radial basis function neural networks training
Comparison of multilayer and radial basis function neural networks for text dependent speaker recognition
... artificial neural networks to both text-independent and text-dependent speaker recognition ...of radial basis function (RBF) networks ...MLP networks [14, 5, ...RBF ...
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Face Recognition using Rectangular Feature
... A radial basis function network is an artificial neural network that uses radial basis functions as activation ...of radial basis functions. They are used in ...
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Long-Term Peak Demand Forecasting by Using Radial Basis Function Neural Networks
... Radial basis function network (RBFN) can be used successfully to forecast distribution load ...The training phase constitutes the optimization of a fitting procedure based on the known data ...
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Comparison between Multi Layer Perceptron and Radial Basis Function Networks for Sediment Load Estimation in a Tropical Watershed
... The MLP network is comparatively more capable of tracing fluctuations in daily sediment load than the RBF network (Figures 4 and 5). As highlighted in Figures 4 and 5, the points corresponding to sediment load with an ...
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Function Approximation Using Robust Radial Basis Function Networks
... stochastic training methods that are based on minimization of some loss ...loss function is the quadratic function corresponding to a Gaussian noise model with zero mean, and a standard deviation ...
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Learning enhancement of radial basis function neural network with harmony search algorithm
... These meta-heuristic SGO techniques are inspired by biological processes which has the characteristics of training algorithms that overcome the aforementioned inefficiencies. Besides that, Harmony Search (HS) ...
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Comparative Application of Radial Basis Function and Multilayer Perceptron Neural Networks to Predict Traffic Noise Pollution in Tehran Roads
... Network training and testing were performed using the same datasets applied to the MLP net- ...for training RBF network with appropriate generalization ability, the target error was first considered zero, ...
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Radial Basis Function Networks for Conversion of Sound Spectra
... of Radial Basis Function Network, RBFN, and is a special case of feedforward neural network which ex- hibit high performances in nonlinear curve-fitting (approxi- mation) problems ...the ...
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A Comparative Study of Nonlinear Time Varying Process Modeling Techniques: Application to Chemical Reactor
... a Radial Basis Function (RBF) neural networks and the second is a Multi Layer Perceptron ...However, training MLP network based on back propagation learning is computationally ...
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Numerical Solution of Sixth Order Differential Equations Arising in Astrophysics by Neural Network
... Hopfield neural network ...forward neural network. Artificial neural networks based on Broyden-Fletcher-Goldfarb-Shanno (BF GS) optimization technique for solving ordinary and partial ...
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A Computerized Neural Network System to Filter Unwanted Messages from OSN User Walls
... of neural learning which is today recognized as one of the most efficient solutions in text ...on Radial Basis Function Networks (RBFN) with Self Organizing Neural Network ...
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Learning Methods of Radial Basis Function Neural Network
... Radial Basis Function Neural Network （ RBF ） is widely used now; it can solve the following problems: the small sample, nonlinear, dimension and local ...
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Performance Evaluation Analysis of MLP & DG-RBF Feed Forward Neural Networks for Pattern Classification of Handwritten English Curve Scripts Naveen Kumar Sharma 1, S R Pande2 and Manu Pratap Singh 3*
... forward neural network trained with back propagation algorithm does not perform better in comparison to feed forward neural network trained with decent gradient with ...the basis function ...
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Combining gene expression programming and genetic algorithm as a powerful hybrid modeling approach for pear rootstocks tissue culture media formulation
... The mathematical operators and functions chosen in the present study are illustrative and not conclusive as the plant modeling designer as the freedom to choose such functions so as suit the anatomy of the problem under ...
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Prediction of the pharmaceutical solubility in water and organic solvents via different soft computing models
... SVR neural networks have been used in recent years for modeling several systems [34, ...the function approximation in the regression problem. Vapnik’s loss function is used for the application ...
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Image Based Face Detection Using Probabilistic Neural Network
... Face can be defined as the front part of head from the forehead to the chin [1]. Biometrics deals with the identification of individuals based on their biological or behavioral characteristics [2]. A number of biometrics ...
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An Approach of Artificial Neural Networks Modeling Based on Fuzzy Regression for Forecasting Purposes
... identified as black box techniques. Uncertain conditions often are available through rapid development of new technologies, imprecise and inadequate data, and lack of ensuring in adequacy of defined independent ...
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An Evolutionary Approach: Analysis of Artificial Neural Networks
... to networks known as self organizing maps. These kinds of networks are not in widespread ...These networks use no external influences to adjust their weights (strength of ...These networks ...
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Intrusion Detection System Using Hybrid Approach by MLP and K-Means Clustering
... introduce Neural Network Committee Machine (NNCM), it consist Input Reduction System which is based on Intrusion Detection System and Principal Component Analysis (PCA) and these are represented by three level ...
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Detecting Network Intrusion Using BPA & RBF Neural Network Algorithms
... The BPNN [Reyadh Shaker Naoum, et al, 2012, Meera Gandhi, et al, 2008] searches for weight values that minimize the total error of the network over a set of training examples. It consists of the repeated ...
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