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Results for “radial basis function network”

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PubMed2005

Estimation of electrical conductivity distribution within the human head from magnetic flux density measurement.

We have developed a new algorithm for magnetic resonance electrical impedance tomography (MREIT), which uses only one component of the magnetic flux density to reconstruct the electrical conductivity distribution within the body. The radial basis function (RBF) network and simplex method are used in the present approach to estimate the conductivity distribution by minimizing the errors between the 'measured' and model-predicted magnetic flux densities. Computer simulations were conducted in a realistic-geometry head model to test the feasibility of the proposed approach. Single-variable and three-variable simulations were performed to estimate the brain-skull conductivity ratio and the conductivity values of the brain, skull and scalp layers. When SNR = 15 for magnetic flux density measurements with the target skull-to-brain conductivity ratio being 1/15, the relative error (RE) between the target and estimated conductivity was 0.0737 +/- 0.0746 in the single-variable simulations. In the three-variable simulations, the RE was 0.1676 +/- 0.0317. Effects of electrode position uncertainty were also assessed by computer simulations. The present promising results suggest the feasibility of estimating important conductivity values within the head from noninvasive magnetic flux density measurements.

Algorithms↗

PubMed2006

Digital image processing techniques for the detection and removal of cracks in digitized paintings.

An integrated methodology for the detection and removal of cracks on digitized paintings is presented in this paper. The cracks are detected by thresholding the output of the morphological top-hat transform. Afterward, the thin dark brush strokes which have been misidentified as cracks are removed using either a median radial basis function neural network on hue and saturation data or a semi-automatic procedure based on region growing. Finally, crack filling using order statistics filters or controlled anisotropic diffusion is performed. The methodology has been shown to perform very well on digitized paintings suffering from cracks.

Algorithms↗

PubMed2006

An RBF-based compression method for image-based relighting.

In image-based relighting, a pixel is associated with a number of sampled radiance values. This paper presents a two-level compression method. In the first level, the plenoptic property of a pixel is approximated by a spherical radial basis function (SRBF) network. That means that the spherical plenoptic function of each pixel is represented by a number of SRBF weights. In the second level, we apply a wavelet-based method to compress these SRBF weights. To reduce the visual artifact due to quantization noise, we develop a constrained method for estimating the SRBF weights. Our proposed approach is superior to JPEG, JPEG2000, and MPEG. Compared with the spherical harmonics approach, our approach has a lower complexity, while the visual quality is comparable. The real-time rendering method for our SRBF representation is also discussed.

Algorithms↗

PubMed2005

Analysis of augmented-input-layer RBFNN.

In this paper we present and analyze a new structure for designing a radial basis function neural network (RBFNN). In the training phase, input layer of RBFNN is augmented with desired output vector. Generalization phase involves the following steps: (1) identify the cluster to which a previously unseen input vector belongs; (2) augment the input layer with an average of the targets of the input vectors in the identified cluster; and (3) use the augmented network to estimate the unknown target. It is shown that, under some reasonable assumptions, the generalization error function admits an upper bound in terms of the quantization errors minimized when determining the centers of the proposed method over the training set and the difference between training samples and generalization samples in a deterministic setting. When the difference between the training and generalization samples goes to zero, the upper bound can be made arbitrarily small by increasing the number of hidden neurons. Computer simulations verified the effectiveness of the proposed method.

Neural Networks, Computer↗

PubMed2006

Stable neurovisual servoing for robot manipulators.

In this paper, we propose a stable neurovisual servoing algorithm for set-point control of planar robot manipulators in a fixed-camera configuration an show that all the closed-loop signals are uniformly ultimately bounded (UUB) and converge exponentially to a small compact set. We assume that the gravity term and Jacobian matrix are unknown. Radial basis function neural networks (RBFNNs) with online real-time learning are proposed for compensating both gravitational forces and errors in the robot Jacobian matrix. The learning rule for updating the neural network weights, similar to a back propagation algorithm, is obtained from a Lyapunov stability analysis. Experimental results on a two degrees of freedom manipulator are presented to evaluate the proposed controller.

Algorithms↗

PubMed2005

Modeling and analysis of gene expression time-series based on co-expression.

In this paper a novel approach is introduced for modeling and clustering gene expression time-series. The radial basis function neural networks have been used to produce a generalized and smooth characterization of the expression time-series. A co-expression coefficient is defined to evaluate the similarities of the models based on their temporal shapes and the distribution of the time points. The profiles are grouped using a fuzzy clustering algorithm incorporated with the proposed co-expression coefficient metric. The results on artificial and real data are presented to illustrate the advantages of the metric and method in grouping temporal profiles. The proposed metric has also been compared with the commonly used correlation coefficient under the same procedures and the results show that the proposed method produces better biologically relevant clusters.

Algorithms↗

PubMed2005

On the capabilities of higher-order neurons: a radial basis function approach.

