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Synchronization of chaos using radial basis functions neural networks

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Journal of Systems Engineering and Electronics 2007年第1期18卷 83-88,100页

作者： Ren Haipeng Liu Ding School of Automation and Information Engineering Xi'an Univ. of Technology Xi'an 710048 P.R.China

The radial basis functions neural Network （RBFNN） is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response system can be implemented by employing the RBFNN model and state feedback control. In this case, the exact mathematical model, which is the precondition for the conventional method, is unnecessary for implementing synchronization. The effect of the model error is investigated and a corresponding theorem is developed. The effect of the parameter perturbations and the measurement noise is investigated through simulations. The simulation results under different conditions show the effectiveness of the method.

关键词： Chaos synchronization radial basis function neural networks Model error Parameter perturbation Measurement noise.

Nonlinear Identification and Control of Laser Welding Based on RBF neural networks

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Computer Systems Science & Engineering 2022年第4期41卷 51-65页

作者： Hongfei Wei Hui Zhao Xinlong Shi Shuang Liang Henan Polytechnic Institute Nanyang473000China Zhengzhou Railway Vocational&Technical College Zhengzhou450000China University of Florence Firenze50041Italy

A laser beam is a heat source with a high energy density;this technol-ogy has been rapidly developed and applied in theﬁeld of welding owing to its potential advantages,and supplements traditional welding techniques.An in-depth analysis of its operating process could establish a good foundation for its application in China.It is widely understood that the welding process is a highly nonlinear and multi-variable coupling process;it comprises a signiﬁcant number of complex processes with random uncertain factors.Because of their nonlinear mapping and self-learning characteristics,artiﬁcial neural networks(ANNs)have certain advantages in comparison to traditional methods in theﬁeld of welding.Laser welding is a nonlinear dynamic process;these processes still pose a major challenge in theﬁeld of control.Therefore,establishing a stable model is a pre-requisite for achieving accurate control.In this study,the identiﬁcation and con-trol of radial basis function neural networks in laser welding processes and self-tuning PID control methods are proposed to improve weld quality.Using a MATLAB simulation,it is shown that the proposed method can obtain a good description of the level of nonlinear dynamic control,and that the algorithm iden-tiﬁcation accuracy is high,practical,and effective.Using this method,the weld width quickly reaches the expected value and the system remains stable,with good robustness.Further,it ensures the stability and dynamic performance of the welding process and improves weld quality.

关键词： Laser welding radial basis function neural networks self-tuning nonlinear identification

Neuro-Based Higher Order Sliding Mode Control for Perturbed Nonlinear Systems

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Intelligent Automation & Soft Computing 2023年第4期36卷 385-400页

作者： Ahmed M.Elmogy Wael M.Elawady Computer Engineering Department Prince Sattam Bin Abdelaziz UniversityAlkharj24567KSA Department of Computers and Control Engineering Tanta UniversityTanta13457Egypt

One of the great concerns when tackling nonlinear systems is how to design a robust controller that is able to deal with uncertainty.Many researchers have been working on developing such type of controllers.One of the most efﬁ-cient techniques employed to develop such controllers is sliding mode control(SMC).However,the low order SMC suffers from chattering problem which harm the actuators of the control system and thus unsuitable to be used in many practical applications.In this paper,the drawbacks of low order traditional sliding mode control(FOTSMC)are resolved by presenting a novel adaptive radial basis function neural network–based generalized rth order sliding mode control strategy for nth order uncertain nonlinear systems.The proposed solution adopts neural networks for their excellent capability in function approximation and thus used to approximate the nonlinearities and uncertainties for systems under considera-tion.The approximation errors are completely considered in the developed approach.The proposed approach can be used with any order of sliding mode and thus can be generally used with various types of applications.The global sta-bility of the proposed control approach is proved through Lyapunov stability cri-terion.The proposed approach is validated and assessed through simulations on the nonlinear inverted pendulum system with severe modeling uncertainties.The simulations results show that the proposed approach provide superior perfor-mance compared with other approaches in the literature.

