Oil production estimation plays a critical role in economic plans for local governments and organizations. Therefore, many studies applied different Artificial Intelligence (AI) based methods to estimate oil countries. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is well-known model that has been successfully employed various applications, including time-series forecasting. However, the AN...

Abstract The geomechanical characteristics of a drill formation are uncontrollable factors that crucial to determining the optimal controllable parameters for drilling operation. In present study, data collected in wells drilled Marun oilfield southwestern Iran were used develop adaptive network-based fuzzy inference system (ANFIS) models parameters. specific energy (DSE) was calculated using s...

Bedrock unconfined compressive strength (UCS) is a key parameter in designing thegeosciences and building related projects comprising both the underground and surface rock structures. Determination of rock UCS using standard laboratory tests is a complicated, expensive, and time-consuming process, which requires fresh core specimens. However, preparing fresh cores is not always possible, especi...

In a modeling process of a real world problem, there usually are a huge number of potential inputs involved. A large number of inputs may increase the complexity in computation and cause other problems related to running time, memory spaces, etc. In the case of modeling process with large input, the number of inputs should be reduced and the priority inputs should be determined by an optimal se...

Predicting stock prices is an important objective in the financial world. This paper presents a novel forecasting model for stock markets on the basis of the wrapper ANFIS (Adaptive Neural Fuzzy Inference System)ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick. Two approaches of Raw-based and Signal-based are devised to extract the model’s input variables w...

Abstract Nonlinear properties and natural uncertainties in the rainfall–runoff process, necessity of extensive data, complexity physical models have caused researchers to use methods inspired by nature such as artificial neural networks, fuzzy systems, genetic algorithms (GA). The main purpose this study was estimate runoff employing Adaptive Neuro-Fuzzy Inference System (ANFIS) GA using access...