This paper focuses on the study of the error composition of a fuzzy decision tree induction method recently proposed by the authors, called soft decision trees. This error may be expressed as a sum of three types of error: residual error, bias and variance. The paper studies empirically the tradeo between bias and variance in a soft decision tree method and compares it with the tradeo of classi...

Process variables vary with time in certain applications. Monitoring systems let us avoid severe economic losses resulting from unexpected electric system failures by improving the system reliability and maintainability The installation and maintenance of such monitoring systems is easy when it is implemented using wireless techniques. ZigBee protocol, that is a wireless technology developed as...

in the present study, the energetic and economic modeling of lentil and chickpea production in esfahan province of iran was conducted using adaptive neuro-fuzzy inference system (anfis) and linear regression. data were taken by interviewing and visiting of 140 lentil farms and 110 chickpea farms during 2014-2015 production period. the results showed that the yield and total energy consumption w...

This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined...

Mohammad Muzzammil Department of Civil Engineering, AMU, Aligarh, UP 202002, India E-mail: [email protected] An accurate estimation of the maximum possible scour depth at bridge abutments is of paramount importance in decision-making for the safe abutment foundation depth and also for the degree of scour counter-measure to be implemented against excessive scouring. Despite analysis of...

This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for USD/JPY exchange rates forecasting. Previous work often used time series techniques and neural networks (NN). ANFIS can be used to better explain solutions to users than completely black-box models, such as NN. The proposed neurofuzzy rule based system applies some technical and fundamental indexes as input variables. In o...

Nonlinear Adaptive Noise Cancellation for 2-D Signals with Adaptive Neuro-Fuzzy Inference Systems Hao Qin Advisor: University of Guelph, 2004 Professor Simon X. Yang Neuro-fuzzy systems are capable of inducing rules from observations, where the adaptive neuro-fuzzy inference system (ANFIS) is an effective method that can be applied to a variety of domains such as pattern recognition, robotics, ...

Soft tissue modeling for the creation of a haptic simulator for training medical skills has been the focus of many attempts up to now. In soft tissue modeling the most important parameter considered is its being real-time, as well as its accuracy and sensitivity. In this paper, ANFIS approach is used to present a nonlinear model for soft tissue. The required data for training the neuro-fuzzy mo...

This paper presents an approach to predict the operating conditions of machine based on classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined...