This paper experimentally investigates Independent Component Analysis (ICA) and Principle Component Analysis (PCA) on reducing the input dimension of a Radial Basis Function (RBF) network such that the net’s complexity is reduced. The results have shown that a RBF network with ICA as an input pre-process has the similar generalization ability to the one without pre-processing, but the former’s ...

A new learning algorithm called extreme learning machine (ELM) has recently been proposed for single-hidden layer feedforward neural networks (SLFNs) with additive neurons to easily achieve good generalization performance at extremely fast learning speed. ELM randomly chooses the input weights and analytically determines the output weights of SLFNs. It is proved in theory that ELM can be extend...

To effectively organize and analyze massive Web information, in this paper, we have designed a Web classification mining system. BP network has lots of disadvantages, so we have proposed a method that uses RBFNN (Radial Basis Function Neural Network) to classify the text information in Web pages. In this paper, the model of classification system mainly includes RBF (Radial Basis Function) class...

Three methods for improving the performance of (gaussian) radial basis function (RBF) networks were tested on the NETtaik task. In RBF, a new example is classified by computing its Euclidean distance to a set of centers chosen by unsupervised methods. The application of supervised learning to learn a non-Euclidean distance metric was found to reduce the error rate of RBF networks, while supervi...

In this paper, a nonlinear offline model of the solid oxide fuel cell (SOFC) is built by using a radial basis function (RBF) neural network based n a genetic algorithm (GA). During the process of modeling, the GA aims to optimize the parameters of RBF neural networks and the optimum alues are regarded as the initial values of the RBF neural network parameters. Furthermore, we utilize the gradie...

We present a novel numerical method for solving ordinary differential equations using radial basis function (RBF) network with extreme learning machine algorithm. A single-layer RBF link neural model has been developed the proposed method. The weight from hidden layer to output can be calculated efficiently by experimental comparison of various methods proves that shows better performance than ...

The problem of selecting the appropriate number of basis functions is a critical issue for radial basis function neural networks. An RBF network with an overly restricted basis gives poor predictions on new data, since the model has too little flexibility (yielding high bias and low variance). By contrast, an RBF network with too many basis functions also gives poor generalization performance s...

In this article a new neural-network architecture suitable for learning and generalization is discussed and developed. Although similar to the radial basis function (RBF) net, our computational model called the net with complex weights (CWN) has demonstrated a considerable gain in performance and efficiency in number of applications compared to RBF net. Its better performance in classification ...

We address a contrastive study between the well known Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks and a SOM based supervised architecture in a number of data classification tasks. Well known databases like Breast Cancer, Parkinson and Iris were used to evaluate the three architectures by constructing confusion matrices. The results are encouraging and indicate t...

A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network model is proposed for identification of non-linear systems. At each stage of orthogonal forward regression (OFR) model construction process, PSO is adopted to tune one RBF unit’s centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automat...