Generalized multiscale radial basis function networks.
Billings, Stephen A; Wei, Hua-Liang; Balikhin, Michael A
2007-12-01
A novel modelling framework is proposed for constructing parsimonious and flexible multiscale radial basis function networks (RBF). Unlike a conventional standard single scale RBF network, where all the basis functions have a common kernel width, the new network structure adopts multiscale Gaussian functions as the bases, where each selected centre has multiple kernel widths, to provide more flexible representations with better generalization properties for general nonlinear dynamical systems. As a direct extension of the traditional single scale Gaussian networks, the new multiscale network is easy to implement and is quick to learn using standard learning algorithms. A k-means clustering algorithm and an improved orthogonal least squares (OLS) algorithm are used to determine the unknown parameters in the network model including the centres and widths of the basis functions, and the weights between the basis functions. It is demonstrated that the new network can lead to a parsimonious model with much better generalization property compared with the traditional single width RBF networks.
Generalization performance of radial basis function networks.
Lei, Yunwen; Ding, Lixin; Zhang, Wensheng
2015-03-01
This paper studies the generalization performance of radial basis function (RBF) networks using local Rademacher complexities. We propose a general result on controlling local Rademacher complexities with the L1 -metric capacity. We then apply this result to estimate the RBF networks' complexities, based on which a novel estimation error bound is obtained. An effective approximation error bound is also derived by carefully investigating the Hölder continuity of the lp loss function's derivative. Furthermore, it is demonstrated that the RBF network minimizing an appropriately constructed structural risk admits a significantly better learning rate when compared with the existing results. An empirical study is also performed to justify the application of our structural risk in model selection.
Radial basis function networks and complexity regularization in function learning.
Krzyzak, A; Linder, T
1998-01-01
In this paper we apply the method of complexity regularization to derive estimation bounds for nonlinear function estimation using a single hidden layer radial basis function network. Our approach differs from previous complexity regularization neural-network function learning schemes in that we operate with random covering numbers and l(1) metric entropy, making it possible to consider much broader families of activation functions, namely functions of bounded variation. Some constraints previously imposed on the network parameters are also eliminated this way. The network is trained by means of complexity regularization involving empirical risk minimization. Bounds on the expected risk in terms of the sample size are obtained for a large class of loss functions. Rates of convergence to the optimal loss are also derived.
Radial basis function network design for chaotic time series prediction
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Shin, Chang Yong; Kim, Taek Soo; Park, Sang Hui [Yonsei University, Seoul (Korea, Republic of); Choi, Yoon Ho [Kyonggi University, Suwon (Korea, Republic of)
1996-04-01
In this paper, radial basis function networks with two hidden layers, which employ the K-means clustering method and the hierarchical training, are proposed for improving the short-term predictability of chaotic time series. Furthermore the recursive training method of radial basis function network using the recursive modified Gram-Schmidt algorithm is proposed for the purpose. In addition, the radial basis function networks trained by the proposed training methods are compared with the X.D. He A Lapedes`s model and the radial basis function network by non-recursive training method. Through this comparison, an improved radial basis function network for predicting chaotic time series is presented. (author). 17 refs., 8 figs., 3 tabs.
Side effects of normalising radial basis function networks.
Shorten, R; Murray-Smith, R
1996-05-01
Normalisation of the basis function activations in a Radial Basis Function (RBF) network is a common way of achieving the partition of unity often desired for modelling applications. It results in the basis functions covering the whole of the input space to the same degree. However, normalisation of the basis functions can lead to other effects which are sometimes less desirable for modelling applications. This paper describes some side effects of normalisation which fundamentally alter properties of the basis functions, e.g. the shape is no longer uniform, maxima of basis functions can be shifted from their centres, and the basis functions are no longer guaranteed to decrease monotonically as distance from their centre increases--in many cases basis functions can 'reactivate', i.e. re-appear far from the basis function centre. This paper examines how these phenomena occur, discusses their relevance for non-linear function approximation and examines the effect of normalisation on the network condition number and weights.
Integration of macromolecular diffraction data using radial basis function networks.
Pokrić, B; Allinson, N M; Helliwell, J R
2000-11-01
This paper presents a novel approach for intensity calculation of X-ray diffraction spots based on a two-stage radial basis function (RBF) network. The first stage uses pre-determined reference profiles from a database as basis functions in order to locate the diffraction spots and identify any overlapping regions. The second-stage RBF network employs narrow basis functions capable of local modifications of the reference profiles leading to a more accurate observed diffraction spot approximation and therefore accurate determination of spot positions and integrated intensities.
