APPLICATION STUDY ON ADAPTIVE NEURAL FUZZY INFERENCE MODEL IN COMPLEX SOCIAL-TECHNICAL SYSTEM

The adaptive neural fuzzy inference system （ANFIS） is used to make a ease study considering features of complex social-technical system with the target of increasing organizational efficiency of public scientific research institutions. An integrated ANFIS model is built and the effectiveness of the model is verified by means of investigation data and their processing results. The model merges the learning mechanism of neural network and the language inference ability of fuzzy system, and thereby remedies the defects of neural network and fuzzy logic system. Result of this case study shows that the model is suitable for complicated socio-technical systems and has bright application perspective to solve such problems of prediction, evaluation and policy-making in managerial fields.

Experimental investigation and adaptive neural fuzzy inference system prediction of copper recovery from flotation tailings by acid leaching in a batch agitated tank

The potential of copper recovery from flotation tailings was experimentally investigated using a laboratory-mixing tank. The experiments were performed with solid weight percentages of 30 wt%, 35 wt%, 40 wt% and 45 wt% in water. The measurements revealed that adding sulfuric acid all at once to the tank rapidly increased the efficiency of the leaching process, which was attributed to the rapid change in the acid concentration. The rate of iron dissolution from tailings was less than when the acid was added gradually. The sample with 40 wt% solid is recommended as an appropriate feed for the recovery of copper. The adaptive neural fuzzy system(ANFIS) was also used to predict the copper recovery from flotation tailings. The back-propagation algorithm and least squares method were applied for the training of ANFIS. The validation data was also applied to evaluate the performance of these models. Simulation results revealed that the testing results from these models were in good agreement with the experimental data.

Comparison between Neural Network and Adaptive Neuro-Fuzzy Inference System for Forecasting Chaotic Traffic Volumes

This paper applies both the neural network and adaptive neuro-fuzzy inference system for forecasting short-term chaotic traffic volumes and compares the results. The architecture of the neural network consists of the input vector, one hidden layer and output layer. Bayesian regularization is employed to obtain the effective number of neurons in the hidden layer. The input variables and target of the adaptive neuro-fuzzy inference system are the same as those of the neural network. The data clustering technique is used to group data points so that the membership functions will be more tailored to the input data, which in turn greatly reduces the number of fuzzy rules. Numerical results indicate that these two models have almost the same accuracy, while the adaptive neuro-fuzzy inference system takes more time to train. It is also shown that although the effective number of neurons in the hidden layer is less than half the number of the input elements, the neural network can have satisfactory performance.

Characteristics Prediction Method of Electro-hydraulic Servo Valve Based on Rough Set and Adaptive Neuro-fuzzy Inference System

Synthesis characteristics of the electro-hydraulic servo valve are key factors to determine eligibility of the hydraulic production. Testing all synthesis characteristics of the electro-hydraulic servo valve after assembling leads to high repair rate and reject rate, so accurate prediction for the synthesis characteristics in the industrial production is particular important in decreasing the repair rate and the reject rate of the product. However, the research in forecasting synthesis characteristics of the electro-hydraulic servo valve is rare. In this work, a hybrid prediction method was proposed based on rough set（RS） and adaptive neuro-fuzzy inference system（ANFIS） in order to predict synthesis characteristics of electro-hydraulic servo valve. Since the geometric factors affecting the synthesis characteristics of the electro-hydraulic servo valve are from workers＇ experience, the inputs of the prediction method are uncertain. RS-based attributes reduction was used as the preprocessor, and then the exact geometric factors affecting the synthesis characteristics of the electro-hydraulic servo valve were obtained. On the basis of the exact geometric factors, ANFIS was used to build the final prediction model. A typical electro-hydraulic servo valve production was used to demonstrate the proposed prediction method. The prediction results showed that the proposed prediction method was more applicable than the artificial neural networks（ANN） in predicting the synthesis characteristics of electro-hydraulic servo valve, and the proposed prediction method was a powerful tool to predict synthesis characteristics of the electro-hydraulic servo valve. Moreover, with the use of the advantages of RS and ANFIS, the highly effective forecasting framework in this study can also be applied to other problems involving synthesis characteristics forecasting.

