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.

基于粗糙集高速公路混沌T-S FNN控制仿真

研究基于粗糙集理论的高速公路混沌系统模糊神经网络入口匝道控制方法。针对高速公路车流量不确定性特点,提出了通过数据挖掘技术建立交通流入口匝道智能混沌控制器知识库的思想;设计了以密度、上游流量和最大李亚普诺夫指数作为输入,红灯时间作为输出的T-S模糊神经网络混沌控制器;采用粗糙集理论建立混沌控制器知识库,确定模糊神经网络控制器结构并提取模糊规则;采用模糊神经网络方法对控制器参数进行优化。仿真结果表明:采用该方法设计的智能混沌控制器,可实现保持高速公路有序运动、避免交通堵塞、提高交通通行能力的目的,是提高高速公路管理控制水平的有效方法。

基于T-S模型的FNN降水粒子相态识别方法

提出一种基于T-S(Takagi-Sugeno)模型的模糊神经网络(Fuzzy Neural Network,FNN)降水粒子相态识别方法。该方法建立一种多层前馈的模糊神经网络,在对双线偏振气象雷达接收的偏振参量进行模糊化、规则计算、模糊推理和退模糊处理基础上,利用模糊神经网络误差反馈的思想自适应的调节不同降水类型各偏振参量隶属函数的参数,保证降水粒子相态识别的精度要求。经过实测数据的处理结果证明了该方法的有效性。

Parameter Optimization of Interval Type-2 Fuzzy Neural Networks Based on PSO and BBBC Methods

Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs.

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.

Novel stability criteria for fuzzy Hopfield neural networks based on an improved homogeneous matrix polynomials technique

The global stability problem of Takagi-Sugeno（T-S） fuzzy Hopfield neural networks（FHNNs） with time delays is investigated.Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarantee the asymptotic stability of the FHNNs with less conservatism.Firstly,using both Finsler＇s lemma and an improved homogeneous matrix polynomial technique,and applying an affine parameter-dependent Lyapunov-Krasovskii functional,we obtain the convergent LMI-based stability criteria.Algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis via the homogeneous matrix polynomials technique.Secondly,to further reduce the conservatism,a new right-hand-side slack variables introducing technique is also proposed in terms of LMIs,which is suitable to the homogeneous matrix polynomials setting.Finally,two illustrative examples are given to show the efficiency of the proposed approaches.

Application of fuzzy neural network to the nuclear power plant in process fault diagnosis

The fuzzy logic and neural networks are combined in this paper,setting up the fuzzy neural network (FNN); meanwhile, the distinct differences and connections between the fuzzy logic and neural network are compared. Furthermore, the algorithm and structure of the FNN are introduced. In order to diagnose the faults of nuclear power plant, the FNN is applied to the nuclear power plant, and the intelligence fault diagnostic system of the nuclear power plant is built based on the FNN . The fault symptoms and the possibility of the inverted U-tube break accident of steam generator are discussed. In order to test the system’s validity, the inverted U-tube break accident of steam generator is used as an example and maoy simulation experiments are performed. The test result shows that the FNN can identify the fault.

Design and Implementation of Computer-Aid Garment Coordination Tool Using Fuzzy Neural Network

By modeling the decision-making process of garment coordination of fashion designers,a kind of computer-aid garment coordination using fuzzy neural network was proposed. The Takagi-Sugeno Fuzzy Neural Network (TSFNN) is used to learn the knowledge and rules of fashion designers on garment coordination and calculate the garment coordination satisfaction index (GCSI). The implementation of the computer-aid garment coordination tool is divided into two stages. The first stage is to acquire the knowledge of garment coordination. The second stage is to train and use the fuzzy neural network to conduct garment coordination. Three layers structure were also discussed for developing the system. By applying the computer-aid garment coordination tool into a real fashion-retailing store,the experimental results show the system performs well with choosing a suitable value for screening out the satisfaction coordination pairs.

