A Multilayer Recurrent Fuzzy Neural Network for Accurate Dynamic System Modeling

A multilayer recurrent fuzzy neural network(MRFNN)is proposed for accurate dynamic system modeling.The proposed MRFNN has six layers combined with T-S fuzzy model.The recurrent structures are formed by local feedback connections in the membership layer and the rule layer.With these feedbacks,the fuzzy sets are time-varying and the temporal problem of dynamic system can be solved well.The parameters of MRFNN are learned by chaotic search(CS)and least square estimation(LSE)simultaneously,where CS is for tuning the premise parameters and LSE is for updating the consequent coefficients accordingly.Results of simulations show the proposed approach is effective for dynamic system modeling with high accuracy.

Fake News Classification Using a Fuzzy Convolutional Recurrent Neural Network

In recent years,social media platforms have gained immense popularity.As a result,there has been a tremendous increase in content on social media platforms.This content can be related to an individual’s sentiments,thoughts,stories,advertisements,and news,among many other content types.With the recent increase in online content,the importance of identifying fake and real news has increased.Although,there is a lot of work present to detect fake news,a study on Fuzzy CRNN was not explored into this direction.In this work,a system is designed to classify fake and real news using fuzzy logic.The initial feature extraction process is done using a convolutional recurrent neural network(CRNN).After the extraction of features,word indexing is done with high dimensionality.Then,based on the indexing measures,the ranking process identifies whether news is fake or real.The fuzzy CRNN model is trained to yield outstanding resultswith 99.99±0.01%accuracy.This work utilizes three different datasets(LIAR,LIAR-PLUS,and ISOT)to find the most accurate model.

Robust stability analysis of Takagi-Sugeno uncertain stochastic fuzzy recurrent neural networks with mixed time-varying delays

In this paper, the global stability of Takagi-Sugeno （TS） uncertain stochastic fuzzy recurrent neural networks with discrete and distributed time-varying delays （TSUSFRNNs） is considered. A novel LMI-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of TSUSFRNNs. The proposed stability conditions are demonstrated through numerical examples. Furthermore, the supplementary requirement that the time derivative of time-varying delays must be smaller than one is removed. Comparison results are demonstrated to show that the proposed method is more able to guarantee the widest stability region than the other methods available in the existing literature.

The Fuzzy Neural Network Control Scheme With H∞ Tracking Characteristic of Space Robot System With Dual-arm After Capturing a Spin Spacecraft

In this paper,the dynamic evolution for a dualarm space robot capturing a spacecraft is studied,the impact effect and the coordinated stabilization control problem for postimpact closed chain system are discussed.At first,the pre-impact dynamic equations of open chain dual-arm space robot are established by Lagrangian approach,and the dynamic equations of a spacecraft are obtained by Newton-Euler method.Based on the results,with the process of integral and simplify,the response of the dual-arm space robot impacted by the spacecraft is analyzed by momentum conservation law and force transfer law.The closed chain system is formed in the post-impact phase.Closed chain constraint equations are obtained by the constraints of closed-loop geometry and kinematics.With the closed chain constraint equations,the composite system dynamic equations are derived.Secondly,the recurrent fuzzy neural network control scheme is designed for calm motion of unstable closed chain system with uncertain system parameter.In order to overcome the effects of uncertain system inertial parameters,the recurrent fuzzy neural network is used to approximate the unknown part,the control method with H∞tracking characteristic.According to the Lyapunov theory,the global stability is demonstrated.Meanwhile,the weighted minimum-norm theory is introduced to distribute torques guarantee that cooperative operation between manipulators.At last,numerical examples simulate the response of the collision,and the efficiency of the control scheme is verified by the simulation results.

Robust fuzzy control of Takagi-Sugeno fuzzy neural networks with discontinuous activation functions and time delays

The problem of global robust asymptotical stability for a class of Takagi-Sugeno fuzzy neural networks（TSFNN） with discontinuous activation functions and time delays is investigated by using Lyapunov stability theory.Based on linear matrix inequalities（LMIs）,we originally propose robust fuzzy control to guarantee the global robust asymptotical stability of TSFNNs.Compared with the existing literature,this paper removes the assumptions on the neuron activations such as Lipschitz conditions,bounded,monotonic increasing property or the right-limit value is bigger than the left one at the discontinuous point.Thus,the results are more general and wider.Finally,two numerical examples are given to show the effectiveness of the proposed stability results.

神经网络结构的递归T-S模糊模型

提出一种新的递归 T- S模型 (Takagi- Sugeno模型 )的模糊神经网络结构 (TSFRNN ) ,利用动态 BP(DBP)算法来学习训练神经网络的参数 ,通过与通常的多层前馈神经网络结构的 T- S模糊神经网络(TSFNN)的对比仿真实验 ,说明在非线性系统建模方面 TSFRNN比 TSFNN更加优越 .

