Performance Of Radial Basis Function Research Articles

Performance of Radial Basis Function and Group Method of Data Handling-type Neural-Networks in Flammability Characteristics Prediction of PMMA

Performance of Radial Basis Function and Group Method of Data Handling-type Neural-Networks in Flammability Characteristics Prediction of PMMA

Integrated neural network model with pre-RBF kernels.

To improve the network performance of radial basis function (RBF) and back-propagation (BP) networks on complex nonlinear problems, an integrated neural network model with pre-RBF kernels is proposed. The proposed method is based on the framework of a single optimized BP network and an RBF network. By integrating and connecting the RBF kernel mapping layer and BP neural network, the local features of a sample set can be effectively extracted to improve separability; subsequently, the connected BP network can be used to perform learning and classification in the kernel space. Experiments on an artificial dataset and three benchmark datasets show that the proposed model combines the advantages of RBF and BP networks, as well as improves the performances of the two networks. Finally, the effectiveness of the proposed method is verified.

Analysis of enhanced complex SVR interpolation and SCG-based neural networks for LTE downlink system

In this article, we operate and evaluate the performance of radial basis function (RBF)-based support vector machine regression (SVR) and scaled conjugate gradient back propagation (SCG)-based artificial neural network (ANN), to estimate the channel deviations in frequency domain using the standardised pilot symbols structure for LTE downlink system. We apply complex SVR and ANN to estimate the real vehicular a channel environment well-defined by the International Telecommunications Union (ITU). The suggested procedures use data obtained from the received pilot symbols to estimate the overall frequency response of the frequency selective multipath fading channel in two stages. In the first stage, each technique learns to adjust to the channel fluctuations, then, in the second stage, it predicts all the channel frequency responses. Lastly, in order to assess the abilities of the considered channel estimators, we deliver performance of complex SVR and ANN, which are compared to traditional least squares (LS) and decision feedback (DF) methods. Computer simulation results demonstrate that the complex RBF-based SVR approach has a better precision than other estimation methods.

Analysis of enhanced complex SVR interpolation and SCG-based neural networks for LTE downlink system

In this article, we operate and evaluate the performance of radial basis function (RBF)-based support vector machine regression (SVR) and scaled conjugate gradient back propagation (SCG)-based artificial neural network (ANN), to estimate the channel deviations in frequency domain using the standardised pilot symbols structure for LTE downlink system. We apply complex SVR and ANN to estimate the real vehicular a channel environment well-defined by the International Telecommunications Union (ITU). The suggested procedures use data obtained from the received pilot symbols to estimate the overall frequency response of the frequency selective multipath fading channel in two stages. In the first stage, each technique learns to adjust to the channel fluctuations, then, in the second stage, it predicts all the channel frequency responses. Lastly, in order to assess the abilities of the considered channel estimators, we deliver performance of complex SVR and ANN, which are compared to traditional least squares (LS) and decision feedback (DF) methods. Computer simulation results demonstrate that the complex RBF-based SVR approach has a better precision than other estimation methods.

A Regularizer Approach for RBF Networks Under the Concurrent Weight Failure Situation.

Many existing results on fault-tolerant algorithms focus on the single fault source situation, where a trained network is affected by one kind of weight failure. In fact, a trained network may be affected by multiple kinds of weight failure. This paper first studies how the open weight fault and the multiplicative weight noise degrade the performance of radial basis function (RBF) networks. Afterward, we define the objective function for training fault-tolerant RBF networks. Based on the objective function, we then develop two learning algorithms, one batch mode and one online mode. Besides, the convergent conditions of our online algorithm are investigated. Finally, we develop a formula to estimate the test set error of faulty networks trained from our approach. This formula helps us to optimize some tuning parameters, such as RBF width.

Prediction Modeling of PM-10 in Chiangmai City Moat by Using Artificial Neural Networks

A mathematical prediction model has been developed in order to detect particles with a diameter of 10 micrometers or less (PM-10) that are responsible for adverse health effects because of their ability to cause serious respiratory conditions in areas of high pollution such as Chiang Mai City moat area. The prediction model is based on 3 types of Artificial Neural Networks (ANNs), including Multi-layer perceptron (MLP-NN), Radial basis function (RBF-NN), and hybrid of RBF and Genetic algorithm (RBF-NN-GA). The model uses 8 input variables to predict PM-10, consisting of 4 air pollution substances ( CO, O3, NO2 and SO2) and 4 meteorological variables related PM-10 (wind speed, temperature, atmospheric pressure and relative humidity). These 3 types of ANN have proved efficient instrument in predicting the PM-10. However, the performance of RBF-NN was superior in comparison with MLP-NN and RBF-NN-GA respectively.

Generalization performance of radial basis function networks.

This paper studies the generalization performance of radial basis function (RBF) networks using local Rademacher complexities. We propose a general result on controlling local Rademacher complexities with the L1 -metric capacity. We then apply this result to estimate the RBF networks' complexities, based on which a novel estimation error bound is obtained. An effective approximation error bound is also derived by carefully investigating the Hölder continuity of the lp loss function's derivative. Furthermore, it is demonstrated that the RBF network minimizing an appropriately constructed structural risk admits a significantly better learning rate when compared with the existing results. An empirical study is also performed to justify the application of our structural risk in model selection.