Application of variable-filtrating technique on fuzzy-reasoning neural network system predicting BOF end-point carbon content

Artificial intelligence techniques have been used to predict basic oxygen furnace(BOF) end-points. However,the main challenge is to effectively reduce the input nodes as too many input nodes in neural network increase complexity,decrease accuracy and slow down the training speed of the network.Simply picking-up variables as input usually influence validity of model.It is quite necessary to develop an effective method to reduce the number of input nodes whereby to simplify the network and improve model performance.In this study,a variable-filtrating technique combining both metallurgical mechanism model and partial least-squares(PLS ) regression method has been proposed by taking the advantages of both of them,i.e.qualitive and quantative relationships between variables respectively.Accordingly,a fuzzy-reasoning neural network(FNN) prediction model for basic oxygen furnace(BOF) end-point carbon content based on this technique has been developed.The prediction results showed that this model can effectively improve the hit rate of end-point carbon content and increase network training speed.The successful hit rate of the model can reach up to 94.12%with about 0.02% error range.

A Fuzzy Neural Network Model of Linguistic Dynamic Systems Based on Computing with Words

Linguistic dynamic systems(LDS)are dynamic processes involving computing with words(CW)for modeling and analysis of complex systems.In this paper,a fuzzy neural network(FNN)structure of LDS was proposed.In addition,an improved nonlinear particle swarm optimization was employed for training FNN.The experiment results on logistics formulation demonstrates the feasibility and the efficiency of this FNN model.

基于T-S模糊神经网络的齿槽效应补偿方法研究

针对齿槽效应带来的齿槽误差问题,提出在传感器探头内布设齿槽位置检测线圈,建立传感器齿槽特性模型和基于T-S模糊神经网络的齿槽补偿系统模型,依据齿槽位置信号对传感器进行齿槽误差补偿。利用附加动量的BP学习方法对网络进行学习和测试。仿真结果表明补偿模型的输出不再随齿槽位置波动,最大误差为±0.2 mm,该种方法可以有效地消除齿槽效应并提高传感器的检测精度,满足高速磁浮车悬浮控制系统要求。

基于T-S模糊神经网络模型的编码器故障软闭环容错控制方法

针对舞台吊杆调速系统中速度反馈元件增量式编码器可能产生的丢码、断码等问题,为防止故障影响的传播,结合数据驱动技术提出了一种基于T-S模糊神经网络(T-S FNN)模型的编码器故障检测与软闭环容错控制方法。首先,利用系统正常运行时的历史数据建立系统较为精确的T-S FNN预测模型,并用实际编码器测量值与模型预测值相减获得残差信息;其次,将其残差实时数据通过改进的序贯概率比检验(SPRT)算法进行故障检测,以克服检测延迟确保故障检测的可靠性,当检测出故障时,再用T-S FNN模型的预测输出替代故障编码器的输出,实现软闭环方式下的容错运行;最后,针对编码器丢码、断码等故障,采用上述方法进行了软闭环容错控制的有效性仿真验证。仿真结果表明,该方法能够快速可靠地检测到编码器故障,并用预测的重构信息通过容错切换机制,及时、安全地以软闭环方式实现了对故障编码器的容错控制,提高了舞台吊杆调速系统运行的安全可靠性。

基于改进FNN-CCC的双伺服压力机同步控制策略研究

目的改善双伺服压力机同步控制策略的动态响应性能和鲁棒性,提升双伺服压力机的单轴跟踪精度和双轴同步精度,实现成形过程的高精度位置控制。方法建立双伺服压力机驱动系统数学模型,分析系统同步误差来源,结合模糊神经网络单轴控制算法,引入迭代学习律,设计一种改进模糊神经网络-交叉耦合(FNN-CCC)同步控制器。基于系统控制模型进行单轴阶跃响应特性与双轴正弦跟随特性仿真,搭建嵌入式双伺服压力机驱动系统试验平台,在偏载干扰条件下进行双轴同步控制试验,验证所提出理论的有效性。结果仿真结果表明,与模糊控制算法和BP神经网络控制算法相比,该控制器单轴控制算法的超调量分别减少了11.5%和25.5%,调节时间分别减少了48.8%和34.4%,具有更好的动态响应性能。与原控制器相比,改进后的交叉耦合同步控制器最大双轴同步误差降低了65.7%,同步控制精度有所提高。试验结果表明,与传统PID-交叉耦合控制器相比,改进的FNN-CCC控制器有更好的控制性能,在热冲压合模成形阶段,单轴跟踪误差分别减小了81.8%和75.0%,双轴同步误差减小了69.2%。结论所提出的同步控制策略在偏载干扰条件下具有较好的动态响应性能和鲁棒性,能够使同步误差快速收敛,提高了双伺服压力机驱动系统的单轴跟踪精度和双轴同步控制精度,实现了对双伺服压力机的高精度控制。