On the classification enhancement of radial basis function networks
Ciftcioglu, O.; Durmisevic, S.; Sariyildiz, I.S.
2001-01-01
Artificial neural networks are powerfultools for analysing information expressed as data sets, which contain complex nonlinear relationships to be identified and classified. In particular radial basis function (RBF) neural networks have outstanding features for this. However, due to far reaching imp
Orthogonal least squares learning algorithm for radial basis function networks
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Chen, S.; Cowan, C.F.N.; Grant, P.M. (Dept. of Electrical Engineering, Univ. of Edinburgh, Mayfield Road, Edinburgh EH9 3JL, Scotland (GB))
1991-03-01
The radial basis function network offers a viable alternative to the two-layer neural network in many applications of signal processing. A common learning algorithm for radial basis function networks is based on first choosing randomly some data points as radial basis function centers and then using singular value decomposition to solve for the weights of the network. Such a procedure has several drawbacks and, in particular, an arbitrary selection of centers is clearly unsatisfactory. The paper proposes an alternative learning procedure based on the orthogonal least squares method. The procedure choose radial basis function centers one by one in a rational way until an adequate network has been constructed. The algorithm has the property that each selected center maximizes the increment to the explained variance or energy of the desired output and does not suffer numerical ill-conditioning problems. The orthogonal least squares learning strategy provides a simple and efficient means for fitting radial basis function networks, and this is illustrated using examples taken from two different signal processing applications.
Orthogonal least squares learning algorithm for radial basis function networks.
Chen, S; Cowan, C N; Grant, P M
1991-01-01
The radial basis function network offers a viable alternative to the two-layer neural network in many applications of signal processing. A common learning algorithm for radial basis function networks is based on first choosing randomly some data points as radial basis function centers and then using singular-value decomposition to solve for the weights of the network. Such a procedure has several drawbacks, and, in particular, an arbitrary selection of centers is clearly unsatisfactory. The authors propose an alternative learning procedure based on the orthogonal least-squares method. The procedure chooses radial basis function centers one by one in a rational way until an adequate network has been constructed. In the algorithm, each selected center maximizes the increment to the explained variance or energy of the desired output and does not suffer numerical ill-conditioning problems. The orthogonal least-squares learning strategy provides a simple and efficient means for fitting radial basis function networks. This is illustrated using examples taken from two different signal processing applications.
Universal approximation by radial basis function networks of Delsarte translates.
Arteaga, Cristian; Marrero, Isabel
2013-10-01
We prove that, under certain mild conditions on the kernel function (or activation function), the family of radial basis function neural networks obtained by replacing the usual translation with the Delsarte one, and taking the same smoothing factor in all kernel nodes, has the universal approximation property.
Using Radial-basis Function Network for CLV
Institute of Scientific and Technical Information of China (English)
李纯青; 郑建国
2002-01-01
Analysis and comparing with three existing and popularly used forcasting customer lifetime value (CLV) methods, which are the Dwyer method, customer event-method and fitting method, and using performances of the existent artificial neural network, we apply the Radial-basis Function(RBF) network to forecast the CLV, the RBF network can approach the objective function partially. To every input/output pairs, it only needs adjust the weight a little and learn quickly which is very important to the forecast precision. Simulation and experimental results on the customers' data of a company in Shaanxi Province reveal the effectiveness and feasibility of the RBF network.
Radial basis function neural networks applied to NASA SSME data
Wheeler, Kevin R.; Dhawan, Atam P.
1993-01-01
This paper presents a brief report on the application of Radial Basis Function Neural Networks (RBFNN) to the prediction of sensor values for fault detection and diagnosis of the Space Shuttle's Main Engines (SSME). The location of the Radial Basis Function (RBF) node centers was determined with a K-means clustering algorithm. A neighborhood operation about these center points was used to determine the variances of the individual processing notes.
Implementation of Radial Basis Function Neural Network for Image Steganalysis
Sambasiva Rao Baragada; S. Ramakrishna; M.S. Rao; S. Purushothaman
2008-01-01
Steganographic tools and techniques are becoming more potential and widespread. Illegal use of steganography poses serious challenges to the law enforcement agencies. Limited work has been carried out on supervised steganalysis using neural network as a classifier. We present a combined method of identifying the presence of covert information in a carrier image using fisherÃ¢â‚¬â„¢s linear discriminant (FLD) function followed by the radial basis function (RBF). Experiments show promising resu...
