共查询到20条相似文献，搜索用时 62 毫秒
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Chia-Nan Ko 《Journal of The Franklin Institute》2012,349(5):1758-1780
This paper proposes a fuzzy neural network (FNN) based on wavelet support vector regression (WSVR) approach for system identification, in which an annealing robust learning algorithm (ARLA) is adopted to adjust the parameters of the WSVR-based FNN (WSVR-FNN). In the WSVR-FNN, first, the WSVR method with a wavelet kernel function is used to determine the number of fuzzy rules and the initial parameters of FNN. After initialization, the adjustment for the parameters of FNNs is performed by the ARLA. Combining the self-learning ability of neural networks, the compact support of wavelet functions, the adaptive ability of fuzzy logic, and the robust learning capability of ARLA, the proposed FNN has the superiority among the several existed FNNs. To demonstrate the performance of the WSVR-FNN, two nonlinear dynamic plants and a chaotic system taken from the extant literature are considered to illustrate the system identification. From the simulation results, it shows that the proposed WSVR-FNN has the superiority over several presented FNNs even the number of training parameters is considerably small. 相似文献
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Previously proposed adaptive fuzzy sliding mode control (AFSMC) and adaptive fuzzy sliding mode observer (AFSMO) methods are mixed and extended for the case of affine systems in which the input gain matrix is state-dependent, non-diagonal and non-positive definite. The proposed Extended AFSMCO (E-AFSMCO) method is then applied for position control of a Stewart Manipulator (SM), whose parameters are strongly state-dependent and complex and not suitable for practical control purposes. A robust observer-based control method which can work with a simplified model of the plant, and at the same time can preserve the stability and performance of the overall complex system is of great need. In this study, the SM dynamic model is simplified by removing the dynamic effects of the legs and the neglected terms are considered as un-modeled dynamics, for which the upper bound of the uncertainty is progressively estimated using the proposed adaptation rules. The final controller is comprised of a fuzzy controller in parallel with a robust switching controller. The second Lyapunov theorem is used to prove the closed-loop asymptotic stability. The proposed E-AFSMCO method is verified numerically and experimentally, depicting the effectiveness of the method for real-time industrial applications. 相似文献
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本文以水泥回转窑的控制为对象,采用模糊控制器来实现控制。阐述了控制器的设计思想,并成功应用于水泥回转窑的控制中,取得了理想的控制效果。 相似文献
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A. Aziz Khater Ahmad M. El-Nagar Mohammad El-Bardini Nabila M. El-Rabaie 《Journal of The Franklin Institute》2018,355(14):6390-6415
In this paper, an adaptive Takagi–Sugeno (T–S) fuzzy controller based on reinforcement learning for controlling the nonlinear dynamical systems is proposed. The parameters of the T–S fuzzy system are learned using the reinforcement learning based on the actor-critic method. This on-line learning algorithm improves the controller performance over the time, which it learns from its own faults through the reinforcement signal from the external environment and tries to reinforce the T–S fuzzy system parameters to converge. The updating parameters are developed using the Lyapunov stability criterion. The proposed controller is faster in learning than the T–S fuzzy that parameters learned using the gradient descent method under the same conditions. Moreover, it is able to handle the load changes and the system uncertainties. The test is carried out based on two mathematical models. In addition, the proposed controller is applied practically for controlling a direct current (DC) shunt machine. The results indicate that the response of the proposed controller has a good performance compared with other controllers. 相似文献
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《Journal of The Franklin Institute》2022,359(2):952-966
