background: municipal solid waste (msw) is the natural result of human activities. msw generation modeling is of prime im­portance in designing and programming municipal solid waste management system. this study tests the short-term pre­diction of waste generation by artificial neural network (ann) and principal component-regression analysis. methods: two forecasting techniques are presented in...

The quantitative structure-retention relationship (QSRR) of nanoparticles in roadside atmosphere against the comprehensive two-dimensional gas chromatography which was coupled to high-resolution time-of-flight mass spectrometry was studied. The genetic algorithm (GA) was employed to select the variables that resulted in the best-fitted models. After the variables were selected, the linear multi...

artificial neural networks (ann) have shown to be a powerful tool for system modeling in a wide range of applications. the focus of this study is on neural network applications to data analysis in egg production. an ann model with two hidden layers, trained with a back propagation algorithm, successfully learned the relationship between the input (age of hen) and output (egg production) variabl...

the main focus in this study is on data pre-processing, reduction in number of inputs or input space size reduction the purpose of which is the justified generalization of data set in smaller dimensions without losing the most significant data. in case the input space is large, the most important input variables can be identified from which insignificant variables are eliminated, or a variable ...

The paper deals with training the neural models of microwave structures. The first, an artificial neural network (ANN) is trained with basic genetic algorithm (GA). Training abilities are discussed. Further, the modification of GA and an approach to learning artificial neural networks (ANN) with backpropagation is described. Neural networks are implemented in MATLAB. Results of training abiliti...

Genetic programming (GP) based data fusion and AdaBoost can both improve in vitro prediction of Cytochrome P450 activity by combining artificial neural networks (ANN). Pharmaceutical drug design data provided by high throughput screening (HTS) is used to train many base ANN classifiers. In data mining (KDD) we must avoid over fitting. The ensembles do extrapolate from the training data to other...

this study shows the usefulness of artificial neural network (ann) in maintenance planning and man-agement. an ann model based on the multi-layer perceptron having three hidden layers and four processing elements per layer was built to predict the expected downtime resulting from a breakdown or a maintenance activity. the model achieved an accuracy of over 70% in predicting the expected downtime.

thermal conductivity is an important property of juices in the prediction of heat- and mass-transfer coefficients and in the design of heat- and mass-transfer equipment for the fruit juice industry. an artificial neural network (ann) was developed to predict thermal conductivity of pear juice. temperature and concentration were input variables. thermal conductivity of juices was outputs. the op...

genetic algorithm and partial least square (ga-pls), the kernel pls (kpls) and levenberg-marquardt artificial neural network (l-m ann) techniques were used to investigate the correlationbetween retention time (rt) and descriptors for 15 nanoparticle compounds which obtained by thecomprehensive two dimensional gas chromatography system (gc x gc). application of thedodecanethiol monolayer-protect...

injection molding is one of the most important and common plastic formation methods. combination of modeling tools and optimization algorithms can be used in order to determine optimum process conditions for the injection molding of a special part. because of the complication of the injection molding process and multiplicity of parameters and their interactive effects on one another, analytical...