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Artifical Neural Network Models For The Analysis Of Permeable Pavement Performance., Ata Radfar
Artifical Neural Network Models For The Analysis Of Permeable Pavement Performance., Ata Radfar
Electronic Theses and Dissertations
This dissertation is a numerical modeling study based on the findings of the two installed Permeable Interlocking Concrete Pavements (PICPs) in Louisville, KY and twenty one laboratory models. A new model derived to more accurately predict the captured surface runoff volume by the PICPs using Artificial Neural Networks (ANNs). The proposed model relates rainfall parameters and site characteristics to the runoff volume captured by the permeable pavements. The database used for developing the prediction models is obtained from the collected data of the monitored permeable pavements. The performance of the ANN-based models are analyzed and the results demonstrate that the …
Reduced Hyperbf Networks : Practical Optimization, Regularization, And Applications In Bioinformatics., Rami Nezar Mahdi 1982-
Reduced Hyperbf Networks : Practical Optimization, Regularization, And Applications In Bioinformatics., Rami Nezar Mahdi 1982-
Electronic Theses and Dissertations
A hyper basis function network (HyperBF) is a generalized radial basis function network (RBF) where the activation function is a radial function of a weighted distance. The local weighting of the distance accounts for the variation in local scaling and discriminative power along each feature. Such generalization makes HyperBF networks capable of interpolating decision functions with high accuracy. However, such complexity makes HyperBF networks susceptible to overfitting. Moreover, training a HyperBF network demands weights, centers and local scaling factors to be optimized simultaneously. In the case of a relatively large dataset with a large network structure, such optimization becomes computationally …