Temperature modeling and control of Direct Methanol Fuel Cell based on adaptive neural fuzzy technology

Aiming at on-line controlling of Direct Methanol Fuel Cell （DMFC） stack, an adaptive neural fuzzy inference technology is adopted in the modeling and control of DMFC temperature system. In the modeling process, an Adaptive Neural Fuzzy Inference System （ANFIS） identification model of DMFC stack temperature is developed based on the input-output sampled data, which can avoid the internal complexity of DMFC stack. In the controlling process, with the network model trained well as the reference model of the DMFC control system, a novel fuzzy genetic algorithm is used to regulate the parameters and fuzzy rules of a neural fuzzy controller. In the simulation, compared with the nonlinear Proportional Integral Derivative （PID） and traditional fuzzy algorithm, the improved neural fuzzy controller designed in this paper gets better performance, as demonstrated by the simulation results.

Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

When designing solar systems and assessing the effectiveness of their many uses,estimating sun irradiance is a crucial first step.This study examined three approaches(ANN,GA-ANN,and ANFIS)for estimating daily global solar radiation(GSR)in the south of Algeria:Adrar,Ouargla,and Bechar.The proposed hybrid GA-ANN model,based on genetic algorithm-based optimization,was developed to improve the ANN model.The GA-ANN and ANFIS models performed better than the standalone ANN-based model,with GA-ANN being better suited for forecasting in all sites,and it performed the best with the best values in the testing phase of Coefficient of Determination(R=0.9005),Mean Absolute Percentage Error(MAPE=8.40%),and Relative Root Mean Square Error(rRMSE=12.56%).Nevertheless,the ANFIS model outperformed the GA-ANN model in forecasting daily GSR,with the best values of indicators when testing the model being R=0.9374,MAPE=7.78%,and rRMSE=10.54%.Generally,we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved,and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory.The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

Modelling and control PEMFC using fuzzy neural networks

Proton exchange membrane generation technology is highly efficient, clean and considered as the most hopeful “green” power technology. The operating principles of proton exchange membrane fuel cell (PEMFC) system involve thermo-dynamics, electrochemistry, hydrodynamics and mass transfer theory, which comprise a complex nonlinear system, for which it is difficult to establish a mathematical model and control online. This paper first simply analyzes the characters of the PEMFC; and then uses the approach and self-study ability of artificial neural networks to build the model of the nonlinear system, and uses the adaptive neural-networks fuzzy infer system (ANFIS) to build the temperature model of PEMFC which is used as the reference model of the control system, and adjusts the model parameters to control it online. The model and control are implemented in SIMULINK environment. Simulation results showed that the test data and model agreed well, so it will be very useful for optimal and real-time control of PEMFC system.

Adaptive neuro fuzzy inference system for classification of water quality status

An adaptive neuro fuzzy inference system was used for classifying water quality status of river. It applied several physical and inorganic chemical indicators including dissolved oxygen, chemical oxygen demand, and ammonia-nitrogen. A data set （nine weeks, total 845 observations） was collected from 100 monitoring stations in all major river basins in China and used for training and validating the model. Up to 89.59% of the data could be correctly classified using this model. Such performance was more competitive when compared with artificial neural networks. It is applicable in evaluation and classification of water quality status.

Diagnosis of Neem Leaf Diseases Using Fuzzy-HOBINM and ANFIS Algorithms

This paper proposes an approach to detecting diseases in neem leaf that uses a Fuzzy-Higher Order Biologically Inspired Neuron Model(F-HOBINM)and adaptive neuro classifier(ANFIS).India exports USD 0.28-million worth of neem leaf to the UK,USA,UAE,and Europe in the form of dried leaves and powder,both of which help reduce diabetesrelated issues,cardiovascular problems,and eye disorders.Diagnosing neem leaf disease is difficult through visual interpretation,owing to similarity in their color and texture patterns.The most common diseases include bacterial blight,Colletotrichum and Alternaria leaf spot,blight,damping-off,powdery mildew,Pseudocercospora leaf spot,leaf web blight,and seedling wilt.However,traditional color and texture algorithms fail to identify leaf diseases due to irregular lumps and surfaces,and rough ridges,as the classification time involved takes as long as a week.The proposed F-HOBINM algorithm recognizes the leaf intensity through the leaky capacitor,and uses subjective intensity and physical stimulus to interpret the diagnosis.Further,the processed leaf images from the HOBINM algorithm are applied to the ANFIS classifier to identify neem leaf diseases.The experimental results show 92.18%accuracy from a database of 1,462 neem leaves.

