Network traffic modeling significantly affects various considerations in networking, including network resource allocation, quality of service provisioning, network traffic management, congestion control, and bandwidth efficiency. These are very important issues in network protocol design, too. In this paper, a comprehensive comparison of modeling approaches of adaptive neuro fuzzy inference sy...

Since liquid tank systems are commonly used in industrial applications, system-related requirements results in many modeling and control problems because of their interactive use with other process control elements. Modeling stage is one of the most noteworthy parts in the design of a control system. Although nonlinear tank problems have been widely addressed in classical system dynamics, when ...

The prediction of elastic modulus is one of the fundamental facts of structural engineering studies. The performance of adaptive neuro-fuzzy inference system (ANFIS) for predicting the elastic modulus of normaland high-strength concrete was investigated. Results indicate that the proposed ANFIS modeling approach outperforms the other given models in terms of prediction capability. According to ...

28 Facing fierce competition in marketplaces, companies try to determine the optimal settings of design 29 attribute of new products from which the best customer satisfaction can be obtained. To determine 30 the settings, customer satisfaction models relating affective responses of customers to design attributes 31 have to be first developed. Adaptive neuro-fuzzy inference systems (ANFIS) was a...

Modeling of water flow in carbon nanotubes is still a challenge for the classic models of fluid dynamics. In this investigation, an adaptive-network-based fuzzy inference system (ANFIS) is presented to solve this problem. The proposed ANFIS approach can construct an input-output mapping based on both human knowledge in the form of fuzzy if-then rules and stipulated input-output data pairs. Good...

This paper investigates the effectiveness of four different soft computing methods, namely radial basis neural network (RBNN), adaptive neuro fuzzy inference system (ANFIS) with subtractive clustering (ANFIS-SC), ANFIS with fuzzy c-means clustering (ANFIS-FCM) and M5 model tree (M5Tree), for predicting the ultimate strength and strain of concrete cylinders confined with fiber-reinforced polymer...

Churn prediction is a useful tool to predict customer at churn risk. By accurate prediction of churners and non-churners, a company can use the limited marketing resource efficiently to target the churner customers in a retention marketing campaign. Accuracy is not the only important aspect in evaluating a churn prediction models. Churn prediction models should be both accurate and comprehensib...

overtaking a slow lead vehicle is a complex maneuver because of the variety of overtaking conditions and driver behavior. in this study, two novel prediction models for overtaking behavior are proposed. these models are derived based on multi-input multi-output adaptive neuro-fuzzy inference system (manfis). they are validated at microscopic level and are able to simulate and predict the future...

The paper presents an adaptive neuro-fuzzy inference system (ANFIS) based modeling approach to predict the monthly global solar radiation (MGSR) in Bhubaneswar. Comparisons of the predicted and measured value of monthly global solar radiation (GSR) on a horizontal surface are presented. The input parameters of the model used in this paper are sunshine duration, temperature, humidity, clearness ...

Though the hi-tech industry has focused on value innovation and improving the quality of the new product development (NPD) process to drive new product performance, new product success has not changed dramatically over the years. This study presents a novel approach based on structural equation modeling (SEM) and adaptive neuro-fuzzy inference system (ANFIS) to forecast value innovation and the...