phase is proposed. For obtaining an on-line performance and self-tuning controller, the proposed control scheme contains recurrent fuzzy neural network (RFNN) identifier, nonfuzzy controller, and RFNN compensator. The nonfuzzy controller is designed for nominal system using the techniques of backstepping and feedback linearization, is the main part for stabilization. The RFNN compensator is used to compensate adaptively for the nonfuzzy controller, i.e., it acts like a fine tuner; and the RFNN identifier provides the system's sensitivity for tuning the controller parameters. Based on the Lyapunov approach, rigorous proofs are also presented to show the closed-loop stability of the proposed control architecture. With the aid of the RFNN compensators, the parallel controller can indeed improve system performance, reject disturbance, and enlarge the domain of attraction. Furthermore, computer simulations of several examples are given to illustrate the applicability and effectiveness of this proposed controller. 相似文献
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Jun‐Fei Qiao Gai‐Tang Han Hong‐Gui Han Cui‐Li Yang Wei Li 《Asian journal of control》2019,21(3):1270-1280
Due to the characteristics of strong coupling and high nonlinearity in the control process, an intelligent decoupling control strategy based on recurrent fuzzy neural network (RFNN) is proposed in this paper to control the wastewater treatment process (WWTP). Firstly, the architecture of the RFNN controller is designed with a mechanism analysis of WWTP. Secondly, a decoupling strategy in combination with a gradient descent search algorithm is used to decouple the control loop of dissolved oxygen (DO) concentration and nitrate nitrogen (SNO) concentration. Finally, stability analysis based on a Lyapunov function is investigated. The proposed approach has been applied to the WWTP simulation model. Compared to model predictive control, echo state network‐based HDP (E‐HDP), conventional RFNN, and neural network on‐line modelling and controlling methods, the proposed method has better control performance. 相似文献
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In this article, a fuzzy adaptive controller approach is presented for nonlinear systems. The proposed quasi-ARX neural network based on Lyapunov learning algorithm is used to update its weight for prediction model as well as to modify fuzzy adaptive controller. The improving performances of the Lyapunov learning algorithm are stable in the learning process of the controller and able to increase the accuracy of the controller as well as fast convergence of error. The simulations are intended to show the effectiveness of the proposed method. 相似文献
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在非线性系统的模糊动力学模型基础上,提出一种模糊神经网络变结构自适应控制器;网络的结构根据非线性系统特性动态构成,基于该网络提出非线性预测器,基于梯度法提出了一种网络参数学习算法,并分析了收敛性及其性质。将网络预测器与参数学习算法相结合,构成自适应控制算法,证明了算法的收敛性。仿真结果证实了算法的有效性。 相似文献
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A fuzzy neural network (FNN) controller with adaptive learning rates is proposed to control a nonlinear mechanism system in this study. First, the network structure and the on-line learning algorithm of the FNN is described. To guarantee the convergence of the tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the adaptive learning rates of the FNN. Next, a slider-crank mechanism, which is driven by a permanent magnet (PM) synchronous motor, is studied as an example to demonstrate the effectiveness of the proposed control technique; the FNN controller is implemented to control the slider position of the motor-slider-crank nonlinear mechanism. The robust control performance and learning ability of the proposed FNN controller with adaptive learning rates is demonstrated by simulation and experimental results. 相似文献