Nonlinear Modeling and Neuro-Fuzzy Control of PEMFC

The proton exchange membrane generation technology is highly efficient, and clea n and is considered as the most hopeful “green” power technology. The operatin g principles of proton exchange membrane fuel cell (PEMFC) system involve thermody namics, electrochemistry, hydrodynamics and mass transfer theory, which comprise a complex nonlinear system, for which it is difficult to establish a mathematic al model and control online. This paper analyzed the characters of the PEMFC; an d used the approach and self-study ability of artificial neural networks to bui ld the model of nonlinear system, and adopted the adaptive neural-networks fuzz y infer system to build the temperature model of PEMFC which is used as the refe rence model of the control system, and adjusted the model parameters to control online. The model and control were implemented in SIMULINK environment. The resu lts of simulation show the test data and model have a good agreement. The model is useful for the optimal and real time control of PEMFC system.

基于自适应神经模糊推理的竖向地震动参数预测模型

为解决当前竖向地震动参数估计不确定性较大的问题,提出基于随机自适应神经模糊推理的竖向地震动预测模型。首先,以太平洋地震工程研究中心的PEER NGA强震数据库为基础,将地震震级、断层距及场地平均剪切波波速3个参数作为输入,将竖向地震动峰值加速度PGA及峰值速度PGV作为估计目标,建立训练数据集及测试数据集;其次,根据地震动参数预测方程,利用随机自适应神经模糊推理技术构建竖向地震动参数ANFIS预测模型,并给出全面的结果分析及信度检验。结果表明,ANFIS模型的竖向地震动衰减结果呈现出近场大震饱和效应、场地放大效应及软土减震效应;ANFIS竖向地震动模型平均绝对百分比误差MAPE约为0.15,与Campbell-Bozorgnia地震动衰减关系相比,PGA与PGV预测的准确率分别提升约77.4%和62.7%,具有较好的可信度。

基于优化模糊推理系统的电力变压器故障检测方法

为了提高电力变压器故障检测的准确性和稳定性,提出一种基于一维卷积神经网络和优化自适应神经模糊推理系统的检测方法;将利用溶解气体分析法得到的14个特征属性作为自适应神经模糊推理系统的初始未处理输入,通过一维卷积神经网络从中选择8个最具指示性的属性;采用改进帝王蝶优化算法对自适应神经模糊推理系统进行训练,并通过真实数据集实验与其他电力变压器故障诊断算法进行检测性能对比。结果表明,所提出方法的电力变压器故障检测准确率达98.91%,50次独立运行中故障检测的标准偏差为±0.01,具有检测准确性高、性能稳健、运行时间短的优点。

人工智能在辅助青光眼性眼底病变的应用前景

人工智能(AI)在青光眼性眼底病变(GON)角膜内应用的详细视图。为眼科疾病相关临床信息提供诊断依据,对正常及异常角膜实施准确评估,相较于以往的信息技术,AI具有准确性,还可以无创性的综合分析。神经网络的机器深度学习方式包括识别、定位及量化大量的眼科疾病病理特征,提供准确诊断及提高效率。AI开始被广泛应用于视网膜病变、眼前节疾病以及眼底病变的诊疗中。其中AI医学影像判读可协助临床医生对青光眼性眼底病变(GON)的筛查、辅助诊断做出判断,同时对面临诊断率及准确率的研究与挑战并存。

基于GRA-AIFNN的住宅环境性能需求研究

为了让住宅环境更好的反映出居民的个性化需求,提出一种基于GRA-AIFNN的住宅环境性能评价模型,通过对居民满意度影响较大的关键因素进行模糊语言推理,利用自适应直觉模糊神经网络对输入语言进行训练,得出各因素的得分与评价等级。结果表明:交通道路与站点和绿植种类的得分为0.8469和0.9260,等级为A级,需求度较高;停车场地、运动器械和节能设施的得分分别为0.4159、0.5202和0.7722,等级为B级,需求度适中;景观设计、采光效果和物业服务的得分分别为0.2567、0.1972和0.3310,等级为C级,需求度偏低。该研究可为房地产开发商对住宅环境性能综合评判提供科学可靠的技术指导与支持。

