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 ...

This paper reports on the use of Artificial Neural Networks (ANN) and Partial Least Squareregression (PLS) combined with NIR spectroscopy (900-1700 nm) to design calibration models for thedetermination of sugar content in sugar beet. In this study a total of 80 samples were used as the calibration set,whereas 40 samples were used for prediction. Three pre-processing methods, including Multiplic...

Improving time series forecastingaccuracy is an important yet often difficult task.Both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. In this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

A comprehensive set of ten artificial neural networks is developed to suggest optimal dimensions of type ‘C’ Bi-lobe tanks used in the shipping of liquefied natural gas. Multi-objective optimization technique considering the maximum capacity and minimum cost of vessels are implemented for determining optimum vessel dimensions. Generated populations from a genet...

Neuromodulation is considered a key factor in learning and memory in biological neural networks. Recent computational models of modulated plasticity have shown increased learning capabilities also in artificial neural networks. In this study, a rewardbased dynamic scenario is employed to test networks evolved with modulatory dynamics. The analysis on the networks shows that neuromodulation does...

life style and life expectancy of inhabitants have been affected by the increase of particulate matter 10 micrometers or less in diameter (pm10) in cities and this is why maximum pm10 concentrations have received extensive attention. an early notice system for pm10 concentrations necessitates an accurate forecasting of the pollutant. in the current study an artificial neural network was used t...

Fault detection and isolation (FDI) in dynamic data from an automotive engine air path using artificial neural networks is investigated. A generic SI mean value engine model is used for experimentation. Several faults are considered, including leakage, EGR valve and sensor faults, with different fault intensities. RBF neural networks are trained to detect and diagnose the faults, and also to in...

The application of pattern recognition (PR) techniques, artificial neural networks (ANNs), and nowadays hybrid artificial intelligence (AI) techniques in manufacturing can be regarded as consecutive elements of a process started two decades ago. The fundamental aim of the paper is to outline the importance of soft computing and hybrid AI techniques in manufacturing by introducing a genetic algo...

the aim of this study is quantity determining and evaluating of the quality position of teaching and learningprocess. so, the artificial neural networks were used for modeling nonlinear relationships to inspect and evaluate different models of service quality evaluation. the statistical population include 1070 peoples, which were master and phd students of faculty of agriculture of ferdowsiuniv...