Depending upon the nonlinear feature between neural units in artificial neural networks (ANN), artificial neural network was used to develop a model for greenhouse inside air temperature management. Data was collected and processed for training and simulation of temperature model. The design of the network structure and transfer function was also discussed. Because heuristic logic of greenhouse...

The paper presents a method for classifying coffees according to their scents using artificial neural network (ANN). The proposed method of uses genetic algorithm (GA) to determine the optimal parameters and topology of ANN. It uses adaptive back-propagation to accelerate the training process so that the entire optimization process can be achieved in an accelerated time. The optimized ANN has s...

Artificial neural network (ANN) has application in communication engineering in diverse areas such as channel equalization, channel modeling, error control code because of its capability of nonlinear processing, adaptability, and parallel processing. On the other hand, wavelet transform (WT) with both the time and the frequency resolution provides the exact representation of signal in both doma...

one of the main aims of water resource planners and managers is to estimate and predict the parameters of groundwater quality so that they can make managerial decisions. in this regard, there have many models developed, proposing better management in order to maintain water quality. most of these models require input parameters that are either hardly available or time-consuming and expensive to...

In this study, artificial neural network was used to predict the microhardness of Al2024-multiwall carbon nanotube(MWCNT) composite prepared by mechanical alloying. Accordingly, the operational condition, i.e., the amount of reinforcement, ball to powder weight ratio, compaction pressure, milling time, time and temperature of sintering as well as vial speed were selected as independent input an...

in this study artificial neural network (ann) was used for modeling of wastewater treatment plants with using. for this purpose, the base of the quality parameters measured at the plant input, plant output value was predicted. neural network input data, including temperature (t), biochemical oxygen demand (bod), chemical oxygen demand (cod), total suspended solids (tss), total solids (ts) and p...

Water resource problems currently are much more important in proper planning especially for arid regions, such as Gansu in China. For agricultural and industrial activities, prediction of groundwater status is critical. As a main branch of neural network, shallow artificial neural network models have been deployed in prediction areas such as groundwater and rainfall since late 1980s. In this pa...

This research intends to develop a method based on the Artificial Neural Network (ANN) to predict permanent earthquake-induced deformation of the earth dams and embankments. For this purpose, data sets of observations from 152 published case histories on the performance of the earth dams and embankments, during the past earthquakes, was used. In order to predict earthquake-induced deformation o...

three artificial neural networks (ann) models; general regression neural network (grnn), redial basis function (rbf) and three layer multiple perceptron network were carried out to evaluate the prediction of the apparent metabolizable energy (ame) of wheat and corn from its chemical composition in broiler. input variables included: gross energy (ge), crude protein (cp), crude fiber (cf), ether ...