We present a method of modifyiog the structure of radial basis function (RBF) network to work with nonstationary series that exhibit homogeneous nonstationary behavior. In the original RBF network, the hidden node’s function is to sense the trajectory of the time series and to respond when there is a strong correlation between the input pattern and the hidden node’s center. This type of respons...

This paper introduces a novel method for human face detection with its orientation by using wavelet, principle component analysis (PCA) and redial basis networks. The input image is analyzed by two-dimensional wavelet and a two-dimensional stationary wavelet. The common goals concern are the image clearance and simplification, which are parts of de-noising or compression. We applied an effectiv...

Difficulty was known to get satisfactory measurement effect on precision in capacitive grain’s moisture measurement due to many influencing factors, such as temperature, species, compaction and so on. The data confusion method of Radial Basis Function (RBF) nerve network is adopted. With improved orthogonal optimal method, the RBF nerve network’s weight factors can be obtained. This method can ...

Radical Basis Function (RBF) networks have been widely used in time series prediction because of their simplicity, robustness, good approximation and generalization ability. However, it is still rather difficult to select the number and locations of the hidden units of the RBF network appropriately for a specific time series prediction problem. In this paper, the Generalized RBF networks have b...

Speaker identification is the process of determining which registered speaker provides a given utterance. Speaker identification required to make a claim on the identity of speaker from the Ns trained speaker in its user database. In this study, we propose the combination of clustering algorithm and the classification technique – subtractive and Radial Basis Function (RBF). The proposed techniq...

Radial Basis Function Neural Networks (RBF NNs) are one of the most applicable NNs in the classification of real targets. Despite the use of recursive methods and gradient descent for training RBF NNs, classification improper accuracy, failing to local minimum and low-convergence speed are defects of this type of network. In order to overcome these defects, heuristic and meta-heuristic algorith...

Abstract: Much attention has been paid to Taylor series expansion (TSE) method these years, which has been extensively used for solving nonlinear equations for its good robustness and accuracy of positioning. A Taylor-series expansion location algorithm based on the RBF neural network (RBF-TSE) is proposed before to the performance of TSE highly depends on the initial estimation. In order to ha...

three artificial neural networks (ann) models; general regression neural network (grnn), redial basis function (rbf) and three layer multiple perceptron network were carried out to evaluate the prediction of the apparent metabolizable energy (ame) of wheat and corn from its chemical composition in broiler. input variables included: gross energy (ge), crude protein (cp), crude fiber (cf), ether ...

TThe uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. This paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. As a novelty, the proposed controller employs a simple Gaussian Radial-Basis-Function Network as an uncertainty estimator. The proposed netw...

In this review we bring together some of our recent work from the angle of the diversified RBF topologies, including three different topologies; (i) the RBF network with tunable nodes; (ii) the Box-Cox output transformation based RBF network (Box-Cox RBF); and (iii) the RBF network with boundary value constraints (BVC-RBF). We show that the modified topologies have some advantages over the conv...