一种改进T-S模糊神经网络及其在装备保障力量动态部署中的应用

为提高模糊神经网络的全局搜索能力和学习效率,提出一种常规T-S 模糊神经网络(Takagi-Sugeno fuzzyneural network,T-S FNN)的改进方法。将遗传算法引入常规T-S FNN,采用其搜索功能来确定T-S FNN 的权值和参数,建立装备保障力量动态部署模型,对装备保障力量部署问题进行说明,通过改进方法训练学习,获取动态部署方案,并对其进行比较与分析。结果表明:该方法提高了自适应性和学习效率,可为装备保障力量动态部署的快速辅助决策提供参考。

基于模糊神经网络(FNN)的赤潮预警预测研究

为研究各种理化因子与赤潮藻类浓度间的非线性对应规律和有效预测赤潮藻类浓度,构建了基于BP算法的一个四层模糊神经网络模型。将模糊神经网络(FNN)技术引入赤潮预测研究,并与普通BP网络、RBF网络的结果作比较,结果表明,该模型能够较好地反演出各种理化因子与夜光藻密度的非线性对应变化规律,有更好的预测功能。

EFNN——一种增强型模糊神经网络

提出了一种较为广义的增强型模糊神经网络 ,以达到更高的非线性系统逼近能力 .该网络模糊规则的结论以函数形式给出 ,从而决定了网络的结构由两个子网络组成 ,即特征网络和功能网络 .网络采用梯度算法来修正网络的参数 .仿真表明 :该网络具有较强的非线性逼近能力和较快的学习速度 .

高斯激活函数特征值分解修剪技术的D-FNN算法研究

提出了一种D-FNN的新算法。其算法的最主要特点是:D-FNN选择高斯函数作为网络的激活函数和模糊系统的隶属函数,该算法不仅具有强大的全局映射泛化能力,而且在细化局部方面也有效;使用特征值分解修剪技术使得网络结构不会持续增长,可获得更为紧凑的D-FNN结构,避免了过拟合现象。最后通过对Her-mite多项式逼近能力来验证所提方案的有效性。仿真结果表明使用特征值分解修剪技术和高斯激活函数的D-FNN具有良好的性能。

基于QPSO-FNN的混沌时间序列预测

提出一种太阳黑子月均数混沌时序的模糊神经网络预测方法。该方法根据时间序列的延迟因子和饱和嵌入维数重构相空间,利用Lyapunov指数法判别时序系统的混沌特性,采用混合pi-sigma模糊神经推理方法拟合混沌吸引子特性。其中混合pi-sig-ma模糊神经网络以高斯基函数作为模糊子集的隶属度函数,在线动态调整隶属度函数和结论参数,并采用量子粒子群算法(QPSO)优化网络初始参数,提高预测准确度。该模型具有物理意义清晰、预测精度高以及预测结果确定等优点,仿真实验结果证明了该方法的有效性。

基于GD-FNN的金融股指预测模型

针对股票市场内部结构复杂性和外部因素多变性,构建一种基于椭圆基函数且能够动态调整网络结构的广义动态模糊神经网络模型对金融股指进行预测。以上证指数为例,在价格和成交量的基础上,将与股票市场密切相关的宏观经济指标引入模型预测指标体系。通过滑动时间窗对数据集进行处理,提高了模型预测准确性并降低了运算时间。与其他神经网络模型预测效果进行比较,结果表明提出的模型具有较好的预测效果。

基于FNN解耦纸张定量水分控制策略的研究与应用

针对纸张抄造过程中纸张定量与水分之间存在强耦合的问题,提出一种模糊神经网络(Fuzzy Neural Network,FNN)的解耦控制器,首先利用模糊控制对控制系统进行耦合补偿,然后利用神经网络的自学习、自调整能力不断在控制过程中优化模糊控制规则及解耦补偿参数,成功地将纸张抄造过程的多变量系统转变为单变量系统,实现纸张定量、水分之间的解耦。仿真结果表明,采用FNN解耦控制器具有较好的动态响应和较强的鲁棒性。将该策略应用于国内某造纸厂的纸板机控制系统,纸张定量控制精度为±3.9 g/m^2左右,水分控制精度为±1.0%左右,满足该纸机定量水分高精度控制要求。