A multi-objective optimization (MOO) of two-element wing models with morphing flap by using computational fluid dynamics (CFD) techniques, artificial neural networks (ANN), and non-dominated sorting genetic algorithms (NSGA II), is performed in this paper. At first, the domain is solved numerically in various two-element wing models with morphing flap using CFD techniques and lift (L) and drag ...

estimate of sediment load is required in a wide spectrum of water resources engineering problems. the nonlinear nature of suspended sediment load series necessitates the utilization of nonlinear methods to simulate the suspended sediment load. in this study artificial neural networks (anns) are employed to estimate daily suspended sediment load. two different ann algorithms, multi layer percept...

NeuroEvolution (NE) is the application of evolutionary algorithms to Artificial Neural Networks (ANN). This paper reports on an investigation into the relative importance of weight evolution and topology evolution when training ANN using NE. This investigation used the NE technique Cartesian Genetic Programming of Artificial Neural Networks (CGPANN). The results presented show that the choice o...

Many artificial intelligence (AI) techniques are inspired by problem-solving strategies found in nature. Robustness is a key feature in many natural systems. This paper studies robustness in artificial neural networks (ANNs) and proposes several novel, nature inspired ANN architectures. The paper includes encouraging results from experimental studies on these networks showing increased robustne...

conclusions results showed that ann is a powerful tool for predicting sorption coefficients using soil organic carbon content variations. results the multilayer perceptron (mlp) artificial neural networks (ann) model with 1-6-1 layout, predicted nearly 98% of the variance of kd as well as 94% of the koc variations with soil organic carbon content. materials and methods data of this study were d...

Artificial neural networks are bio-inspired models used mainly in the problems solution with nonlinear behavior. Reconfigurable devices (FPGA) are widely employed in the implementation of artificial neural networks. The contributions of this work is in the implementation of a Multilayer ANN with four neurons, one hidden layer and hyperbolic tangent for activation function. A nonlinear function ...

Artificial Neural Networks are bio-inspired mechanisms for intelligent decision support. Artificial Neural Networks have got remarkable ability to learn and derive meaning from large amount of domain data. The paper discusses the limitations of ANN from user interface perspective and emphasizes how fuzzy logic can serve as an effective user interface tool for ANN. The paper presents a framework...

In recent years, wind energy has a remarkable growth in the world, but one of the important problems of power generated from wind is its uncertainty and corresponding power. For solving this problem, some approaches have been presented. Recently, the Artificial Neural Networks (ANN) as a heuristic method has more applications for this propose. In this paper, short-term (1 hour) and mid-term (24...

Ensemble learning methods have received remarkable attention in the recent years and led to considerable advancement in the performance of the regression and classification problems. Bagging and boosting are among the most popular ensemble learning techniques proposed to reduce the prediction error of learning machines. In this study, bagging and gradient boosting algorithms are incorporated in...

This study develops a new approach for forecasting shear Strength of concrete beam without stirrups based on the artificial neural networks (ANN). Proposed ANN considers geometric and mechanical properties of cross section and FRP bars, and shear span-depth ratio. The ANN model is constructed from a set of experimental database available in the past literature. Efficiency of the ANN model was c...