Multilayer Perceptron Network (MLP) has a better prediction Multilayer Perceptron Network (MLP) has a better prediction performance compared to other networks since the structure of the MLP is suitable for training processes in solving prediction problems. However, to the best of our knowledge, there is no rule of thumb in determining the number of hidden nodes within the MLP structure. Researchers normally test with various numbers of hidden nodes to obtain the lowest square error value for optimal prediction results since none of the approaches has yet to be claimed as the best practice. Thus, the aim of this study is to determine the best MLP network by varying the number of hidden nodes of developed networks to predict cycle time for pr...
The main advantage of detecting chaos is that the time series is short term predictable. The predict...
A critical question in the neural network research today concerns how many hidden neurons to use. Th...
Abstract- A rich literature discussing techniques for adopting neural networks for metamodelling of ...
Multilayer Perceptron Network (MLP) has a better prediction Multilayer Perceptron Network (MLP) has ...
Multilayer Perceptron Network (MLP) has a better prediction performance compared to other networks s...
Multilayer Perceptron Network (MLP) has a better prediction performance compared to other networks s...
Most application work within neural computing continues to employ multi-layer perceptrons (MLP). Tho...
Forecasting, classification, and data analysis may all gain from improved pattern recognition result...
The duration of software development projects has become a competitive issue: only 39% of them are f...
Several neural network architectures have been developed over the past several years. One of the mos...
Abstract- Determining the optimal number of hidden nodes isthe most challenging aspect of Artificial...
Performance metrics are a driving force in many fields of work today. The field of constructive neur...
The architectures of Artificial Neural Networks (ANN) are based on the problem domain and it is appl...
Multilayer perceptrons (MLPs) (1) are the most common artificial neural networks employed in a large...
A multilayer perceptron is a feed forward artificial neural network model that maps sets of input da...
The main advantage of detecting chaos is that the time series is short term predictable. The predict...
A critical question in the neural network research today concerns how many hidden neurons to use. Th...
Abstract- A rich literature discussing techniques for adopting neural networks for metamodelling of ...
Multilayer Perceptron Network (MLP) has a better prediction Multilayer Perceptron Network (MLP) has ...
Multilayer Perceptron Network (MLP) has a better prediction performance compared to other networks s...
Multilayer Perceptron Network (MLP) has a better prediction performance compared to other networks s...
Most application work within neural computing continues to employ multi-layer perceptrons (MLP). Tho...
Forecasting, classification, and data analysis may all gain from improved pattern recognition result...
The duration of software development projects has become a competitive issue: only 39% of them are f...
Several neural network architectures have been developed over the past several years. One of the mos...
Abstract- Determining the optimal number of hidden nodes isthe most challenging aspect of Artificial...
Performance metrics are a driving force in many fields of work today. The field of constructive neur...
The architectures of Artificial Neural Networks (ANN) are based on the problem domain and it is appl...
Multilayer perceptrons (MLPs) (1) are the most common artificial neural networks employed in a large...
A multilayer perceptron is a feed forward artificial neural network model that maps sets of input da...
The main advantage of detecting chaos is that the time series is short term predictable. The predict...
A critical question in the neural network research today concerns how many hidden neurons to use. Th...
Abstract- A rich literature discussing techniques for adopting neural networks for metamodelling of ...