基于动态T-S递归模糊神经网络的闪速熔炼过程参数软测量

闪速熔炼过程中存在大量多元非线性因素,难以从统计学和机理上确立操作参数。为优化闪速炉的操作参数,建立了动态T-S递归模糊神经网络(DTRFNN)的软测量模型,推导了DTRFNN的权值学习算法。将其应用到某厂铜闪速熔炼过程中的参数软测量上,平均精确率达到97%,能为生产操作提供有益的指导。

动态T-S递归模糊神经网络及其应用

提出了动态T-S递归模糊神经网络(DTRFNN)。该网络具有全局收敛特性的递归结构;采用BP算法进行网络权值的学习;并利用Lyapunov定理证明该模型具有全局收敛性,并在此基础上提出了克服局部极小的方法。最后以动态系统的辨识为例,进行实验研究,取得了很好的效果,表明DTRFNN动态模型能很好的对动态系统进行辨识。

动态T-S递归神经网络及其应用

基于递归神经网络和模糊系统,给出了一种动态T-S递归模糊神经网络(DTRFNN)。该神经网络用BP算法进行网络权值的学习,并在权值学习的基础上采用改进的BP算法克服局部极小。以动态系统的辨识为例进行仿真实验研究,并与一般的模糊神经网络进行了比较。结果表明,DTRFNN的辨识误差较小,取得了很好的辨识效果。该神经网络应用于某金属温度软测量时,能很好地实现温度的在线检测。

递归T-S模糊模型的神经网络

在常规T-S模糊神经网络的基础上加入动态递归元件,提出了递归T-S模糊模型的神经网络。在系统辨识中采用无监督聚类算法和动态反向传播算法训练该递归神经网络的参数,给出了该递归网络的逼近性证明。辨识效果与常规T-S模糊模型作比较,说明递归T-S模糊模型的神经网络在非线性系统辨识中表现出更好的性能。

Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots

There are many techniques using sensors and wearable devices for detecting and monitoring patients with Parkinson’s disease(PD).A recent development is the utilization of human interaction with computer keyboards for analyzing and identifying motor signs in the early stages of the disease.Current designs for classification of time series of computer-key hold durations recorded from healthy control and PD subjects require the time series of length to be considerably long.With an attempt to avoid discomfort to participants in performing long physical tasks for data recording,this paper introduces the use of fuzzy recurrence plots of very short time series as input data for the machine training and classification with long short-term memory(LSTM)neural networks.Being an original approach that is able to both significantly increase the feature dimensions and provides the property of deterministic dynamical systems of very short time series for information processing carried out by an LSTM layer architecture,fuzzy recurrence plots provide promising results and outperform the direct input of the time series for the classification of healthy control and early PD subjects.

T-S norm FNN controller based on hybrid learning algorithm

Aiming at the problems that fuzzy neural network controller has heavy computation and lag,a T-S norm Fuzzy Neural Network Control based on hybrid learning algorithm was proposed.Immune genetic algorithm (IGA) was used to optimize the parameters of membership functions (MFs) off line,and the neural network was used to adjust the parameters of MFs on line to enhance the response of the controller.Moreover,the latter network was used to adjust the fuzzy rules automatically to reduce the computation of the neural network and improve the robustness and adaptability of the controller,so that the controller can work well ever when the underwater vehicle works in hostile ocean environment.Finally,experiments were carried on " XX" mini autonomous underwater vehicle (min-AUV) in tank.The results showed that this controller has great improvement in response and overshoot,compared with the traditional controllers.

A new neural network model for the feedback stabilization of nonlinear systems

A new neural network model termed ‘standard neural network model’ (SNNM) is presented, and a state-feedback control law is then designed for the SNNM to stabilize the closed-loop system. The control design constraints are shown to be a set of linear matrix inequalities (LMIs), which can be easily solved by the MATLAB LMI Control Toolbox to determine the control law. Most recurrent neural networks (including the chaotic neural network) and nonlinear systems modeled by neural networks or Takagi and Sugeno (T-S) fuzzy models can be transformed into the SNNMs to be stabilization controllers synthesized in the framework of a unified SNNM. Finally, three numerical examples are provided to illustrate the design developed in this paper.

A novel compensation-based recurrent fuzzy neural network and its learning algorithm

Based on detailed study on several kinds of fuzzy neural networks, we propose a novel compensationbased recurrent fuzzy neural network （CRFNN） by adding recurrent element and compensatory element to the conventional fuzzy neural network. Then, we propose a sequential learning method for the structure identification of the CRFNN in order to confirm the fuzzy rules and their correlative parameters effectively. Furthermore, we improve the BP algorithm based on the characteristics of the proposed CRFNN to train the network. By modeling the typical nonlinear systems, we draw the conclusion that the proposed CRFNN has excellent dynamic response and strong learning ability.

