In this paper, the hydro power plant model (with penstock-wall elasticity and compressible water column effect) is simulated at random load disturbance variation with output as turbine speed for random gate position as input. The multilayer perceptron neural network (i.e. NNARX) and fused neural network and fuzzy inference system (i.e. ANFIS) for identification of turbine speed as output variab...

Intelligent autonomous robots and multiagent systems, having different skills and capabilities for specific subtasks, have the potential to solve problems more efficiently and effectively. In this paper both f i m y logic (FL) and subtractive clustering (SC) are used for the design of autonomous robot behaviours. The design procedure is conducted in two stages: first subtractive clustering is a...

Accurate energy production forecasting is critical when planning for the economic development of a country. A deep learning approach based on Long Short-Term Memory (LSTM) to forecast one-day-ahead from run-of-river hydroelectric power plants in Turkey was introduced present study. In addition LSTM network, three different data-driven methods, namely, adaptive neuro-fuzzy inference system (ANFI...

The development of an excavation damaged zone (EDZ) around an underground excavation can change the physical, mechanical and hydraulic behaviors of the rock mass near an underground space. This might result in endangering safety, achievement of costs and excavation planed. This paper presents an approach to build a prediction model for the assessment of EDZ, based upon rock mass characteristics...

Present study investigates the capabilities of six distinct machine learning techniques such as ANFIS network with fuzzy c-means (ANFIS-FCM), grid partition (ANFIS-GP), subtractive clustering (ANFIS-SC), feed-forward neural (FNN), Elman (ENN), and long short-term memory (LSTM) in one-day ahead soil temperature (ST) forecasting. For this aim, daily ST data gathered at three different depths 5 cm...

Objective: Soil temperature serves as a key variable in hydrological investigations to determine soil moisture content as well as hydrological balance in watersheds. The ingoing research aims to shed lights on potential of artificial neural networks (ANNs) and Neuro-Fuzzy inference system (ANFIS) to simulate soil temperature at 5-100 cm depths. To satisfy this end, climatic and...

Objective: Soil temperature serves as a key variable in hydrological investigations to determine soil moisture content as well as hydrological balance in watersheds. The ingoing research aims to shed lights on potential of artificial neural networks (ANNs) and Neuro-Fuzzy inference system (ANFIS) to simulate soil temperature at 5-100 cm depths. To satisfy this end, climatic and...

Knowledge of the distinctive frequencies and amplitudes broken rotor bar (BRB) faults in induction motor (IM) is essential for most fault diagnosis methods. Fast Fourier transform (FFT) widely applied to diagnose within BRBs. However, this method does not provide satisfactory results if it directly stator current signal at low slip because a high-resolution spectrum required separate different ...