Higher-order neurons with k monomials in n variables are shown to have Vapnik-Chervonenkis (VC) dimension at least nk + 1. This result supersedes the previously known lower bound obtained via k-term monotone disjunctive normal form (DNF) formulas. Moreover, it implies that the VC dimension of higher-order neurons with k monomials is strictly larger than the VC dimension of k-term monotone DNF. The result is achieved by introducing an exponential approach that employs gaussian radial basis function neural networks for obtaining classifications of points in terms of higher-order neurons.

Algorithms↗

PubMed2004

Inter-residue spatial distance map prediction by using integrating GA with RBFNN.

The spatial ordering information of amino acid residue in protein primary sequence is an important determinant of protein three-dimensional structure. In this paper, we describe a radial basis function neural network (RBFNN), whose hidden centers and basis function widths are optimized by a genetic algorithm (GA), for the purpose of predicting three dimensional spatial distance location from primary sequence information. Experimental evidence on soybean protein sequences indicates the utility of this approach.

Algorithms↗

PubMed2003

Estimation of urban runoff and water quality using remote sensing and artificial intelligence.

Water quality and quantity of runoff are strongly dependent on the landuse and landcover (LULC) criteria. In this study, we developed a more improved parameter estimation procedure for the environmental model using remote sensing (RS) and artificial intelligence (AI) techniques. Landsat TM multi-band (7bands) and Korea Multi-Purpose Satellite (KOMPSAT) panchromatic data were selected for input data processing. We employed two kinds of artificial intelligence techniques, RBF-NN (radial-basis-function neural network) and ANN (artificial neural network), to classify LULC of the study area. A bootstrap resampling method, a statistical technique, was employed to generate the confidence intervals and distribution of the unit load. SWMM was used to simulate the urban runoff and water quality and applied to the study watershed. The condition of urban flow and non-point contaminations was simulated with rainfall-runoff and measured water quality data. The estimated total runoff, peak time, and pollutant generation varied considerably according to the classification accuracy and percentile unit load applied. The proposed procedure would efficiently be applied to water quality and runoff simulation in a rapidly changing urban area.

Artificial Intelligence↗

PubMed2004

Study of quantitative structure-mobility relationship of carboxylic and sulphonic acids in capillary electrophoresis.

A quantitative structure-mobility relationship (QSMR) was developed for the absolute mobilities of 115 carboxylic and sulphonic acids in capillary electrophoresis based on the descriptors calculated from the structure alone. The heuristic method (HM) and radial basis function neural networks (RBFNN) were utilized to construct the linear and nonlinear prediction models, respectively. The prediction results were in agreement with the experimental values. The HM model gave an root-mean-square (RMS) error of 3.76 electrophoretic mobility units for the training set, 5.59 for the test set, and 4.19 for the whole data set, while the RBFNN gave an RMS error of 1.78, 2.04, and 1.83, respectively. The heuristic linear model could give some insights into the factors that are likely to govern the mobilities of the compounds, however, the prediction results of the RBFNN model seem to be better than that of the heuristic method.

Carboxylic Acids↗

PubMed2005

[Application of wavelet packets analysis-interpolation-RBF to the simultaneous determination of Fe, Mn, Cu and Zn in aluminous-alloy].

A new approach was proposed so that noises invisible spectra are eliminated by wavelet packets, and the specimen numbers of calibration samples are increased by linear interpolation. It can used to solve problems of few specimen numbers and many variables in simultaneous spectrophotometric determination. Thus forecasting resultant veracity is improved. The contents of Fe, Mn, Cu and Zn in aluminous-alloy was determined simultaneously by using wavelet packets analysis-linear interpolation-RBF (radial basis function) neural networks (WPA-Interp-RBF). Wavelet packets analysis can eliminate noises to a desired level. Linear interpolation makes specimenof training samples cover recognizable room better. So RBF can abstract more characteristic information and improve its performance. Relative error(RE) of sample is reduced remarkably with desired results.

English Abstract↗

PubMed2006

On global-local artificial neural networks for function approximation.

We present a hybrid radial basis function (RBF) sigmoid neural network with a three-step training algorithm that utilizes both global search and gradient descent training. The algorithm used is intended to identify global features of an input-output relationship before adding local detail to the approximating function. It aims to achieve efficient function approximation through the separate identification of aspects of a relationship that are expressed universally from those that vary only within particular regions of the input space. We test the effectiveness of our method using five regression tasks; four use synthetic datasets while the last problem uses real-world data on the wave overtopping of seawalls. It is shown that the hybrid architecture is often superior to architectures containing neurons of a single type in several ways: lower mean square errors are often achievable using fewer hidden neurons and with less need for regularization. Our global-local artificial neural network (GL-ANN) is also seen to compare favorably with both perceptron radial basis net and regression tree derived RBFs. A number of issues concerning the training of GL-ANNs are discussed: the use of regularization, the inclusion of a gradient descent optimization step, the choice of RBF spreads, model selection, and the development of appropriate stopping criteria.

