One of the most significant and effective criteria in the process of cutting dimensional rocks using the gang saw is the maximum energy consumption rate of the machine, and its accurate prediction and estimation can help designers and owners of this industry to achieve an optimal and economic process. In the present research work, it is attempted to study and provide models for predicting the m...

Abstract Energy forecasting is crucial for efficient energy management and planning future needs. Previous studies have employed hybrid modeling techniques, but insufficient attention has been given to hyper-parameter tuning parameter selection. In this study, we present a model, which combines fuzzy c-means clustered adaptive neuro-fuzzy inference system (ANFIS) genetic algorithm (GA), named G...

—Machine prognosis is a significant part of Condition-Based Maintenance (CBM) and intends to monitor and track the time evolution of the fault so that maintenance can be performed or the task be terminated to avoid a catastrophic failure. A new prognostic method is developed in this paper using adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering. The ANFIS is traine...

Weather prediction is an ever challenging area of investigation for scientists. The Adaptive Neuro-Fuzzy Inference System (ANFIS) has been widely used for modeling different kinds of nonlinear systems including rainfall forecasting. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) combines the capabilities of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) to solve different ki...

This work presents a method based on an adaptive neuro-fuzzy inference system (ANFIS) for modeling protein secondary structure prediction which aims at acquiring the unknown structure information of target protein directly from its sequence data which is available. The number of input variables and inference rules are commonly too large, sometimes even huge, to make the model building feasible....

Modeling human operator’s behavior as a controller in a closed-loop control system recently finds applications in areas such as training of inexperienced operators by expert operator’s model or developing warning systems for drivers by observing the driver model parameter variations. In this research, first, an experimental setup has been developed for collecting data from human operators as th...

This paper proposes an adaptive neurofuzzy interface system ANFIS approach to identify the real power transfer between generators. Based on solved load flow results, it first uses modified nodal equation method MNE to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to train the designed ANFIS. It also incorpor...

Modeling of stream flow–suspended sediment relationship is one of the most studied topics in hydrology due to itsessential application to water resources management. Recently, artificial intelligence has gained much popularity owing toits application in calibrating the nonlinear relationships inherent in the stream flow–suspended sediment relationship. Thisstudy made us of adaptive neuro-fuzzy ...

In this research paper, ANFIS modeling and validation of Vestas 660 kW wind turbine based on actual data obtained from Eoun-Ebn-Ali wind farm in Tabriz, Iran, and FAST is performed. The turbine modeling is performed by deriving the non-linear dynamic equations of different subsystems. Then, the model parameters are identified to match the actual response. ANFIS is an artificial intelligent tech...

Coagulation is an important component of water treatment. Determining the optimal coagulant dosage is vital, as insufficient dosage will result in unqualified water quality. Traditionally, jar tests and operators’ own experience are used to determine the optimum coagulant dosage. However, jar tests are time-consuming and less adaptive to changes in raw water quality in real time. When an unusua...