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Artificial Neural networks are utilized to predict flow properties of a confined, isothermal, and swirling flowfield in an axisymmetric sudden expansion combustor using a two-component laser Doppler velocimetry capable of measuring the mean velocity components and their statistics. Generalized feedforward, radial basis function, and coactive neuro-fuzzy inference system neural networks are tested...
A new measure for objective speech quality evaluation based on the improved generalized congruence neural network (GCNN/OSQE) is proposed, which needs less training time and has better performance. Compared with radial basis function neural network for objective speech quality evaluation measure (RBFNN/OSQE), besides owning all the merits of RBFNN/OSQE, GCNN/OSQE has many more merits: higher correlation,...
In this paper, we propose an artificial neural network approach to determine the quantitative structure-activity relationship (QSAR) among known aldose reductase inhibitors (ARI). In order to accurately describe the structural properties of ARIs, besides the popularly used 2-dimensional (2D) descriptors, we have used 3-dimensional (3D) molecular descriptors which are obtained through the DRAGON software...
This paper presents the evolutionary neural network (ENN) model for the prediction of output from a grid-connected photovoltaic system installed at Malaysian Energy Centre (PTM), Bangi, Malaysia. The ENN model had been developed using evolutionary programming (EP) through the optimization of the number of nodes in the hidden layer, the learning rate and the momentum rate. The ENN model employs solar...
This paper evaluates the neural network autoregressive with exogenous (NNARX) structure in modeling the steam distillation essential oil extraction. The model order will be selected based on Rissanenpsilas minimum description length (MDL) information criterion. In the training of NNARX model, both unregularized and regularized models will be assessed. There are three regularization levels of the weight...
This paper presents the optimization of one-hidden layer artificial neural network (ANN) design using evolutionary programming (EP) for predicting the energy output of a grid-connected photovoltaic system installed at Malaysian Energy Centre (PTM), Bangi, Malaysia. In this study, the architecture and training parameters of the multi-layer feedforward back-propagation ANN model had been optimized while...
In this paper, an improved grey self-organizing map (GSOM) model is proposed and applied in the detection of deny of service (DOS) attack. For detection of the DOS attacks, this improved model can consider the relativity of the data set of DOS attacks. Finally, the experiments on the DOS data set confirm their validities and feasibilities over this improved GSOM model.
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