Direction-dependent learning approach for radial basis function networks.
Singla, Puneet; Subbarao, Kamesh; Junkins, John L
2007-01-01
Direction-dependent scaling, shaping, and rotation of Gaussian basis functions are introduced for maximal trend sensing with minimal parameter representations for input output approximation. It is shown that shaping and rotation of the radial basis functions helps in reducing the total number of function units required to approximate any given input-output data, while improving accuracy. Several alternate formulations that enforce minimal parameterization of the most general radial basis functions are presented. A novel "directed graph" based algorithm is introduced to facilitate intelligent direction based learning and adaptation of the parameters appearing in the radial basis function network. Further, a parameter estimation algorithm is incorporated to establish starting estimates for the model parameters using multiple windows of the input-output data. The efficacy of direction-dependent shaping and rotation in function approximation is evaluated by modifying the minimal resource allocating network and considering different test examples. The examples are drawn from recent literature to benchmark the new algorithm versus existing methods.
Learning without local minima in radial basis function networks.
Bianchini, M; Frasconi, P; Gori, M
1995-01-01
Learning from examples plays a central role in artificial neural networks. The success of many learning schemes is not guaranteed, however, since algorithms like backpropagation may get stuck in local minima, thus providing suboptimal solutions. For feedforward networks, optimal learning can be achieved provided that certain conditions on the network and the learning environment are met. This principle is investigated for the case of networks using radial basis functions (RBF). It is assumed that the patterns of the learning environment are separable by hyperspheres. In that case, we prove that the attached cost function is local minima free with respect to all the weights. This provides us with some theoretical foundations for a massive application of RBF in pattern recognition.
Modeling Marine Electromagnetic Survey with Radial Basis Function Networks
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Agus Arif
2011-08-01
Full Text Available A marine electromagnetic survey is an engineering endeavour to discover the location and dimension of a hydrocarbon layer under an ocean floor. In this kind of survey, an array of electric and magnetic receivers are located on the sea floor and record the scattered, refracted and reflected electromagnetic wave, which has been transmitted by an electric dipole antenna towed by a vessel. The data recorded in receivers must be processed and further analysed to estimate the hydrocarbon location and dimension. To conduct those analyses successfuly, a radial basis function (RBF network could be employed to become a forward model of the input-output relationship of the data from a marine electromagnetic survey. This type of neural networks is working based on distances between its inputs and predetermined centres of some basis functions. A previous research had been conducted to model the same marine electromagnetic survey using another type of neural networks, which is a multi layer perceptron (MLP network. By comparing their validation and training performances (mean-squared errors and correlation coefficients, it is concluded that, in this case, the MLP network is comparatively better than the RBF network
Combining regression trees and radial basis function networks.
Orr, M; Hallam, J; Takezawa, K; Murra, A; Ninomiya, S; Oide, M; Leonard, T
2000-12-01
We describe a method for non-parametric regression which combines regression trees with radial basis function networks. The method is similar to that of Kubat, who was first to suggest such a combination, but has some significant improvements. We demonstrate the features of the new method, compare its performance with other methods on DELVE data sets and apply it to a real world problem involving the classification of soybean plants from digital images.
An incremental design of radial basis function networks.
Yu, Hao; Reiner, Philip D; Xie, Tiantian; Bartczak, Tomasz; Wilamowski, Bogdan M
2014-10-01
This paper proposes an offline algorithm for incrementally constructing and training radial basis function (RBF) networks. In each iteration of the error correction (ErrCor) algorithm, one RBF unit is added to fit and then eliminate the highest peak (or lowest valley) in the error surface. This process is repeated until a desired error level is reached. Experimental results on real world data sets show that the ErrCor algorithm designs very compact RBF networks compared with the other investigated algorithms. Several benchmark tests such as the duplicate patterns test and the two spiral problem were applied to show the robustness of the ErrCor algorithm. The proposed ErrCor algorithm generates very compact networks. This compactness leads to greatly reduced computation times of trained networks.