Robots are commonly used to perform repetitive or dangerous tasks, and these systems integrate different components that increase the complexity of their dynamic model. Thus, in order to ensure an acceptable tracking performance, the implementation of robust control strategies should be considered. This paper proposes a discrete-time model-free-implementation controller, which relies on the well-known structural properties of mechanical systems, but it does not consider any knowledge on the values of the system parameters. Besides, a fuzzy logic formulation is considered to compensate external disturbances. The outputs of the fuzzy inference system are adapted online to improve the closed-loop response. Finally, representatives simulation and experiments are analyzed to demonstrated the feasibility of the proposed approach. 相似文献
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In this paper, an evolutionary algorithm (EA)-based fuzzy proportional-derivative (PD)-type controller is employed to reservoir control of dams with the purpose of operating spillway gates during any flood of any magnitude, which is not predictable beforehand. EA is used to evolve the main parameters of the fuzzy PD controller. The use of the EA, in conjunction with a systematic neighborhood structure for the determining of fuzzy rule-base parameters, leads to a significant improvement in the performance of the controller. The major objective of the controller is to achieve better system performance over the conventional control methods. In order to demonstrate the high performance of the presented method, we simulate the control system using different probable inflow hydrographs of various magnitudes. The simulation results indicate that the EA-based fuzzy PD controller not only performs an accurate and efficient solution, but also exhibits more desirable and reliable results than the conventional approaches. 相似文献
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Takagi-Sugeno (T-S) fuzzy models can provide an effective representation of complex nonlinear systems with a series of linear input/output submodels in terms of fuzzy sets and fuzzy reasoning. In this paper, the T-S fuzzy model approach is extended to the stability analysis and controller design for nonlinear systems with time delays. An improved stability condition is proposed by introducing adjustable parameters into the Lyapunov-Krasovskii functional. Stabilization approach for fuzzy state feedback is also presented. Sufficient conditions for the existence of fuzzy feedback gain are derived through the numerical solution of a set of obtained linear matrix inequalities (LMIs). Compared with the existing methods in the literature, the proposed approach has less conservatism and both the sizes of delay and its derivative are involved in the criterion. The dynamical performance of the system can be adjusted by changing the adjustable parameters. Finally, two examples are given to show the effectiveness of the proposed approach. 相似文献
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《Journal of The Franklin Institute》2022,359(18):10510-10524
This paper investigates the problem of finite-time attack detection for nonlinear complex cyber-physical networks under false data injection (FDI) attacks. Firstly, a Takagi-Sugeno (T-S) fuzzy model is used to approximate nonlinear complex cyber-physical networks in which the measurement channels are injected by FDI attacks. Secondly, based on adding a power integrator technique, a finite-time fuzzy observer is designed to achieve the rapid state observation of complex cyber-physical networks within a finite time by adjusting the observer parameters. Then, an attack detection mechanism consisting of the finite-time fuzzy observer and an attack detector is developed to detect FDI attacks, which can trigger an alarm within a finite time when FDI attacks occur. Finally, simulation results are given to show the effectiveness and superiority of the proposed method. 相似文献
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IEEE802.21工作小组为解决异构网络互联问题提出了媒介独立切换(Media Independent Handover,MIH)方案。对WIFI、WIMAX异构网络融合进行了研究,为解决异构网络之间移动的平滑切换问题设计出一套切换流程和切换判决策略。通过仿真实验验证了其良好的性能。 相似文献