RBF neural network based on q-Gaussian function in function approximation

To enhance the generalization performance of radial basis function (RBF) neural networks, an RBF neural network based on a q-Gaussian function is proposed. A q-Gaussian function is chosen as the radial basis function of the RBF neural network, and a particle swarm optimization algorithm is employed to select the parameters of the network. The non-extensive entropic index q is encoded in the particle and adjusted adaptively in the evolutionary process of population. Simulation results of the function approximation indicate that an RBF neural network based on q-Gaussian function achieves the best generalization performance.

An Efficient Weather Forecasting System using Radial Basis Function Neural Network

Problem statement: Accurate weather forecasting plays a vital role fo r planning day to day activities. Neural network has been use in numerous meteorological applications including weather forecasting. Approach: A neural network model has been developed for weather forecasting, based on various factors obtained from meteorological expert s. This study evaluates the performance of Radial Basis Function (RBF) with Back Propagation (BPN) neural network. The back propagation neural network and radial basis function neural network we re used to test the performance in order to investigate effective forecasting technique. Results: The prediction accuracy of RBF was 88.49%. Conclusion: The results indicate that proposed radial basis fu nction neural network is better than back propagation neural network.

Performance of auto-configuring RBF networks trained with significant patterns

This paper presents two new ideas to improve the performance of Radial Basis Function (RBF) networks. In the first instance, we choose a set of patterns for training, which are closer to decision boundaries, from different classes of training samples that constitute the input space. We call those set of patterns significant patterns and discuss their selection process from the given data set. Secondly, we use these significant patterns to train Adaptive incremental learning RBF network and Resource Allocating Network (RAN). The learning curves and generalisation characteristics of the generated RBF networks are presented. The performance results are discussed.

Transport schemes on a sphere using radial basis functions

The aim of this work is to introduce the physics community to the high performance of radial basis functions (RBFs) compared to other spectral methods for modeling transport (pure advection) and to provide the first known application of the RBF methodology to hyperbolic partial differential equations on a sphere. First, it is shown that even when the advective operator is posed in spherical coordinates (thus having singularities at the poles), the RBF formulation of it is completely singularity free. Then, two classical test cases are conducted: (1) linear advection, where the initial condition is simply transported around the sphere and (2) deformational flow (idealized cyclogenesis), where an angular velocity is applied to the initial condition, spinning it up around an axis of rotation. The results show that RBFs allow for a much lower spatial resolution (i.e. lower number of nodes) while being able to take unusually large time-steps to achieve the same accuracy as compared to other commonly used spectral methods on a sphere such as spherical harmonics, double Fourier series, and spectral element methods. Furthermore, RBFs are algorithmically much simpler to program.

An efficient learning algorithm for improving generalization performance of radial basis function neural networks

This paper presents an efficient recursive learning algorithm for improving generalization performance of radial basis function (RBF) neural networks. The approach combines the rival penalized competitive learning (PRCL) [Xu, L., Kizyzak, A. & Oja, E. (1993). Rival penalized competitive learning for clustering analysis, RBF net and curve detection, IEEE Transactions on Neural Networks, 4, 636–649] and the regularized least squares (RLS) to provide an efficient and powerful procedure for constructing a minimal RBF network that generalizes very well. The RPCL selects the number of hidden units of network and adjusts centers, while the RLS constructs the parsimonious network and estimates the connection weights. In the RLS we derived a simple recursive algorithm, which needs no matrix calculation, and so largely reduces the computational cost. This combined algorithm significantly enhances the generalization performance and the real-time capability of the RBF networks. Simulation results of three different problems demonstrate much better generalization performance of the present algorithm over other existing similar algorithms.

Mean-tracking clustering algorithm for radial basis function centre selection

Radial basis functions can be combined into a network structure that has several advantages over conventional neural network solutions. However, to operate effectively the number and positions of the basis function centres must be carefully selected. Although no rigorous algorithm exists for this purpose, several heuristic methods have been suggested. In this paper a new method is proposed in which radial basis function centres are selected by the mean-tracking clustering algorithm. The mean-tracking algorithm is compared with k means clustering and it is shown that it achieves significantly better results in terms of radial basis function performance. As well as being computationally simpler, the mean-tracking algorithm in general selects better centre positions, thus providing the radial basis functions with better modelling accuracy.

Performance of radial basis functions in decodingarecently proposed chaotic modulation strategy

The use of radial basis functions for decoding a recently proposed modulation strategy is investigated. A range of data signals are considered and practical aspects are discussed when predicting, what in practice would be, a multitude of mapping and decoding parameters. The authors show that radial basis functions do not predict the next unmodulated chaotic sample with the degree of accuracy required to accurately decode a modulating signal. The authors therefore conclude that the proposed modulation technique, together with many other desirable features, results in secure transmission.