Radial basis function networks for fast contingency ranking
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Devaraj, D.; Ramar, K. [Indian Inst. of Technology, Madras (India). Dept. of Electrical Engineering; Yegnanarayana, B. [Indian Inst. of Technology, Madras (India). Dept. of Computer Science and Engineering
2002-06-01
This paper presents an artificial neural network-based approach for static-security assessment. The proposed approach uses radial basis function (RBF) networks to predict the system severity level following a given list of contingencies. The RBF networks are trained off-line to capture the nonlinear relationship between the pre-contingency line flows and the post-contingency severity index. A method based on mutual information is proposed for selecting the input features of the networks. Mutual information has the advantage of measuring the general relationship between the independent variables and the dependent variables as against the linear relationship measured by the correlation-based methods. The performance of the proposed approach is demonstrated through contingency ranking in IEEE 30-bus test system. (author)
A growing and pruning method for radial basis function networks.
Bortman, M; Aladjem, M
2009-06-01
A recently published generalized growing and pruning (GGAP) training algorithm for radial basis function (RBF) neural networks is studied and modified. GGAP is a resource-allocating network (RAN) algorithm, which means that a created network unit that consistently makes little contribution to the network's performance can be removed during the training. GGAP states a formula for computing the significance of the network units, which requires a d-fold numerical integration for arbitrary probability density function p(x) of the input data x (x in R(d)) . In this work, the GGAP formula is approximated using a Gaussian mixture model (GMM) for p(x) and an analytical solution of the approximated unit significance is derived. This makes it possible to employ the modified GGAP for input data having complex and high-dimensional p(x), which was not possible in the original GGAP. The results of an extensive experimental study show that the modified algorithm outperforms the original GGAP achieving both a lower prediction error and reduced complexity of the trained network.
Implementation of Radial Basis Function Neural Network for Image Steganalysis
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Sambasiva Rao Baragada
2008-09-01
Full Text Available Steganographic tools and techniques are becoming more potential and widespread. Illegal use of steganography poses serious challenges to the law enforcement agencies. Limited work has been carried out on supervised steganalysis using neural network as a classifier. We present a combined method of identifying the presence of covert information in a carrier image using fisherÃ¢â‚¬â„¢s linear discriminant (FLD function followed by the radial basis function (RBF. Experiments show promising results when compared to the existing supervised steganalysis methods, but arranging the retrieved information is still a challenging problem.
Learning and generalization in radial basis function networks.
Freeman, J A; Saad, D
1995-09-01
The two-layer radial basis function network, with fixed centers of the basis functions, is analyzed within a stochastic training paradigm. Various definitions of generalization error are considered, and two such definitions are employed in deriving generic learning curves and generalization properties, both with and without a weight decay term. The generalization error is shown analytically to be related to the evidence and, via the evidence, to the prediction error and free energy. The generalization behavior is explored; the generic learning curve is found to be inversely proportional to the number of training pairs presented. Optimization of training is considered by minimizing the generalization error with respect to the free parameters of the training algorithms. Finally, the effect of the joint activations between hidden-layer units is examined and shown to speed training.
Radial Basis Function Networks for Conversion of Sound Spectra
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Carlo Drioli
2001-03-01
Full Text Available In many advanced signal processing tasks, such as pitch shifting, voice conversion or sound synthesis, accurate spectral processing is required. Here, the use of Radial Basis Function Networks (RBFN is proposed for the modeling of the spectral changes (or conversions related to the control of important sound parameters, such as pitch or intensity. The identification of such conversion functions is based on a procedure which learns the shape of the conversion from few couples of target spectra from a data set. The generalization properties of RBFNs provides for interpolation with respect to the pitch range. In the construction of the training set, mel-cepstral encoding of the spectrum is used to catch the perceptually most relevant spectral changes. Moreover, a singular value decomposition (SVD approach is used to reduce the dimension of conversion functions. The RBFN conversion functions introduced are characterized by a perceptually-based fast training procedure, desirable interpolation properties and computational efficiency.
Snow cover thickness estimation by using radial basis function networks
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A. Guidali
2012-07-01
Full Text Available This work investigates learning and generalisation capabilities of radial basis function networks (RBFN used to solve snow cover thickness estimation model as regression and classification. The model is based on a minimal set of climatic and topographic data collected from a limited number of stations located in the Italian Central Alps. Several experiments have been conceived and conducted adopting different evaluation indexes in both regression and classification tasks. The snow cover thickness estimation by RBFN has been proved a valuable tool able to deal with several critical aspects arising from the specific experimental context.