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自2015年国家科技体制改革以来,为解决以往地方科技计划体系中存在的科技资源“碎片化”“重复资助”等问题,广东省等省市逐步开展科技计划体系改革,将以往的零散项目整合为以重大项目为主体的地方科技计划体系。针对改革后地方重大科技项目的创新绩效影响因素,本文基于模糊ANP分析法,结合专家咨询结果构建评价指标体系,采用标准度和三角模糊数法确定各级因素的重要性和关联度,从而得出各影响因素的重要性排序。最后以广东省为实例得出的结果表明,技术路线、实施计划、产业需求等因素对该地区科技项目成功绩效影响较大,符合广东省重点领域研发计划以解决产业核心技术瓶颈问题设立的战略定位,验证了本方法的科学性和合理性。 相似文献
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This paper presents an optimal fuzzy partition based Takagi Sugeno Fuzzy Model (TSFM) in which a novel clustering algorithm, known as Modified Fuzzy C-Regression Model (MFCRM), has been proposed. The objective function of MFCRM algorithm has been developed by considering of geometrical structure of input data and linear functional relation between input–output data. The MFCRM partitions the data space to create fuzzy subspaces (rules). A new validation criterion has been developed for detecting the right number of rules (subspaces) in a given data set. The obtained fuzzy partition is used to build the fuzzy structure and identify the premise parameters. Once, right number of rules and premise parameters have been identified, then consequent parameters have been identified by orthogonal least square (OLS) approach. The cluster validation index has been tested on synthetic data set. The effectiveness of MFCRM based TSFM has been validated on benchmark examples, such as Boiler Turbine system, Mackey–Glass time series data and Box–Jenkins model. The model performance is also validated through high-dimensional data such as Auto-MPG data and Boston Housing data. 相似文献
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结合BT模式和大型光伏发电项目特点,构建BT光伏发电项目风险评价指标体系,应用模糊层次分析模型,对构建的指标体系风险权重进行计算,根据计算结果对关键风险因素做进一步分析,为项目相关方项目决策和风险管理提供科学依据。 相似文献
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基于模糊理论,提出以百分比填答的模糊语义表,采用模糊语义算法,构建科研项目立项评估模型。构建过程先由回收的电子打分表,取模糊值大于模糊中位数的有效值,并提取出评估科研项目的主指标、次指标,建立科研项目评估表,供评审委员会主任、副主任、专家总体组长进行评估。由评审专家按照科研项目评估表给予各准则的权重,并根据此准则进行科研项目评分,采用模糊集方式表达,将各抽象的准则转为量化的三角模糊数,对模糊数进行排序评估。最后进行案例评估分析,对构建的模型进行验证,结果表明具有良好的优越性,为科学、客观、公正的评估和优选科研项目立项提供了一种更加可行的方法。 相似文献
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《Journal of The Franklin Institute》2019,356(12):5944-5960
Due to the unknown system structure of the froth flotation process and frequent fluctuations in production conditions, design of control strategy is a challenging problem. As a result, manual operation is still widely applied in practice by observing froth image features. However, since the manual observation is subjective and the production conditions are time-varying, the manual operation cannot make decisions quickly and accurately. In this paper, a data-driven-based adaptive fuzzy neural network control strategy is developed to implement the automatic control of the antimony flotation process. The strategy is composed of fuzzy neural network (FNN) controllers, a data-driven model, and an on-line adaptive algorithm. The FNN is constructed to derive the control laws of the reagent dosages. The parameters of the FNN controllers are tuned by gradient descent algorithm. To obtain the real-time error feedback information, the data-driven model is established, which integrates the long short term memory (LSTM) network and radial basis function neural network (RBFNN). The LSTM network is utilized as a primary model, and the RBFNN is used as an error compensation model. To handle the challenges of the frequent fluctuations in the production conditions, the on-line adaptive algorithm is proposed to tune the parameters of the FNN controllers. Simulations and experiments are carried out in a real-world antimony flotation plant in China. The results demonstrate that the proposed adaptive fuzzy neural network control strategy produces better control performance than the other two existing methods. 相似文献
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细胞信号转导网络的结构复杂,规模庞大,建立的数学模型维数高,变量多,具有高度非线性。在复杂系统分析设计中,模型简化始终是主要的研究问题之一。提出一种基于混合推理方法的模型简化策略,利用代谢控制分析、敏感性分析、主元分析和通量分析相结合,降低系统模型维数,减少生化反应个数,简化系统结构。以NF-κB信号转导网络作为研究对象,原模型由24个常微分方程和64个参数组成,简化模型则包括17个常微分方程,1个代数方程和52个参数。仿真结果表明,简化模型能够准确地预测系统的动态特性,为模型分析和参数辨识提供方便,验证了模型简化策略的有效性。 相似文献