RBF networks
Numerical Analysis near Singularities in RBF Networks
... neural networks are still unknown and, therefore, it is important to examine the learning dynamics near ...of RBF networks and Wei and Amari (2008) has obtained the analytical form of averaged ...
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Binary Classification for Hydraulic Fracturing Operations in Oil & GasWells via Tree Based Logistic RBF Networks
... neural networks (NNs) [5] are one of the techniques to handle binary classification ...that RBF-NNs has become very popular for applications for real life data ...accompanying RBF networks ...
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FACIAL EMOTION RECOGNITION THROUGH GABOR FILTER AND RBF NETWORKS
... Human Emotion Recognition plays an vital role in the human-computer interaction (HCI). In this paper, the emotions are detected on segmented image by means of low dimension weighted Gabor filter bank. The segmentation ...
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NEUROSVM: An Architecture to Reduce the Effect of the Choice of Kernel on the Performance of SVM
... neural networks, although other tools such as radial basis function (RBF) networks can be ...or RBF can be ...and RBF in the classification module of the proposed ...
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Adaptive Control OF Nonlinear Multivariable Dynamical Systems Using MRAN-RBF Neural Networks
... Several learning algorithms, such as resource allocation networks (RAN), RAN-EKF, and MRAN, have been proposed in the literature for training RBF networks. In practical on-line applications, ...
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A simple and effective preconditioner for integrated-RBF-based Cartesian-grid schemes
... Integrated RBF networks (IRBFNs), which have the ability to avoid the reduction in convergence rate caused by differentiation, were successfully developed for the solution of partial differential equations ...
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Date Fruits Classification using MLP and RBF Neural Networks
... neural networks have been applied as classifiers: multi-layer perceptron (MLP) with backpropagation and radial basis function RBF ...neural networks achieved a recognition rate equal to ...and ...
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Finite Mixture Estimation Algorithm for Arbitrary Function Approximation
... neural networks research [1], the field of neural networks has been established as an interdisciplinary subject with deep roots in neurosciences, psychology, mathematics, the physical sciences and ...
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The Automatic Model Selection and Variable Width RBF Neural Networks for Chaotic Time Series Prediction
... appropriate RBF network structure is a key issue because it governs the prediction effect of chaotic time ...developed RBF algorithms, such as k-means, RAN, RPCL, GAP and its variants, intensively depend on ...
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Burst by Burst Adaptive Turbo Coded Radial Basis Function Assisted Decision Feedback Equalization
... and RBF networks ...of RBF based equalizers, which exhibit a structure identical to that of the optimal Bayesian symbol-decision based ...Therefore, RBF equalizers can rely on optimal ...
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Survey on Effective Kidney Stone Disease Diagnosis Using ANN
... neural networks on the basis of its accuracy, time taken to build model, and training data set ...(RBF) networks for diagnosis of kidney stone ...
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Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks
... the RBF network to achieve universal ap- proximation ...standard RBF network by the Mahalanobis distance gives rise to a more general neural-network model [16], ...Gaussian RBF network and can ...
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EMBEDDING RАPIDMINER MODELS IN JAVA CODE
... & RBF Networks) had been done to predict the onset of thyroid ...the networks accurately whereas which is not the same for Hypothyroidism and Hyperthyroidism ...the networks have not been ...
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A Survey on Data Classification using Machine Learning Techniques
... function networks based on a novel kernel density estimation ...(RBF) networks that can provide the same level of accurateness as the support vector machines (SVMs) in data classification ...one ...
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Image Segmentation and Edge Detection Using a Neural Networks RBF Approach
... neuron. RBF networks are similar to K-Means clustering and PNN/GRNN ...PNN/GRNN networks have one neuron for each point in the training file, whereas RBF networks have a variable number ...
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Learning enhancement of radial basis function network with particle swarm optimization
... (RBF) Networks form a class of Artificial Neural Networks (ANNs), which has certain advantages over other types of ANNs, such as better approximation capabilities, simpler network structures and ...
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Prediction of Rainfall Using MLP and RBF Networks
... From Tables 1 and 2, it may be noted that: (i) The RMSEs on the predicted rainfall using MLPN are lesser than the corresponding values of RBFN during testing and therefore the architecture of MLPN is better suited ...
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Inter System EMC Prediction with CG GRBF Networks
... Generalized RBF Networks (Radial Basis Function Networks) and the Conjugate Gradient Method (CG), which optimizes a selection of the standard deviation of the radial basis ...GRBF networks is ...
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Predictive Subset Selection using Regression Trees and RBF Neural Networks Hybridized with the Genetic Algorithm
... In this section, we report our computational results on a simulated data set using hybrid RBF-NN approach between the regression trees RBF networks with regularization, the GA and I COM P(I F I M ) M ...
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Application of Artificial Neural Network and Adaptive Neural based Fuzzy Inference System Techniques in Estimating of Virtual Water
... (MLP, RBF, and GRNN) and ANFIS were applied in this ...(MLP, RBF, and GRNN) structures, RBF with the least amount of RMSE equal to ...the RBF and ANFIS showed that ANFIS with lower RMSE and ...
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