Neurons In First Hidden Layer Research Articles

Optimization of Artificial Neural Network using Evolutionary Programming for Prediction of Cascading Collapse Occurrence due to the Hidden Failure Effect

This paper presents the Evolutionary Programming (EP) which proposed to optimize the training parameters for Artificial Neural Network (ANN) in predicting cascading collapse occurrence due to the effect of protection system hidden failure. The data has been collected from the probability of hidden failure model simulation from the historical data. The training parameters of multilayer-feedforward with backpropagation has been optimized with objective function to minimize the Mean Square Error (MSE). The optimal training parameters consists of the momentum rate, learning rate and number of neurons in first hidden layer and second hidden layer is selected in EP-ANN. The IEEE 14 bus system has been tested as a case study to validate the propose technique. The results show the reliable prediction of performance validated through MSE and Correlation Coefficient (R).

Using artificial neural networks to predict thermal conductivity of pear juice

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 optimal ANN model consisted 2 hidden layers with 5 neurons in first hidden layer and the second one has only one neuron. The ANN model was able to predict thermal conductivity values which closely matched the experimental values by providing lowest mean square error (R2=0.999) compared to conventional and multivariable regression models. However this method also improves the problem of determining the hidden structure of the neural network layer by trial and error. It can be incorporated in heat transfer calculations during juices processing where temperature and concentration dependent thermal conductivity values are required.

Feasibility investigation of using artificial neural network in process monitoring of pumpkin air drying

In this paper, pumpkin cubes were dried by a laboratory scale convective hot air dryer. The drying process was carried out at four different temperatures (65, 75, 85 and 95 °C). After the end of drying process, initially, the experimental drying data were fitted with three well-known drying models. The results indicated that Newton model gave better results compared with other models to monitor the moisture ratio (MR) (with average correlation coefficient of determination, R2=0.993). Also, this study used artificial neural network analysis (ANN) in order to feasibly predict dried pumpkins MR based on the time and temperature drying inputs. In order to do this project, two main activation functions including logsig and tanh that widely used in engineering calculations, were applied. Results showed that logsig activation function with 18 neurons in first hidden layer was selected as the best topology to predict MR. Comparison of the results obtained by ANN and classical modelling showed that artificial neur...