Nonlinear Time Series Forecast Using Radial Basis Function Neural Networks
Institute of Scientific and Technical Information of China (English)
ZHENGXin; CHENTian-Lun
2003-01-01
In the research of using Radial Basis Function Neural Network (RBF NN) forecasting nonlinear time series, we investigate how the different clusterings affect the process of learning and forecasting. We find that k-means clustering is very suitable. In order to increase the precision we introduce a nonlinear feedback term to escape from the local minima of energy, then we use the model to forecast the nonlinear time series which are produced by Mackey-Glass equation and stocks. By selecting the k-means clustering and the suitable feedback term, much better forecasting results are obtained.
Nonlinear Time Series Forecast Using Radial Basis Function Neural Networks
Institute of Scientific and Technical Information of China (English)
ZHENG Xin; CHEN Tian-Lun
2003-01-01
In the research of using Radial Basis Function Neural Network (RBF NN) forecasting nonlinear timeseries, we investigate how the different clusterings affect the process of learning and forecasting. We find that k-meansclustering is very suitable. In order to increase the precision we introduce a nonlinear feedback term to escape from thelocal minima of energy, then we use the model to forecast the nonlinear time series which are produced by Mackey-Glassequation and stocks. By selecting the k-means clustering and the suitable feedback term, much better forecasting resultsare obtained.
Synchronization of chaos using radial basis functions neural networks
Institute of Scientific and Technical Information of China (English)
Ren Haipeng; Liu Ding
2007-01-01
The Radial Basis Functions Neural Network (RBFNN) is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response system can be implemented by employing the RBFNN model and state feedback control. In this case, the exact mathematical model, which is the precondition for the conventional method, is unnecessary for implementing synchronization. The effect of the model error is investigated and a corresponding theorem is developed. The effect of the parameter perturbations and the measurement noise is investigated through simulations. The simulation results under different conditions show the effectiveness of the method.
Dynamics of learning near singularities in radial basis function networks.
Wei, Haikun; Amari, Shun-Ichi
2008-09-01
The radial basis function (RBF) networks are one of the most widely used models for function approximation in the regression problem. In the learning paradigm, the best approximation is recursively or iteratively searched for based on observed data (teacher signals). One encounters difficulties in such a process when two component basis functions become identical, or when the magnitude of one component becomes null. In this case, the number of the components reduces by one, and then the reduced component recovers as the learning process proceeds further, provided such a component is necessary for the best approximation. Strange behaviors, especially the plateau phenomena, have been observed in dynamics of learning when such reduction occurs. There exist singularities in the space of parameters, and the above reduction takes place at the singular regions. This paper focuses on a detailed analysis of the dynamical behaviors of learning near the overlap and elimination singularities in RBF networks, based on the averaged learning equation that is applicable to both on-line and batch mode learning. We analyze the stability on the overlap singularity by solving the eigenvalues of the Hessian explicitly. Based on the stability analysis, we plot the analytical dynamic vector fields near the singularity, which are then compared to those real trajectories obtained by a numeric method. We also confirm the existence of the plateaus in both batch and on-line learning by simulation.
Efficient VLSI Architecture for Training Radial Basis Function Networks
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Wen-Jyi Hwang
2013-03-01
Full Text Available This paper presents a novel VLSI architecture for the training of radial basis function (RBF networks. The architecture contains the circuits for fuzzy C-means (FCM and the recursive Least Mean Square (LMS operations. The FCM circuit is designed for the training of centers in the hidden layer of the RBF network. The recursive LMS circuit is adopted for the training of connecting weights in the output layer. The architecture is implemented by the field programmable gate array (FPGA. It is used as a hardware accelerator in a system on programmable chip (SOPC for real-time training and classification. Experimental results reveal that the proposed RBF architecture is an effective alternative for applications where fast and efficient RBF training is desired.
Efficient VLSI architecture for training radial basis function networks.
Fan, Zhe-Cheng; Hwang, Wen-Jyi
2013-03-19
This paper presents a novel VLSI architecture for the training of radial basis function (RBF) networks. The architecture contains the circuits for fuzzy C-means (FCM) and the recursive Least Mean Square (LMS) operations. The FCM circuit is designed for the training of centers in the hidden layer of the RBF network. The recursive LMS circuit is adopted for the training of connecting weights in the output layer. The architecture is implemented by the field programmable gate array (FPGA). It is used as a hardware accelerator in a system on programmable chip (SOPC) for real-time training and classification. Experimental results reveal that the proposed RBF architecture is an effective alternative for applications where fast and efficient RBF training is desired.