关键词： SMC nonlinear systems PID Lyapunov stability radial basis function neural networks

人工免疫杂交学习方法及其在函数近似中的应用

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人工免疫杂交学习方法及其在函数近似中的应用

作者： SHAZLY ALSOLIBY 南昌大学

学位级别：硕士

The immune system is highly distributed, highly adaptive, self-organizing in nature, maintains a memory of past encounters, and has the ability to continually learn about new encounters. From a computational viewpoint, the immune system has much to offer by way of inspiration. Recently there has been growing interest in the use of the natural immune system as inspiration for the creation of novel approaches to computational problems;this field of research is referred as Immunological Computation (IC) or Artificial Immune Systems (AIS).Over the years, biology has provided a rich source of inspiration for many different people in many different ways ranging from designing aircraft wings to bulletproof vests. Biology has also been used as a source of inspiration for computation problems, which can be classified as biologically motivated computing. This is different from computationally motivated biology, where computing provides the source and inspiration for models in biology. There has been much work done on the use of biological metaphors, for example neural networks, genetic algorithms and genetic programming. Recently, there has been increasing interest in using the natural immune system as a metaphor for computation in a variety of domains.

关键词： Hybrid learning radial basis function neural networks Artificial immune systems Immune system Clonal selection

A Novel RBF neural Network Design Based On Immune Algorithm System

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A Novel RBF Neural Network Design Based On Immune Algorithm ...

第36届中国控制会议

作者： Fei Li Cuili Yang Junfei Qiao Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781538629185

To solve the problem of gradient descent（GD） method which has low accuracy and easily falling into local optimum,the radial basis function（RBF） based on immune algorithm system（IAS-RBF） is proposed.In this method,each antibody is a RBF neural network and the optimal affinity is calculated by immune algorithm system（IAS） to get the best antibody,then the optimal parameter of RBF neural network（i.e.,the RBF centers,the widths,and the output weights） are obtained.The simulation results show that IAS-RBF overcomes the problem of premature convergence,and has a better accuracy than other RBF neural networks.

关键词： radial basis function neural networks immune algorithm system nonlinear approximation ability

Rapid Oscillation Fault Detection for Distributed System via Deterministic Learning 25th

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Rapid Oscillation Fault Detection for Distributed System via...

第25届中国控制与决策会议

作者： Tianrui Chen Cong Wang David J. Hill School of Automation Guangdong University of Technology School of Electrical and Information Engineering University of Sydney School of Automation and the Center for Control and Optimization South China University of Technology

ISBN: (纸本)9781467355339

In this paper, a rapid detection and isolation scheme for oscillation faults in a distributed nonlinear system is proposed based on a recent result on deterministic learning （DL） theory. The distributed nonlinear system considered is modeled as a set of interconnected subsystems. Firstly, a local learning and merging method based on DL is proposed to obtain knowledge of the unknown interconnections and the fault functions. Secondly, by utilizing learned knowledge, a bank of consensus-based dynamical estimators are constructed for each subsystem, and average 1 norms of the residuals are generated to make the detection and isolation decisions. Thirdly, a rigorous analysis for characterizing the detection and isolation capabilities of the proposed scheme is given. The attraction of the intelligence fault diagnosis approach is to give a fast response to faults by using the learned knowledge and processing huge data in a dynamical and distributed manner. Simulation studies are included to demonstrate the effectiveness of the approach.

关键词： Fault detection and isolation distributed systems deterministic learning radial basis function neural networks persistent excitation condition.

State Observation for a Class of Uncertain Nonlinear Systems via Deterministic Learning

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State Observation for a Class of Uncertain Nonlinear Systems...

第二十九届中国控制会议

作者： ZHOU Guopeng1,3,WANG Cong2 1.College of Mathematics and statistics,Xianning University,Xianning 437100,P.R.China2.School of Automation and Center for Control and Optimization,South China University of Technology,Guangzhou 510641,P.R.China3.College of electric and electronic engineering,Huazhong University of Science and Technology,Wuhan 430074,P.R.China

In this paper,based on the recently proposed theory of deterministic learning,a new neural observer is proposed for a class of uncertain nonlinear systems undergoing periodic or recurrent motions.Firstly,it is shown that the state observation error converges to a small neighborhood of zero exponentially in finite time,and a partial persistent excitation（PE）condition is satisfied.Secondly,by imposing an auxiliary filter and constructing a new Lyapunov function,it is obtained that the neural weight estimation error also converges to a small neighborhood of zero.Thus,the system uncertain dynamics along the periodic trajectory can be locally-accurately identified by the radial basis function（RBF）neural networks（NNs）and then stored in a constant RBF NNs.Finally,a constant NNs observer is also implemented,with guaranteed stability and good performance.The proposed observer scheme does not require high-gain design,and the embedded localized NNs can learn（identify）the nonlinear uncertain dynamics along the estimated periodic or recurrent system trajectory.

关键词： State observation deterministic learning radial basis function neural networks Partial persistent excitation