Application of ANFIS to exchange rate forecasting

The need of exchange rate forecasting in order to preventing its disruptive movements has engrossed many policy-makers and economists for many years. The determinants of exchange rate have grown manifold making its behavior complex, nonlinear and volatile so that nonlinear models have better performance for its forecasting. In this study the accuracy of ANFIS as the nonlinear model and ARIMA as the linear models for forecasting 2, 4 and 8 days ahead of daily Iran Rial/∈ and Rial/US$ was compared. Using forecast evaluation criteria we found that nonlinear model outperforms linear model in all three horizons.     

基于自适应神经模糊系统控制的船舶自动舵

介绍了自动舵的设计历史和发展方向,讨论了ANFIS控制器的实现方法,将ANFIS控制器应用到船舶航向非线性系统中,ANFIS自动舵的功能在于将实际航迹与计划航线之间的误差最小化,实施更合理的船舶航行控制。     

New Technology in Shopping: Forecasting Electronic Shopping With the Use of a Neuro-Fuzzy System

This article presents the application of neuro-fuzzy techniques in forecasting a new technology in shopping. Neural networks have been used successfully to forecast time series due to their significant properties of treating nonlinear data with self-learning capability. However, neural networks suffer the difficulty of dealing with qualitative information and the “black box” syndrome that more or less limits their applications in practice. To overcome the drawbacks of neural networks, in this study, we proposed a fuzzy neural network that is a class of adaptive networks functionally equivalent to a fuzzy inference system. The results derived from the experiment based on electronic sales indicated that the suggested fuzzy neural network could be an efficient system to forecast a new technology in shopping. Experimental results also show that the neuro-fuzzy approach outperforms the other two conventional models (AR and ARMA).     

Analyzing Massive Data Sets: An Adaptive Fuzzy Neural Approach for Prediction,with a Real Estate Illustration

Drawing useful predictions from vast accumulations of data is becoming critical to the success of an enterprise. Organizations’ databases grow exponentially from transactions with external stakeholders in addition to their own internal activities. An important organizational computing issue is that, as they grow, the databases become potentially more valuable and also more difficult to analyze. One example is predicting the value of residential real estate based on past comparable sales transactions. This is critical to several important sectors of the US economy including the mortgage finance industry and local governments that collect property taxes. The common methodology for dealing with such property valuation is based on multiple regression, although this methodology has been found to be deficient. Data mining methods have been proposed and tested as an alternative, but the results are very mixed. This article introduces a novel approach for improving predictions using an adaptive, neuro-fuzzy inference model, and illustrates its application to real estate property price prediction through the use of comparable properties. Although neuro-fuzzy–based approaches have been found to be effective for classification and estimation in many fields, there is very little existing work that investigates their potential in a real estate context. In addition, this article addresses several common problems in existing studies, such as small sample size, lack of rigorous data sampling, and poor model validation and testing. Our model is tested with real sales data from the assessment office in a large US city. The results show that the neuro-fuzzy model is superior in all of the test scenarios. The article also discusses and refines a unique technique to defining comparable properties to improve accuracy. Test results show very promising potential for this technique in mass appraisal in real estate and similar contexts when used with the neuro-fuzzy model.     

基于模糊函数的直接序列扩频信号参数估计改进方法

分析了基于模糊函数的直接序列扩频信号参数估计方法,研究了该方法在低信噪比环 境下针对信号码元传输速率、码片周期等参数估计结果的有效性。在此基础上,提出了一种 利用ANFIS（自适应神经模糊推理系统）网络先行去噪,再进行信号参数估计的改进方法。 计算机仿真结果证明了这一改 进方法的有效性。     

改进的混凝土质量管理戴明循环体系研究

研究了混凝土企业的生产现状及混凝土质量影响因素,提出了结构化、半结构化和非结构化分类方法。进而在质量管理戴明循环方法基础上提出了加速PDCA循环的理论。文中基于生产实时数据,综合混凝土生产人员素质、环境影响、设备性能等非结构化因素,采用自适应神经模糊推理系统（ANFIS）方法,建立了混凝土抗压强度模型,成功地预测改进措施对质量的影响程度与趋势,用于加速PDCA循环中检查（C）→处理（A）的过程。