Snow cover thickness estimation using radial basis function networks
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E. Binaghi
2013-05-01
Full Text Available This paper reports an experimental study designed for the in-depth investigation of how the radial basis function network (RBFN estimates snow cover thickness as a function of climate and topographic parameters. The estimation problem is modeled in terms of both function regression and classification, obtaining continuous and discrete thickness values, respectively. The model is based on a minimal set of climatic and topographic data collected from a limited number of stations located in the Italian Central Alps. Several experiments have been conceived and conducted adopting different evaluation indexes. A comparison analysis was also developed for a quantitative evaluation of the advantages of the RBFN method over to conventional widely used spatial interpolation techniques when dealing with critical situations originated by lack of data and limited n-homogeneously distributed instrumented sites. The RBFN model proved competitive behavior and a valuable tool in critical situations in which conventional techniques suffer from a lack of representative data.
Ultrasonic flaw detection using radial basis function networks (RBFNs).
Gil Pita, R; Vicen, R; Rosa, M; Jarabo, M P; Vera, P; Curpian, J
2004-04-01
Ultrasonic flaw detection has been studied many times in the literature. Schemes based on thresholding after a previous matched filter use to be the best solution, but results obtained with this method are only satisfactory when scattering and attenuation are not considered. In this paper, we propose an alternative solution to thresholding detection method. We deal with the usage of different flaw detection methods comparing them with the proposed one. The experiment tries to determinate whether a given ultrasonic signal contains a flaw echo or not. Starting with a set of 24,000 patterns with 750 samples each one, two subsets are defined for the experiments. The first one, the training set, is used to obtain the detection parameters of the different methods, and the second one is used to test the performance of them. The proposed method is based on radial basis functions networks, one of the most powerful neural network techniques. This signal processing technique tries to find the optimal decision criterion. Comparing this method with thresholding based ones, an improvement over 25-30% is obtained, depending on the probability of false alarm. So our new method is a good alternative to flaw detection problem.
Radial basis function networks GPU-based implementation.
Brandstetter, Andreas; Artusi, Alessandro
2008-12-01
Neural networks (NNs) have been used in several areas, showing their potential but also their limitations. One of the main limitations is the long time required for the training process; this is not useful in the case of a fast training process being required to respond to changes in the application domain. A possible way to accelerate the learning process of an NN is to implement it in hardware, but due to the high cost and the reduced flexibility of the original central processing unit (CPU) implementation, this solution is often not chosen. Recently, the power of the graphic processing unit (GPU), on the market, has increased and it has started to be used in many applications. In particular, a kind of NN named radial basis function network (RBFN) has been used extensively, proving its power. However, their limiting time performances reduce their application in many areas. In this brief paper, we describe a GPU implementation of the entire learning process of an RBFN showing the ability to reduce the computational cost by about two orders of magnitude with respect to its CPU implementation.
Neuronal spike sorting based on radial basis function neural networks
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Taghavi Kani M
2011-02-01
Full Text Available "nBackground: Studying the behavior of a society of neurons, extracting the communication mechanisms of brain with other tissues, finding treatment for some nervous system diseases and designing neuroprosthetic devices, require an algorithm to sort neuralspikes automatically. However, sorting neural spikes is a challenging task because of the low signal to noise ratio (SNR of the spikes. The main purpose of this study was to design an automatic algorithm for classifying neuronal spikes that are emitted from a specific region of the nervous system."n "nMethods: The spike sorting process usually consists of three stages: detection, feature extraction and sorting. We initially used signal statistics to detect neural spikes. Then, we chose a limited number of typical spikes as features and finally used them to train a radial basis function (RBF neural network to sort the spikes. In most spike sorting devices, these signals are not linearly discriminative. In order to solve this problem, the aforesaid RBF neural network was used."n "nResults: After the learning process, our proposed algorithm classified any arbitrary spike. The obtained results showed that even though the proposed Radial Basis Spike Sorter (RBSS reached to the same error as the previous methods, however, the computational costs were much lower compared to other algorithms. Moreover, the competitive points of the proposed algorithm were its good speed and low computational complexity."n "nConclusion: Regarding the results of this study, the proposed algorithm seems to serve the purpose of procedures that require real-time processing and spike sorting.
