The application of the neuro-fuzzy inference system to predict the compressive strength of concrete is presented in this study. The adaptive neuro-fuzzy inference system (ANFIS) is introduced for training and testing the data sets consisting of various parameters. To investigate the influence of various parameters which affect the compressive strength, 1551 data pairs are collected from the tec...

Several models have been created for Smart Grid resource-allocation problem. The principal purpose of the models is to connect power sources with appropriate sinks when considering the input parameters of power balance and consumption size, etc. Fuzzy logic is representative of these models. When creating the fuzzy model, the parameters and rule construction play the most significant role. For ...

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...

The use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed 5 Forward Neural Networks (FFNN), for forecasting of daily river f...

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...

In this study, an adaptive neuro-fuzzy inference system (ANFIS) was developed to determine the Nusselt number (Nu) along a wavy wall in a lid-driven cavity under mixed convection regime. Firstly, the main data set of input/output vectors for training, checking and testing of the ANFIS was prepared based on the numerical results of the lattice Boltzmann method (LBM). Then, the ANFIS was develope...

Problem statement: The statistical modeling for predicting network traffic has now become a major tool used for network and is of significant interest in many domains: Adaptive application, congestion and admission control, wireless, network management and network anomalies. To comprehend the properties of IP-network traffic and system conditions, many kinds of reports based on measured network...

Software Effort Prediction is the process of estimating the effort required to develop software. Effectively controlling the expensive investment of software development is achieved by accurately estimating the effort. Effort estimation at the early stage of software development is very difficult because of lot of uncertainty in input parameters which decides the software effort. Adaptive Neuro...

An approach in modeling collaborative and metacognitive data is presented in this paper. The proposed scheme, namely Collaboration/ Metacognition–Adaptive Network-based Fuzzy Inference System (C/M-ANFIS), uses neurofuzzy structure to adaptively infer on the relation between the above data in a meaningful way. More specifically, the collaborative and metacognitive data refer to the participant’s...

Conductive silicone rubber has great advantages for tactile sensing applications. The electrical behavior of the elastomeric material is rate-dependent and exhibit hysteresis upon cyclic loading. Several constitutive models were developed for mechanical simulation of this material upon loading and unloading. One of the successful approaches to model the time-dependent behavior of elastomers is ...