In this paper, a model reference adaptive sliding mode (MRASMC) using a radical basis function (RBF) neural network (NN) is proposed to control the single-phase active power filter (APF). The RBF NN is utilized to approximate the nonlinear function and eliminate the modeling error in the APF system. The model reference adaptive current controller in AC side not only guarantees the globally stab...

This paper proposes a new blind watermarking scheme based on discrete wavelet transform(DWT) domain. The method uses the HVS model, and radial basis function neural networks(RBF). RBF will be implemented while embedding and extracting watermark. The human visual system (HVS) model is used to determine the watermark insertion strength. The neural networks almost exactly recover the watermarking ...

A high performance speaker-independent isolated-word hybrid speech recognizer was developed which combines Hidden Markov Models (HMMs) and Radial Basis Function (RBF) neural networks. In recognition experiments using a speaker-independent E-set database, the hybrid recognizer had an error rate of 11.5% compared to 15.7% for the robust unimodal Gaussian HMM recognizer upon which the hybrid syste...

Estimation of suspended sediments in rivers using soft computing techniques has been extensively performed around the world since 1990’s. However, accuracy in the results was always found to be highly desired and a profound crucial task. This study presents a thorough comparison between the performances of best basis function of Radial Basis Functions (RBF) and the best training algorithm in Mu...

Abstract Multifidelity surrogates (MFSs) replace computationally intensive models by synergistically combining information from different fidelity data with a significant improvement in modeling efficiency. In this paper, modified MFS (MMFS) model based on radial basis function (RBF) is proposed, which two fidelities of can be analyzed adaptively obtaining the scale factor. MMFS, an RBF was emp...

Outlier detection has many important applications in the field of fraud detection, network robustness analysis and intrusion detection. Some researches have utilized the neural network to solve the problem because it has the advantage of powerful modeling ability. In this paper, we propose a RBF neural network model using subtractive clustering algorithm for selecting the hidden node centers, w...

In this paper, the effects of using multi RBF kernel for an online LSSVR on modeling and control performance are investigated. The Jacobian information of the system is estimated via online LSSVR model. Kernel parameter determines how the measured input is mapped to the feature space and a better plant model can be achieved by discarding redundant features. Therefore, introducing flexibility in...

This paper proposes a radial basis function (RBF) based approach for the fuel injection control problem. In the past, neural controllers for this problem have centred on using a cerebellar model articulation controller (CMAC) type network with some success. The current production engine control units also use look-up tables in their fuel injection controllers, and if adaptation is permitted to ...

The improvement of the accuracy of image query retrieval used image classification technique. Image classification is well known technique of supervised learning. The improved method of image classification increases the working efficiency of image query retrieval. For the improvements of classification technique we used RBF neural network function for better prediction of feature used in image...

INTRODUCTION To assess changes in renal blood flow (RBF) in human and experimental sepsis, and to identify determinants of RBF. METHOD Using specific search terms we systematically interrogated two electronic reference libraries to identify experimental and human studies of sepsis and septic acute renal failure in which RBF was measured. In the retrieved studies, we assessed the influence of ...