We evaluated 1) the performance of an artificial neural network (ANN)-based technology in assessing the respiratory system resistance (Rrs) and compliance (Crs) in a porcine model of acute lung injury and 2) the possibility of using, for ANN training, signals coming from an electrical analog (EA) of the lung. Two differently experienced ANNs were compared. One ANN (ANN(BIO)) was trained on trac...

In this study, net surface radiation (Rn) was estimated using artificial neural network (ANN) and Linear Model (LM). Then, estimated Rn with both the models (ANN and LM) were compared with measured Rn from eddy covariance (EC) flux tower. The routinely measured meteorological variables namely air temperature, relative humidity and wind velocity were used as input to the ANN and global solar rad...

This paper deals with modeling of plasma cutting process using artificial neural network (ANN). The objective was to develop an ANN model in order to predict the ten-point height of irregularities (Rz) in terms of three input parameters, cutting current, plate thickness and cutting speed. To this aim, a feed-forward backpropagation ANN was developed and trained using the experimental data. Afte...

In this project we present a heuristic learning process by training ANN (artificial neural network) and KNN (k-nearest neighbor) using the best number of steps, gained from A*, from randomly generated states to the goal. After training ANN and KNN, the mixture of Experts is discussed and the empirical data are collected to demonstrate the feasibility and accuracy of combination of ANN and KNN i...

3 If the same standard state is assumed for the solution in both phases S would be unity, but it is more convenient to assume different standard states so that as long as the properties of the two phases remain constant S will be constant but as a rule its value will not be unity. 4 The partition coefficient includes only the ions K+ and Cl-. It would not be constant if it included the undissoc...

The SWarm Intelligence-based Reinforcement Learning (SWIRL) method is proposed in this paper to efficiently generate Artificial Neural Network (ANN) based solutions to various problems. Basically, two swarm intelligence based algorithms are combined together in SWIRL to train the ANN models. Ant Colony Optimization (ACO) is applied to optimize ANN topology, while Particle Swarm Optimization (PS...

Artificial Neural Networks (ANN) are computer programs that emulate the operation of a large number of processing units that mimic the fundamental mechanisms of the biological activity of nervous cells as well as their connections and interactions. As a human brain, an ANN has the ability to learn from the experience of general relations between variables and thus ANN are particularly suita...

This paper developed an artificial neural network (ANN) model with hybrid Taguchi-genetic algorithm (HTGA) for the nonlinear multiple-input multiple-output (MIMO) model of machining processes. The HTGA in the MIMO ANN model finds the optimal parameters (i.e., weights of links and biases govern the input-output relationship of an ANN) by directly minimizing root-mean-squared error (RMSE), which ...

The main objective of this research is to forecast the daily direction of Standard & Poor's 500 (S&P 500) index using an artificial neural network (ANN). In order to select the most influential features (factors) of the proposed ANN that affect the daily direction of S&P 500 (the response), design of experiments are conducted to determine the statistically significant factors among 27 potential...