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...

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...

The islanding methods are classified in terms of the islanding principle and the distributed Generation (DG). Islanding detection techniques are mainly divided into three types for distribution systems: Remote, local and communication based techniques. Passive methods are based on the information available on the DG site at the point of common coupling (PCC) with the utility grid. A new approac...

Drought plays a crucial role in agriculture, especially farming and has significant impact on the environment. The present study focuses forecast of drought using one hybrid artificial neural network namely Adaptive NeuroFuzzy Inference System (ANFIS). For this study, 39 years monthly precipitation value Erode district are used. Firstly, values, Standard Precipitation Index (SPI) values compute...

Weather forecasting is the application of science and technology to predict the state of the atmosphere for a future time at a given location. It is carried out by collecting quantitative data about the current state of the atmosphere and past and/or present experiences. In this study Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple linear regression model were used to analyze metrolo...

To accurately estimate the state of charge of a lithium-ion battery pack used in electric vehicles, a neurofuzzy system is proposed. The subtractive clustering is used to determine the structure and the initial parameters of the neuro-fuzzy system to reduce heuristic errors. The algorithm of adaptive neuro-fuzzy inference (ANFIS) is adopted to optimize the parameters of the neuro-fuzzy system. ...

This paper presents a comparative analysis of the performance of fuzzy approaches on the task of predicting customer review ratings using a computational intelligence framework based on a genetic algorithm for data dimensionality reduction. The performance of the Fuzzy C-Means (FCM), a neurofuzzy approach combining FCM and the Adaptive Neuro Fuzzy Inference System (ANFIS), and the Simplified Fu...

the impact of air pollution and environmental issues on public health is one of the main topics studied in manycities around the world. ozone is a greenhouse gas that contributes to global climate. this study was conducted topredict and model ozone of yazd in the lower atmosphere by an adaptive neuro-fuzzy inference system (anfis). allthe data were extracted from 721 samples collected daily ove...

Parameters estimation; Induction motor; Squirrel-cage; Artificial neural network; Adaptive neuro-fuzzy inference system Abstract In the transient behavior analysis of a squirrel-cage induction motor, the parameters of the single-cage and double-cage models are studied. These parameters are usually hard to obtain. This paper presents two new methods to predict the induction motor parameters in t...