Radial Basis Function Network Learning Research Articles

New Strategy Based on RBF Network to Develop a Collaborative Filtering Recommender System

Collaborative filtering is a popular recommendation algorithm. It predicts user's interests according to the ratings or behaviour of other users in the system. However, the collaborative filtering recommender system suffers from several major limitations including scalability, sparsity, and cold start. In this paper, a collaborative filtering recommendation approach using radial basis function (RBF) network and power method is proposed. The proposed system has offline and online phases. In the offline phase, the sparse user-item rating matrix is completed by using RBF network based on Cover's theorem on the separability of patterns. RBF network learning is done by unsupervised kernel-based fuzzy c-means clustering algorithm for selecting RBF centers, and supervised gradient descend method for selecting RBF weights. In the offline phase, we predict non-rated items of a user. Then the full rating matrix is used to rank all the users. The ranking is done by solving an eigenvalue problem. This paper overcomes the scalability problem by clustering the users, the sparsity problem by completing the sparse rating matrix, and the new user cold start problem by recommending the top rated items of the high-ranked user. The results of the experiments, on the benchmark data sets, show that the proposed system produces high quality recommendation, in terms of accuracy and quality.

Study of Fault Location Algorithm for Distribution Network with Distributed Generation based on IGA-RBF Neural Network

The access of the Distributed Generation (DG) has to make the fault location problem of distribution network extremely complex and affect the efficiency and accuracy of fault location. According to the fault information that the Supervisory Control And Data Acquisition (SCADA) of distribution network upload and considering the change of configuration and logic relation of distribution network protection after DGs access, this paper proposed a radial basis function (RBF) neural network based on Improved Genetic Algorithm (IGA), which used the real-coded genetic algorithm with adaptive crossover and mutation into the gradient-dropping algorithm as the RBF network learning algorithm, and constructed a new switch function and fitness function. And then the improved algorithm was applied to the fault location of distribution network containing distributed power supply. The simulation results show that the RBF neural network based on IGA not only has the advantages of simple structure and fast operation, but also has better generalization performance. The analysis and comparison results show that the optimized Improved algorithm can effectively improve the convergence speed and precision, and it has good fault-tolerance to the lack or distortion of the fault information.

Radial Basis Function Network Learning with Modified Backpropagation Algorithm

Radial Basis Function Network (RBFN) is a class of Artificial Neural Network (ANN) that was used in many classification problems in science and engineering. Backpropagation (BP) algorithm is a learning algorithm that was widely used in ANN. However, BP has major disadvantages of slow error rate convergence and always easily stuck at the local minima. Hence, Modified BP algorithm was proposed in this study to improve the learning speed of RBFN using discretized data. C programming language was used to develop the program for the proposed method. Performance measurement of the method was conducted and the experimental results indicate that our proposed method performs better in error rate convergence and correct classification compared to the result with continuous dataset. T-test statistical analysis was used to check the significance of the results and most were found to be satisfactorily significant. DOI: http://dx.doi.org/10.11591/telkomnika.v13i2.7032

Using Radial Basis Function Networks for Function Approximation and Classification

The radial basis function (RBF) network has its foundation in the conventional approximation theory. It has the capability of universal approximation. The RBF network is a popular alternative to the well-known multilayer perceptron (MLP), since it has a simpler structure and a much faster training process. In this paper, we give a comprehensive survey on the RBF network and its learning. Many aspects associated with the RBF network, such as network structure, universal approimation capability, radial basis functions, RBF network learning, structure optimization, normalized RBF networks, application to dynamic system modeling, and nonlinear complex-valued signal processing, are described. We also compare the features and capability of the two models.

Optimization and stabilization of sequential learning in RBF network for nonlinear function approximation

This paper proposes a solution for inconsistency pruning of neurons within a sequential learning Radial Basis Function (RBF) Network. This paper adopts the concept that a specific RBF neuron which continuously exhibits low output in a sequence of training patterns does not justify the proposition that the neuron is insignificant to the whole function to be approximated. We establish additional criterions to provide protection from error in pruning RBF neurons within the hidden layer, which we prove is able to improve consistency and stability of neuron evolution. With such stability within the sequential learning process, we also show how the convergence speed of the network can be improved by reducing the number of consecutive observations required to prune a neuron in the hidden layer.

Prediction of Pollutant Levels in Causeway Bay Area of Hong Kong Using an Improved Neural Network Model

The prediction of air quality parameters is of great interest in environmental studies today due to the health impact caused by airborne pollutants [e.g., sulfur dioxide (SO2); nitrogen oxide (NOx); nitric oxide (NO); nitrogen dioxide (NO2); carbon monoxide (CO); respirable suspended particulates (RSPs), etc.] in urban areas. Artificial neural networks are regarded as a reliable and cost-effective method for prediction tasks. The work reported here develops an improved neural network model which combines both the principal component analysis (PCA) technique and the radial basis function (RBF) network to analyze and predict the pollutant data recorded. In the study, PCA is used to reduce and orthogonalize the original variables. The variables treated are then used as input vectors in a RBF neural network model to forecast the pollutant levels, e.g., the RSP level in the downtown area of Hong Kong. This improved method is evaluated based on hourly time series RSP concentrations collected at the Causeway Bay roadside gaseous monitoring station in Hong Kong during 1999. The simulation results show the effectiveness of the model. For high-dimensional input vectors including simpler network architecture and faster learning speed without compromising the generalization capability of the network, the proposed algorithm has advantages over traditional RBF network learning.

Three learning phases for radial-basis-function networks

In this paper, learning algorithms for radial basis function (RBF) networks are discussed. Whereas multilayer perceptrons (MLP) are typically trained with backpropagation algorithms, starting the training procedure with a random initialization of the MLP's parameters, an RBF network may be trained in many different ways. We categorize these RBF training methods into one-, two-, and three-phase learning schemes. Two-phase RBF learning is a very common learning scheme. The two layers of an RBF network are learnt separately; first the RBF layer is trained, including the adaptation of centers and scaling parameters, and then the weights of the output layer are adapted. RBF centers may be trained by clustering, vector quantization and classification tree algorithms, and the output layer by supervised learning (through gradient descent or pseudo inverse solution). Results from numerical experiments of RBF classifiers trained by two-phase learning are presented in three completely different pattern recognition applications: (a) the classification of 3D visual objects; (b) the recognition hand-written digits (2D objects); and (c) the categorization of high-resolution electrocardiograms given as a time series (1D objects) and as a set of features extracted from these time series. In these applications, it can be observed that the performance of RBF classifiers trained with two-phase learning can be improved through a third backpropagation-like training phase of the RBF network, adapting the whole set of parameters (RBF centers, scaling parameters, and output layer weights) simultaneously. This, we call three-phase learning in RBF networks. A practical advantage of two- and three-phase learning in RBF networks is the possibility to use unlabeled training data for the first training phase. Support vector (SV) learning in RBF networks is a different learning approach. SV learning can be considered, in this context of learning, as a special type of one-phase learning, where only the output layer weights of the RBF network are calculated, and the RBF centers are restricted to be a subset of the training data. Numerical experiments with several classifier schemes including k-nearest-neighbor, learning vector quantization and RBF classifiers trained through two-phase, three-phase and support vector learning are given. The performance of the RBF classifiers trained through SV learning and three-phase learning are superior to the results of two-phase learning, but SV learning often leads to complex network structures, since the number of support vectors is not a small fraction of the total number of data points.