In the present work, an artificial neural network (ANN) model was used to study the quantitative structure retention relationship (QSRR) of retention index (RI) of some volatile compounds in natural cocoa and conched chocolate powder. Molecular structural descriptors are selected using genetic algorithm to construct the nonlinear QSRR models, kernel partial least squares PLS (KPLS) and Levenber...

Anti–correlation has been used in training neural network ensembles. Negative correlation learning (NCL) is the state of the art anti–correlation measure. We present an alternative anti–correlation measure, RTQRT–NCL, which shows significant improvements on our test examples for both artificial neural networks (ANN) and genetic programming (GP) learning machines. We analyze the behavior of the ...

Abstract Anti–correlation has been used in training neural network ensembles. Negative correlation learning (NCL) is the state of the art anti–correlation measure. An alternative anti–correlation measure, RTQRT–NCL, which shows significant improvements on our test examples for both artificial neural networks (ANN) and genetic programming (GP) learning machines, is presented. We analyze the beha...

Soft computing techniques have been widely used during the last two decades for nonlinear system modeling, specifically as predictive tools. In this study, the performances of two well-known soft computing predictive techniques, artificial neural network (ANN) and genetic programming (GP), are evaluated based on several criteria, including over-fitting potential. A case study in punching shear ...

Bedload transport is an essential component of river dynamics and estimation of its rate is important to many aspects of river management. In this study, measured bedload by Helley- Smith sampler was used to estimate the bedload transport of Kurau River in Malaysia. An artificial neural network, genetic programming and a combination of genetic programming and a neural network were used to estim...

Bedload transport is an essential component of river dynamics and estimation of its rate is important to many aspects of river management. In this study, measured bedload by Helley- Smith sampler was used to estimate the bedload transport of Kurau River in Malaysia. An artificial neural network, genetic programming and a combination of genetic programming and a neural network were used to estim...

This study focuses on using an artificial neural network (ANN) based model for predicting the performance of high strength concrete (HSC) beams strengthened with surface mounted FRP laminates. Eight input parameters such as geometrical properties of the beam and mechanical properties of FRP laminates were considered for this study. Back propagation network with Lavenberg-Marquardt algorithm has...

Objective techniques for assessing and classifying voice quality for patients recovering from treatment for cancer of the larynx have largely focussed on they use of Artificial Neural Networks (ANN). The results of a preliminary study are reported, where a Genetic Programming (GP) has been trained to classify recovered (normal) and abnormal voices in acoustic data, and produced much more accura...

Automatic monitoring, data collection, analysis and prediction of environmental changes is essential for all living things. Understanding future climate changes does not only helps in measuring the influence on people life, habits, agricultural and health but also helps in avoiding disasters. Giving the high emission of chemicals on air, scientist discovered the growing depletion in ozone layer...

Apparent shear stress acting on a vertical interface between the main channel and floodplain in a compound channel serves to quantify the momentum transfer between sub sections of this cross section. In this study, three soft computing methods are used to simulate apparent shear stress in prismatic compound channels. The Genetic Algorithm Artificial neural network (GAA), Genetic Programming (GP...