In the last decade, the Computational Intelligence tools (CI), including Artificial Neural Networks (ANN) and Fuzzy Systems (FS), applying soft computing, became universal means for many applications. Because of their approximation and learning capabilities, the ANNs have been widely employed to dynamic process modeling, identification, prediction and control, (Boskovic & Narendra, 1995; Haykin...

Prediction of carbon dioxide (CO2) emissions is a critical step towards sustainable environment. In any country, increasing the amount CO2 an indicator increase in environmental pollution. this regard, current study applied three powerful and effective artificial intelligence tools, namely, feed-forward neural network (FFNN), adaptive network-based fuzzy inference system (ANFIS) long short-term...

In this paper, a standalone permanent magnet synchronous generator (PMSG) system is designed to generate power at maximum point (MPP). The variable speed operation of wind energy conversion consists PMSG, controlled rectifier and voltage source inverter co the load. Proportional integral (PI), sliding mode (SM), feed forward neural network (FFNN) control strategies are applied in field oriented...

In this present paper, the machine learning approach is used to optimize, model, and predict factors during drilling Nimonic C263 under dry mode. tough aero alloys, it required find a predictive model optimize in alloy before actual machining process. It helps avoid cost material cost. Experimental trails are planned based on Taguchi analysis, L27 orthogonal array was chosen. Speed, feed, angle...

To properly and quickly evaluate an object’s shape, in a manner that is suitable for real-time applications, set of parameters has been created the shape parametric description (SPD) elaborated. This solution focused on classification amber gemstones according to shape. improve results obtained by SPD, most popular machine learning algorithms were applied tested. The proposed method (i.e., SPD)...

The idea of using Feed-Forward Neural Networks (FFNNs) as regression functions for Nonlinear AutoRegressive eXogenous (NARX) models, leading to models herein named NARXs (NNARXs), has been quite popular in the early days machine learning applied nonlinear system identification, owing their simple structure and ease application control design. Nonetheless, few theoretical results are available c...

Drought is the most dangerous natural disaster. It differs from other disasters in that it occurs insidiously, its effects are revealed gradually, and persists for a long period. has huge, negative on both society ecosystems. In this study, values Standardized Precipitation Index (SPI) were used to generate drought estimation models by using Artificial Neural Networks (ANN). addition, probabili...

Abstract A reliable and accurate load forecasting method is key to successful energy management of smart grids. Due the non-linear relations in data generating process availability issues, remains a challenging task. Here, we investigate application feed forward artificial neural networks, recurrent networks crosslearning methods for day-ahead three days-ahead forecasting. The effectiveness pro...