Algorithms↗

PubMed2003

A new algorithm for online structure and parameter adaptation of RBF networks.

This paper deals with the problem of online adaptation of radial basis function (RBF) neural networks. A new adaptive training method is presented, which is able to modify both the structure of the network (the number of nodes in the hidden layer) and the output weights, as the algorithm proceeds. These adaptation capabilities make the algorithm suitable for modeling dynamical time varying systems, where not only the dynamics but also the operating region changes with time. Therefore, the important issue of extrapolation is faced successfully, but at the same time the algorithm takes care of the size of the network, by deleting the hidden node centers that remain inactive for a long time. The selection of the network centers is based on a fuzzy partition of the input space, which defines a number of fuzzy subspaces. The algorithm considers the centers of the fuzzy subspaces as candidates for becoming hidden node centers and makes the selections, so that at least one center is close enough to each input example. The proposed technique is illustrated through the application to time varying dynamical systems and is compared to other adaptive training methods.

Adaptation, Biological↗

PubMed2000

Short-term prediction of chaotic time series by using RBF network with regression weights.

We propose a framework for constructing and training a radial basis function (RBF) neural network. The structure of the gaussian functions is modified using a pseudo-gaussian function (PG) in which two scaling parameters sigma are introduced, which eliminates the symmetry restriction and provides the neurons in the hidden layer with greater flexibility with respect to function approximation. We propose a modified PG-BF (pseudo-gaussian basis function) network in which the regression weights are used to replace the constant weights in the output layer. For this purpose, a sequential learning algorithm is presented to adapt the structure of the network, in which it is possible to create a new hidden unit and also to detect and remove inactive units. A salient feature of the network systems is that the method used for calculating the overall output is the weighted average of the output associated with each receptive field. The superior performance of the proposed PG-BF system over the standard RBF are illustrated using the problem of short-term prediction of chaotic time series.

Algorithms↗

PubMedDate not supplied

Automatic basis selection techniques for RBF networks.

This paper proposes a generic criterion that defines the optimum number of basis functions for radial basis function (RBF) neural networks. The generalization performance of an RBF network relates to its prediction capability on independent test data. This performance gives a measure of the quality of the chosen model. 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 since it is too flexible and fits too much of the noise on the training data (yielding low bias but high variance). Bias and variance are complementary quantities, and it is necessary to assign the number of basis function optimally in order to achieve the best compromise between them. In this paper we use Stein's unbiased risk estimator to derive an analytical criterion for assigning the appropriate number of basis functions. Two cases of known and unknown noise have been considered and the efficacy of this criterion in both situations is illustrated experimentally. The paper also shows an empirical comparison between this method and two well known classical methods, cross validation and the Bayesian information criterion, BIC.

Electronic Data Processing↗

PubMed2006

A hybrid forward algorithm for RBF neural network construction.

This paper proposes a novel hybrid forward algorithm (HFA) for the construction of radial basis function (RBF) neural networks with tunable nodes. The main objective is to efficiently and effectively produce a parsimonious RBF neural network that generalizes well. In this study, it is achieved through simultaneous network structure determination and parameter optimization on the continuous parameter space. This is a mixed integer hard problem and the proposed HFA tackles this problem using an integrated analytic framework, leading to significantly improved network performance and reduced memory usage for the network construction. The computational complexity analysis confirms the efficiency of the proposed algorithm, and the simulation results demonstrate its effectiveness.

Algorithms↗

PubMed2005

High-speed face recognition based on discrete cosine transform and RBF neural networks.

In this paper, an efficient method for high-speed face recognition based on the discrete cosine transform (DCT), the Fisher's linear discriminant (FLD) and radial basis function (RBF) neural networks is presented. First, the dimensionality of the original face image is reduced by using the DCT and the large area illumination variations are alleviated by discarding the first few low-frequency DCT coefficients. Next, the truncated DCT coefficient vectors are clustered using the proposed clustering algorithm. This process makes the subsequent FLD more efficient. After implementing the FLD, the most discriminating and invariant facial features are maintained and the training samples are clustered well. As a consequence, further parameter estimation for the RBF neural networks is fulfilled easily which facilitates fast training in the RBF neural networks. Simulation results show that the proposed system achieves excellent performance with high training and recognition speed, high recognition rate as well as very good illumination robustness.

Algorithms↗

PubMed2006

Spatio-temporal modeling and analysis of fMRI data using NARX neural network.

This paper presents spatio-temporal modeling and analysis methods to fMRI data. Based on the nonlinear autoregressive with exogenous inputs (NARX) model realized by the Bayesian radial basis function (RBF) neural networks, two methods (NARX-1 and NARX-2) are proposed to capture the unknown complex dynamics of the brain activities. Simulation results on both synthetic and real fMRI data clearly show that the proposed schemes outperform the conventional t-test method in detecting the activated regions of the brain.

Bayes Theorem↗