自适应神经模糊系统在托砖架最高温度预测中的应用

气流床气化炉在气化工业中得到了广泛应用，托砖架用于支撑气化炉耐火砖的重量，必须有足够的强度，温度是托砖架强度的重要影响因素。传统方法使用有限单元法计算，需要编制复杂的计算机程序。本文使用已有软件计算出托砖架最高温度点温度，对一个自适应神经模糊系统(ANFIS)进行训练，然后对未知数据进行预测，取得了准确的结果。     

自适应模糊神经网络在板料弯曲回弹预测中的应用

回弹是板料冲压成形中影响工件质量的重要因素，因为它是一个多变量相互作用的高度非线性问题，至今在解析和数值方法中未能找到一个很有效的解决途径。该文提出利用自适应模糊神经网络(ANFIS)对非线性问题的良好逼近能力，采用基于有限元方法获得训练样本，经训练后得到具有回弹预测能力的ANFIS模型。实验验证了该方法的有效性。     

ANFIS在舵面控制中的应用

任何一种飞机性能的发挥,必须首先保证舵面的精确控制.由于铰链力距(舵面负载)的高度非线性,使得舵面精确控制的工程实现相当困难.由于模糊推理系统和神经网络在解决非线性问题上取得的巨大成功,本文提出了基于自适应神经网络模糊推理系统(ANFIS)获得铰链力矩的方法,并以飞机纵向通道的升降舵面为例进行了仿真验证,结果表明,精度符合工程要求.     

基于ANFIS的非线性系统辨识研究

系统辨识是控制系统设计的基础，对非线性系统进行辨识是当前的难点；文献[1]提出了用模糊建模方法，文献[2]提出了用神经网络方法，在总结上述方法不足的基础上，该文提出了用自适应神经模糊推理系统(ANFIS)对非线性系统进行辨识的方法，仿真结果表明，ANFIS进行非线性系统辨识是可行的，其辨识精度很高。     

基于减法聚类和自适应神经模糊推理系统的递阶模糊系统的设计

提出了一种设计递阶模糊系统的简易而有效的方法.在得到一个单级模糊系统的基础上,用灵敏度分析法对每一个输入变量的重要性进行排序,从而确定每一级子系统的输入变量.利用减法聚类和自适应神经 模糊推理系统逐级对子系统进行训练.所得到的递阶模糊系统可进一步得到简化.仿真实例证实了设计方法的有效性.     

Using mega-fuzzification and data trend estimation in small data set learning for early FMS scheduling knowledge

Provided with plenty of data (experience), data mining techniques are widely used to extract suitable management skills from the data. Nevertheless, in the early stages of a manufacturing system, only rare data can be obtained, and built scheduling knowledge is usually fragile. Using small data sets, this research's purpose is improving the accuracy of machine learning for flexible manufacturing system (FMS) scheduling. The study develops a data trend estimation technique and combines it with mega-fuzzification and adaptive-network-based fuzzy inference systems (ANFIS). The results of the simulated FMS scheduling problem indicate that learning accuracy can be significantly improved using the proposed method involving a very small data set.     

基于自适应神经模糊推理系统的微波炉温度控制

针对微波炉温度对象的不确定性，作者提出了用自适应神经模糊推理系统(ANFIS)对微波炉温度进行自适应控制的自适应神经模糊控制器。对ANFIS训练及检验的结果表明，该自适应神经模糊控制器具有较高的控制精度，控制效果较好。     

Integrated Fuzzy DEA-ANFIS to Measure the Success Effect of Human Resource Spirituality

Workplace spirituality has gained attention as it is proven to be a contributor to organizational performance improvement. This paper aims to assess the impact of human resource spirituality on the success of organizational strategic change projects. The success of the projects is measured by the well-known criterion of deviation from the planned budget cost. Data collection is based on a questionnaire survey of 252 personnel in 36 large and medium-scale organizational change projects in power industry. The paper proposes an integrated algorithm of fuzzy data envelopment analysis (FDEA) and adaptive network-based fuzzy inference system (ANFIS) for measuring the pure effect of human resource spirituality on the success of organizational change projects in the power industry. It also achieves a verified tool capable of addressing complexity, nonlinearity, ambiguity, and fuzziness for measuring spirituality of human resources in the projects. Results show that spirituality of the project team has a significant effect on project success.     

Neural,fuzzy and neuro-fuzzy approach for concentration estimation of volatile organic compounds by surface acoustic wave sensor array

Present study evaluates application of adaptive neuro-fuzzy inference system (ANFIS) for concentration estimation of volatile organic compounds (VOCs) by analyzing response matrix of polymer-functionalized surface acoustic wave (SAW) sensor array. The performance of ANFIS is compared with that of subtractive clustering based fuzzy inference system (SC-FIS) and backpropagation artificial neural network (BP-ANN). For analysis, the raw SAW sensor array data is preprocessed by logarithmic scaling followed by dimensional autoscaling and the feature extraction by principal component analysis (PCA). For concentration prediction, the extracted feature vectors were fed as input to the three methods (ANFIS, SC-FIS and BP-ANN) independently. The performance of the three methods were evaluated on the basis of root mean square error (RMSE) and correlation value involving actual and estimated values of concentration. Five sets of SAW sensor array responses are analyzed. The analysis includes both experimental and synthetic (sensor model generated) data sets. It is found that the ANFIS has the least value of RMSE and highest value of correlation compared to SC-FIS and BP-ANN. This signifies the relative superiority of ANFIS method.     

Wind farm efficiency by adaptive neuro-fuzzy strategy

A wind power plant which consists of a group of wind turbines at a specific location is also known as wind farm. The engineering planning of a wind farm generally includes critical decision-making, regarding the layout of the turbines in the wind farm, the number of wind turbines to be installed and the types of wind turbines to be installed. Two primary objectives of optimal wind farm planning are to minimize the cost of energy and to maximize the net energy production or to maximize wind farm efficiency. In the design process of a wind farm the aerodynamic interactions between the single turbines have become a field of major interest. The upwind turbines in a wind farm will affect the energy potential and inflow conditions for the downwind turbines. The flow field behind the first row turbines is characterized by a significant deficit in wind velocity and increased levels of turbulence intensity. Consequently, the downstream turbines in a wind farm cannot extract as much power from the wind as the first row turbines. Therefore modeling wind farm power production, cost, cost per power unit and efficiency is necessary to find optimal layout of the turbines in the wind farm. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is designed and adapted to estimate wind farm efficiency according to turbines number in wind farm. This soft computing methodology is implemented using MATLAB/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.