Wastewater treatment control method based on a rule adaptive recurrent fuzzy neural network

Purpose-The purpose of this paper is to present an on-line modeling and controlling scheme based on the dynamic recurrent neural network for wastewater treatment system.Design/methodology/approach-A control strategy based on rule adaptive recurrent neural network(RARFNN)is proposed in this paper to control the dissolved oxygen(DO)concentration and nitrate nitrogen(SNo)concentration.The structure of the RARFNN is self-organized by a rule adaptive algorithm,and the rule adaptive algorithm considers the overall information processing ability of neural network.Furthermore,a stability analysis method is given to prove the convergence of the proposed RARFNN.Findings-By application in the control problem of wastewater treatment process(WWTP),results show that the proposed control method achieves better performance compared to other methods.Originality/value-The proposed on-line modeling and controlling method uses the RARFNN to model and control the dynamic WWTP.The RARFNN can adjust its structure and parameters according to the changes of biochemical reactions and pollutant concentrations.And,the rule adaptive mechanism considers the overall information processing ability judgment of the neural network,which can ensure that the neural network contains the information of the biochemical reactions.

AN INTELLIGENT CONTROL SYSTEM BASED ON RECURRENT NEURAL FUZZY NETWORK AND ITS APPLICATION TO CSTR

In this paper, an intelligent control system based on recurrent neural fuzzynetwork is presented for complex, uncertain and nonlinear processes, in which a recurrent neuralfuzzy network is used as controller (RNFNC) to control a process adaptively and a recurrent neuralnetwork based on recursive predictive error algorithm (RNNM) is utilized to estimate the gradientinformation partial deriv y/partial deriv u for optimizing the parameters of controller. Comparedwith many neural fuzzy control systems, it uses recurrent neural network to realize the fuzzycontroller. Moreover, recursive predictive error algorithm (RPE) is implemented to construct RNNM online. Lastly, in order to evaluate the performance of the proposed control system, the presentedcontrol system is applied to continuously stirre'd tank reactor (CSTR). Simulation comparisons,based on control effect and output error, with general fuzzy controller and feed-forward neuralfuzzy network controller (FNFNC), are conducted. In addition, the rates of convergence of RNNMrespectively using RPE algorithm and gradient learning algorithm are also compared. The results showthat the proposed control system is better for controlling uncertain and nonlinear processes.

Achieving of Fuzzy Automata for Processing Fuzzy Logic

At present, there has been an increasing interest in neuron-fuzzy systems, the combinations of artificial neural networks with fuzzy logic. In this paper, a definition of fuzzy finite state automata （FFA） is introduced and fuzzy knowledge equivalence representations between neural networks, fuzzy systems and models of automata are discussed. Once the network has been trained, we develop a method to extract a representation of the FFA encoded in the recurrent neural network that recognizes the training rules.

一种用于非线性动态辨识的新型神经网络

为提高非线性动态系统辨识(NDSI)的效果,在结合自建型模糊神经网络(SCFNN)和多层神经元神经网络(MLPNN)的基础上,提出一种自建递归型模糊神经网络(SCRFNN)。SCRFNN相较于前者,多了一个递归通道与抑制模糊规则产生机制;相较于后者,增加了模糊推论与一个递归通道。为验证SCRFNN在系统辨识中的有效性,设计一个新的NDSI在线学习模型与代码设计流程图,并以此作为在线学习架构,将以上3个神经网络模型对4个串-并型非线性动态系统进行辨识分析。经过仿真表明,新提出的SCRFNN通过存储内部状态,具备了映射动态特征的功能,从而使系统具有适应时变特性的能力,更适合于非线性动态系统的辩识。且在模糊规则数、学习收敛速度、学习与预测误差均方根值、预测精准度方面也取得了良好的效果。

基于循环Legendre模糊神经网络的DFIG二阶滑模容错控制

针对双馈感应发电机(DFIG)易受外界干扰而对并网产生影响的问题,将循环Legendre模糊神经网络(RLFNN)与二阶滑模控制(SOSMC)应用于DFIG控制中,从而提高了DFIG在传感器故障和不确定条件下的功率跟踪能力。首先,SOSMC采用超螺旋算法进行推导,并使用Lyapunov第二定理证明了控制系统的渐近稳定性。其次,提出了使用RLFNN来估计不确定部分,RLFNN的控制律与参数可在线训练,以进一步确保系统鲁棒性。仿真结果表明,所提出的方法能够使DFIG在发生传感器故障、参数变化以及外部干扰情况下保持正常运行,实现了有效容错控制。