Reviewing and designing pre-processing units for RBF networks: initial structure identification and coarse-tuning of free parameters

Prediction of Secondary Metabolites Content of Laurel (Laurus nobilis L.) with Artificial Neural Networks Based on Different Temperatures and Storage times

Bay laurel leaves, also known as bay leaves, are an important herb in many cuisines around the world. In addition to their use in cooking, bay leaves have also been used for their medicinal properties and are thought to have anti-inflammatory and antimicrobial effects. Gas chromatography/mass spectrometry (GC-MS) device was used to determine the secondary metabolites in the essential oil of bay laurel leaves samples kept at different temperatures (−22, −20, −18, 2, 4, 6, and 22°C) and storage times (1, 2, and 3 months). In this research, temperature (°C) and storage time (month) were used as input parameters in the neural network. On the other hand, alpha-pinene, beta-pinene, sabinene, 1.8-cineole, gamma-terpinene, cymenol, linalool, borneol, 4-terpineol, caryophyllene, sabinene, alpha-terpineol, germacrene-D, alpha-selinene, methyl eugenol, caryophyllene oxide, spathulenol, eugenol, and beta-selinenol were used as an output parameter. Considering the R2 values obtained from the artificial neural network analysis, R2 values of 0.97156 for the test, 0.98978 for the training, 0.98998 for the validation value, and 0.98831 for all values were obtained.

Solving Classification Problems Using Projection-Based Learning Algorithm with Fuzzy Radial Basis Function Neural Network

Radial basis function (RBF) is combined with fuzzy C-means algorithms and its learning process made by projection-based learning (PBL) has been proposed in this paper, which is pointed out as PBL-fuzzy radial basis function (PBL-FRBF). The proposed method PBL-FRBF is producing good performances by selecting appropriate center and its width in order to achieve it by unsupervised classification algorithms instead of random selection. The PBL decreases the learning time, finds optimum output weight by its energy function and prefers smallest amount of samples for testing. Performance analysis is evaluated by benchmark datasets for classification problem taken from the UCI machine learning repository. The performance of the proposed PBL-FRBF has produced superior results when compared with FRBF and RBF for classification problems.