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In this study, the problem of real-time chaotic time-series prediction using Radial Basis Function Networks is addressed. The performance of a number of training methods based either on supervised error correction or on adaptive clustering techniques are investigated. Some performance drawbacks due to their exclusive usage are pointed out and a new algorithm combining their desirable properties is...
In this paper, we compare the capabilities of various forms of radial basis function networks as nonlinear short-term predictors for speech signals representing sustained utterances of German vowels. We use RBF and RBF-AR1 network architectures, trained using a standard algorithm or alternatively the extended Kalman filter (EKF) algorithm, and linear least squares predictors. We also look at cascaded...
The control of an acoustic echo canceller (AEC) is an essential part of hands-free telephone sets. Due to the fact that no single estimator is yet known to reliably control the AEC, various estimators should be implemented. Nevertheless, the combination of several estimators is quite difficult and usually determined heuristically. In this paper, an approach for automatic combination of estimators,...
In this paper, a new meta-cognitive RBF neural network classifier that uses a q-Gaussian activation function is presented. The q-Gaussian activation function has the capability to extend or contract the shape/response of the radial basis activation function, based on the value of the parameter q. This property is used to avoid a sharp fall in the response in the tail region, particularly when the...
In this paper, Radial Basis Function (RBF) neural Network has been implemented on eight directional values of gradient features for handwritten Hindi character recognition. The character recognition system was trained by using different samples in different handwritings collected of various people of different age groups. The Radial Basis Function network with one input and one output layer has been...
In this paper, we propose a modified Meta-Cognitive Radial Basis Function Network (McRBFN+) and its Projection Based Learning (PBL) algorithm for classification problems. During learning, as each sample is presented to McRBFN+, the modified meta-cognitive component monitors the prediction error and class-wise significance in cognitive component (RBFN) to efficiently decide on what-to-learn, when-to-learn...
Imaging is a broad field which covers all aspects of the analysis, modification, compression, visualization, and generation of images. There are at least two major areas in imaging science in which applied mathematics has a strong impact: image processing, and image reconstruction. In image processing the input is a (digital) image such as a photograph, while in image reconstruction the input is a...
This work presents a Java platform capable to emulate sound propagation in a controlled 2D environment (obstacles and sound sources selected by the user) based on a cellular automata model. The platform is expandable and so far includes a feature preprocessor for the echo waves and a neural classifier. The proposed virtual environment allows performing various virtual experiments with relevance in...
In the paper, the architecture of a pre-radical basis function(RBF) with deep back propagation(BP) neural network is proposed. The three-layer RBF network is altered into a two-layer RBF, the output of RBF hidden layer is processed and then connected with a multilayer perceptron network. Firstly, the input samples go through RBF hidden units and are pre-trained via unsupervised learning, after the...
A parallel neural network algorithm based on BP and RBF neural network for solving inverse kinematics of robot is proposed in this paper. Concrete steps of this method and related matters that should be noticed are presented. BP network is trained by LM algorithm and RBF network increases radial basis neurons automatically. The simulation results of PUMA560 show that this algorithm is simple and reliable,...
In this paper, the basic principle of space-voltage vector PWM (SVPWM) is presented. Due to back propagation neural networks (BP) based SVPWM controller have local optimization problem and lower training rate, a radial basis function neural networks (RBF) controller based SVPWM is proposed. Using Matlab/Simulink together with Neural Network Toolbox, we develop a computer simulation program for the...
This paper focuses on the nonlinear identification of an experimental pH neutralisation process using real data. The performances of radial basis function (RBF) and local linear model networks (LLMN) for identifying this significantly nonlinear process are compared. Results are presented to illustrate the choice of the various network parameters in the model structures for network training and validation...
In this paper, a precursor for Pitchfork bifurcation in axial compression system was proposed. Firstly, the bifurcation behavior of Moore-Greitzer model was analyzed; Secondly, based on the bifurcation behavior of Moore-Greitzer model, a precursor for Pitchfork bifurcation was proposed via deterministic learning, which was recently presented to learn unknown nonlinear system dynamics from uncertain...
Artificial neural network is a commonly used conversion model in voice conversion system, in which RBF is known for its concise convergence and fast learning. Based on optimizing the centers of RBF network, this article presents a method of using K-means algorithm to cluster and form centers and PSO algorithm to optimize the clustering number to improve the property of RBF, thus to enhance the transformation...
This paper studies the fault diagnosis of inertia navigation unit which plays an important role in inertia navigation system. The method chosen in the fault diagnosis is combined Genetic Algorithm and wavelet neural network. Wavelet transform will effectively handle the collected inertia navigation unit signal. The characteristic signals extracted will be regarded as inputs to the neural network....
Large variations in human actions lead to major challenges in computer vision research. Several algorithms are designed to solve the challenges. Algorithms that stand apart, help in solving the challenge in addition to performing faster and efficient manner. In this paper, we propose a human cognition inspired projection based learning for person-independent human action recognition in the H.264/AVC...
This paper presents the application of Prony analysis and radial basis function technique in estimating the fault location on transmission lines for a two area test system. Prony method is used for data pre-processing and this data are further used for training and testing radial basis function (RBF) network. Prony analysis is used to extract the modal contents and inter-area oscillation symptoms...
One of the disadvantages of using Artificial Neural Networks (ANNs) is their significant training time need, which scales with the complexity of the network and with the complexity of the problem that is needed to be solved. Radial Basis Function Neural Networks (RBFNNs) are neural networks that use the linear combination of radial basis functions, utilizing hybrid learning procedures which can solve...
By introducing an extra dimension to the inputs, sigmoid function can simulate the behavior of traditional RBF units. This paper introduces a sigmoid based RBF neuron and compares it with traditional RBF neuron. Neural networks composed of these neurons are trained with ErrCor algorithm on two classic experiments. Comparison results are presented to show advantages of the sigmoid based RBF model.
Many algorithms such as Support Vector Regression (SVR), Incremental Extreme Learning Machine (I-ELM), Convex Incremental Extreme Learning Machine (CI-ELM), and Enhanced random search based Incremental Extreme Learning Machine (EI-ELM) are being used in current research to solve various function approximation problems. This paper presents a modification to the I-ELM family of algorithms targeted specifically...
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