Multilayer perceptron architecture optimization using parallel computing techniques

Multilayer perceptron architecture optimization using parallel computing techniques

The objective of this research was to develop a methodology for optimizing multilayer-perceptron-type neural networks by evaluating the effects of three neural architecture parameters, namely, number of hidden layers (HL), neurons per hidden layer (NHL), and activation function type (AF), on the sum of squares error (SSE). The data for the study were obtained from quality parameters (physicoche...

Multilayer Perceptron: Architecture Optimization and Training

Architecture Optimization Model for the Multilayer Perceptron and Clustering

This paper presents an approach called Architecture Optimization Model for the multilayer Perceptron. This approach permits to optimize the architectures for the multilayer Perceptron. The results obtained by the neural networks are dependent on their parameters. The architecture has a great impact on the convergence of the neural networks. More precisely, the choice of neurons in each hidden l...

Prediction of daily evaporation using hybrid support vector regression-firefly optimization algorithm and multilayer perceptron

Prediction of daily evaporation is a valuable and determinant tool in sustainable agriculture and hydrological issues, especially in the design and management of water resources systems. Therefore, in this study, the ability of artificial intelligence models of multi-layer perceptron (MLP), support vector regression (SVR), and the hybrid model of support vector regression-firefly optimization a...

Auto-kernel using multilayer perceptron

This work presents a constructive method to train the multilayer perceptron layer after layer successively and to accomplish the kernel used in the support vector machine. Data in different classes will be trained to map to distant points in each layer. This will ease the mapping of the next layer. A perfect mapping kernel can be accomplished successively. Those distant mapped points can be dis...