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.

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。

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.

Using Neural Networks to Predict Secondary Structure for Protein Folding

Protein Secondary Structure Prediction (PSSP) is considered as one of the major challenging tasks in bioinformatics, so many solutions have been proposed to solve that problem via trying to achieve more accurate prediction results. The goal of this paper is to develop and implement an intelligent based system to predict secondary structure of a protein from its primary amino acid sequence by using five models of Neural Network (NN). These models are Feed Forward Neural Network (FNN), Learning Vector Quantization (LVQ), Probabilistic Neural Network (PNN), Convolutional Neural Network (CNN), and CNN Fine Tuning for PSSP. To evaluate our approaches two datasets have been used. The first one contains 114 protein samples, and the second one contains 1845 protein samples.

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.

Feedforward Neural Network for joint inversion of geophysical data to identify geothermal sweet spots in Gandhar,Gujarat,India

Artificial Neural Networks(ANNs)are used in numerous engineering and scientific disciplines as an automated approach to resolve a number of problems.However,to build an artificial neural network that is prudent enough to rely on,vast quantities of relevant data have to be fed.In this study,we analysed the scope of artificial neural networks in geothermal reservoir architecture.In particular,we attempted to solve joint inversion problem through Feedforward Neural Network(FNN)technique.In order to identify geothermal sweet spots in the subsurface,an extensive geophysical studies were conducted in Gandhar area of Gujarat,India.The data were acquired along six profile lines for gravity,magnetics and magnetotellurics.Initially low velocity zone was identified using refraction seismic technique in order to set a common datum level for other potential data.The depth of low velocity zone in Gandhar was identified at 11 m.The FNN backpropagation method was applied to gain the global minima of the data space and model space as desired.The input dataset fed to the inversion algorithm in the form of gravity,magnetic susceptibility and resistivity helped to predict the suitable model after network training in multiple steps.The joint inversion of data is conducive to understanding the subsurface geological and lithological features along with probable geothermal sweet spots.The results of this study show the geothermal sweet spots at depth ranging from 200 m to 300 m.The results from our study can be used for targeted zones for geothermal water exploitation.

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.

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 .

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。

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FNN的覆冰机器人越障机械臂轨迹跟踪控制

覆冰机器人除冰时要跨越各种障碍物。采用卡尔曼滤波学习算法,将自适应模糊神经网络控制器用于覆冰机器人越障时的机械臂轨迹跟踪控制,解决了BP算法实时性差的问题。经过仿真实验论证,该方法对覆冰机器人越障时的机械臂轨迹跟踪控制具有很好的效果,表明控制策略和理论分析的可行性。

基于规则产生准则与修剪策略的D-FNN算法研究

提出了一种D-FNN结构及其学习算法,该D-FNN的结构基于径向基神经网络。模糊规则的产生由输出误差或可容纳边界的有效半径决定。修剪技术的应用,使得网络结构能够保持紧凑,学习速度快,确保系统的泛化能力。对所提算法作了详细探讨,并与相关算法作比较,从而发现了D-FNN的独特思想。编写了D-FNN的仿真程序,对具体案例进行了仿真。结果表明,D-FNN具有紧凑的结构和优秀的性能。

FNN在数据库模糊查询中的研究

提出了一种基于模糊神经网络对数据库模糊查询的新方法,将模糊理论、模糊神经网络与数据库系统相结合。利用FNN直接生成隶属函数,来完成模糊查询,避免了直接指定隶属函数所带来的主观性,并能较好地反映数据的特征。

FNN上的反向传播学习算法

近几年来,模糊神经网络(FNN)的研究引起了广泛的注意。本文对FNN上的反向传播学习方法加以讨论。使用输入均值和输出权重参量来进行模糊化和反模糊化处理,学习的目的是调整这两个参量到合适的值。

基于FTA和FNN的液压系统故障诊断方法研究

针对液压系统故障的复杂性和不确定性等特点,传统的故障推理方法难以满足液压系统故障诊断的要求,提出了基于故障树分析和专家经验知识的模糊神经网络故障诊断方法。以起重设备液压系统为研究对象,建立故障树模型,基于故障树信息和专家经验知识,建立模糊神经网络诊断模型及并提取训练数据,在此基础上,运用统计参数法确定模糊预处理所需的模糊隶属函数。将训练好的网络模型应用于实例诊断,实验结果验证了该方法的实用性和有效性。

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。