The aim of this paper is to present and compare four different neuro-fuzzy approaches to the construction of fuzzy rule-based models for dynamic processes. These approaches have been applied to modelling of an industrial gas furnace system (Box-Jenkins benchmark). The following neuro-fuzzy systems have been considered: nfMod – the system proposed in this paper, the well-known ANFIS and NFIDENT ...

Load sensor is developed using fuzzy logic as well as neuro-fuzzy method. It is two inputs and one output sensor. Both fuzzy logic and neuro-fuzzy algorithms are simulated using MATLAB fuzzy logic toolbox. This paper outlines the basic difference between the results of fuzzy logic and neuro-fuzzy algorithms and provides the better algorithm for load sensor. Index Terms —fuzzy logic, load sensor...

We examine various and different approaches for the prediction of economic crisis periods of US economy. We examine the traditional econometric discrete choice Logit and Probit models then a feed-forward neural network (FFNN) model and finally we apply an Adaptive Neuro-Fuzzy Inference System (ANFIS). We examine the period 1950-2009, where we take as the in-sample or training period 1950-2005, ...

The objective of this work is to model simulation data a dust devils in Comsol using neuro-fuzzy methods (ANFIS: Adaptive Neuro Fuzzy Inference Systems) and perceptron neural networks. Since the number simulations performed was insufficient, we used Spline function increase amount data. results show that more effective than obtained models are excellent, with Nash -Sutcliffe criterion value abo...

predicting the effort of a successful project has been a major problem for software engineers the significance of which has led to extensive investigation in this area. One of the main objectives of software engineering society is the development of useful models to predict the costs of software product development. The absence of these activities before starting the project will lead to variou...

Problem statement: In this study, we present the development of genetic algorithm based neuro fuzzy technique for process grain sized in scheduling of parallel jobs with the help of real lIfe workload data. Approach: The study uses the rule based scheduling strategy for the scheduling and classIfies all possible scheduling strategies. The rule bases are developed with the help of the neuro fuzz...

ne of the greatest challenges for software developers is forecasting the development effort for a software system for the last decades. The capability to provide a good estimation on software development efforts is necessitated by the project managers. Software effort estimation models divided into two main categories: algorithmic and non-algorithmic. Developers should be able to achieve practi...

This paper presents a fuzzy perceptron as a generic model of multilayer fuzzy neural networks, or neural fuzzy systems, respectively. This model is suggested to ease the comparision of diierent neuro{fuzzy approaches that are known from the literature. A fuzzy perceptron is not a fuzziication of a common neural network architecture, and it is not our intention to enhance neural learning algorit...

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...