质子交换膜燃料电池阴阳极恒压差控制策略研究

阴阳极压力控制对提高燃料电池系统性能至关重要。本文阳极侧使用自适应神经模糊推理系统(ANFIS)作为其控制策略,实现对阴极压力的跟随;阴极侧使用RBF-PID控制器作为其过氧比控制策略。仿真结果表明,ANFIS可迅速将阴阳极压力差稳定在需求值并显著降低流量波动。

基于T-S模型的自适应神经模糊推理系统及其在热工过程建模中的应用

在工业热工过程控制中,被控对象动态特性往往表现出非线性、时变性、大迟延和大惯性等特点,这使得难以对其建立比较精确的模型,从而难于精确表达热工过程及实施整体优化控制。针对热工过程建模难的现状,为达到建立精确非线性模型的目的,提出1种基于T-S模型的自适应神经模糊系统(ANFIS)模糊建模方法。该方法通过对模糊系统的结构辨识和参数辨识,使神经模糊网络能够自主、迅速有效地收敛到要求的输入和输出关系,从而达到精确建模的目的。仿真结果验证了所提出的算法的有效性,将其应用到热工过程建模中可获得高精度的非线性模型。

基于自适应直觉模糊推理的目标识别方法

将自适应神经网络——直觉模糊推理系统(adaptive neuro-intuitionistic fuzzy inference system,ANIFIS)引入信息融合领域,提出一种基于自适应直觉模糊推理的目标识别方法。首先,分析了现有目标识别方法的特点与局限性,建立了基于ANIFIS的Takagi-Sugeno型目标识别模型。其次,设计了系统变量属性函数和推理规则,确定了各层输入输出计算关系及合成计算表达式。再次,设计了学习算法对网络和规则进行训练修改。最后,以20批典型目标的类型识别为例,分析比较基于直觉模糊推理及ANIFIS推理的输出结果与识别精度。仿真结果表明该方法是一种比较实用、有效的决策融合方法。

变量喷药自适应神经模糊控制器设计与仿真

为了减少除草剂用量,采用变量喷施除草剂方式进行除草.根据分别建立的杂草面积、喷药机械行驶速度与喷药量关系模型,得知杂草面积和喷药机械行驶速度是影响变量喷施效果的主要因素.为了获取喷药量与车速及杂草面积关系试验数据,设计了室内变量喷药试验台,使用DSP处理器及编码器分别得到杂草面积及喷药机械前进速度信息.结合所获试验数据,设计了一种基于自适应神经模糊推理(ANFIS)的双输入、单输出控制器.对控制器设计过程中输入输出变量的选取、隶属函数的选择及控制器的训练等进行了研究,数据经过30次训练后误差为1.47×10-5.对控制器的速度采集、串行通信、电磁阀驱动等硬件电路及模糊控制软件流程,进行了设计.在Matlab中建立了自适应神经模糊控制仿真模型,仿真结果表明:在喷头打开时间为0.2 s,喷药机械速度为0～1 m/s,杂草面积在0～100 cm2时,控制器可自动调节喷药量在0～4 mL变化.与采用传统模糊控制方式相比,该控制器自适应性强,具有较好的应用前景.

三级倒立摆的自适应神经模糊控制(英文)

在三级倒立摆(TIP)系统中, 应用神经网络与模糊控制相结合的自适应神经模糊推理系统(adaptive neural-fuzzy inference system), 根据样本数据调整隶属函数和控制规则参数, 使得训练后ANFIS控制器很好地模拟期望的输入输出数据. 仿真结果表明所设计的ANFIS控制器对三级倒立摆系统的稳定控制是可行的. 与LQR控制相比, 基于ANFIS控制的倒立摆系统具有良好的动态性能和抗干扰性能.

组合导航智能信息融合自适应滤波算法分析

针对当前自适应组合导航系统算法的研究趋势,总结了卡尔曼滤波技术的缺陷和利用智能融合技术提高滤波器性能的设计思想。对模糊控制自适应算法(FIR AKF)、神经网络自适应算法(NN AKF)和自适应神经网络模糊推理自适应算法(ANFIS AKF)进行了分析。着重研究FIR AKF采用滤波器新息序列和外系统状态的模糊控制器关键的模糊规则设计问题;分析NN AKF在组合导航系统模型调整、故障检测和隔离中的应用方法,并给出ANFIS AKF利用神经网络自动生成推理规则和建立自适应组合导航系统的基本方法。