Organisms modeling: The question of radial basis function networks
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Muzy Alexandre
2014-01-01
Full Text Available There exists usually a gap between bio-inspired computational techniques and what biologists can do with these techniques in their current researches. Although biology is the root of system-theory and artifical neural networks, computer scientists are tempted to build their own systems independently of biological issues. This publication is a first-step re-evalution of an usual machine learning technique (radial basis funtion(RBF networks in the context of systems and biological reactive organisms.
Estimation of spatiotemporal neural activity using radial basis function networks.
Anderson, R W; Das, S; Keller, E L
1998-12-01
We report a method using radial basis function (RBF) networks to estimate the time evolution of population activity in topologically organized neural structures from single-neuron recordings. This is an important problem in neuroscience research, as such estimates may provide insights into systems-level function of these structures. Since single-unit neural data tends to be unevenly sampled and highly variable under similar behavioral conditions, obtaining such estimates is a difficult task. In particular, a class of cells in the superior colliculus called buildup neurons can have very narrow regions of saccade vectors for which they discharge at high rates but very large surround regions over which they discharge at low, but not zero, levels. Estimating the dynamic movement fields for these cells for two spatial dimensions at closely spaced timed intervals is a difficult problem, and no general method has been described that can be applied to all buildup cells. Estimation of individual collicular cells' spatiotemporal movement fields is a prerequisite for obtaining reliable two-dimensional estimates of the population activity on the collicular motor map during saccades. Therefore, we have developed several computational-geometry-based algorithms that regularize the data before computing a surface estimation using RBF networks. The method is then expanded to the problem of estimating simultaneous spatiotemporal activity occurring across the superior colliculus during a single movement (the inverse problem). In principle, this methodology could be applied to any neural structure with a regular, two-dimensional organization, provided a sufficient spatial distribution of sampled neurons is available.
Extending the functional equivalence of radial basis function networks and fuzzy inference systems.
Hunt, K J; Haas, R; Murray-Smith, R
1996-01-01
We establish the functional equivalence of a generalized class of Gaussian radial basis function (RBFs) networks and the full Takagi-Sugeno model (1983) of fuzzy inference. This generalizes an existing result which applies to the standard Gaussian RBF network and a restricted form of the Takagi-Sugeno fuzzy system. The more general framework allows the removal of some of the restrictive conditions of the previous result.
Functional equivalence between radial basis function networks and fuzzy inference systems.
Jang, J R; Sun, C T
1993-01-01
It is shown that, under some minor restrictions, the functional behavior of radial basis function networks (RBFNs) and that of fuzzy inference systems are actually equivalent. This functional equivalence makes it possible to apply what has been discovered (learning rule, representational power, etc.) for one of the models to the other, and vice versa. It is of interest to observe that two models stemming from different origins turn out to be functionally equivalent.
Anderson, H C; Lotfi, A; Westphal, L C; Jang, J R
1998-01-01
The above paper claims that under a set of minor restrictions radial basis function networks and fuzzy inference systems are functionally equivalent. The purpose of this letter is to show that this set of restrictions is incomplete and that, when it is completed, the said functional equivalence applies only to a small range of fuzzy inference systems. In addition, a modified set of restrictions is proposed which is applicable for a much wider range of fuzzy inference systems.
Sherstinsky, A; Picard, R W
1996-01-01
The efficiency of the orthogonal least squares (OLS) method for training approximation networks is examined using the criterion of energy compaction. We show that the selection of basis vectors produced by the procedure is not the most compact when the approximation is performed using a nonorthogonal basis. Hence, the algorithm does not produce the smallest possible networks for a given approximation error. Specific examples are given using the Gaussian radial basis functions type of approximation networks.
A Novel Algorithm of Network Trade Customer Classification Based on Fourier Basis Functions
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Li Xinwu
2013-11-01
Full Text Available Learning algorithm of neural network is always an important research contents in neural network theory research and application field, learning algorithm about the feed-forward neural network has no satisfactory solution in particular for its defects in calculation speed. The paper presents a new Fourier basis functions neural network algorithm and applied it to classify network trade customer. First, 21 customer classification indicators are designed, based on characteristics and behaviors analysis of network trade customer, including customer characteristics type variables and customer behaviors type variables,; Second, Fourier basis functions is used to improve the calculation flow and algorithm structure of original BP neural network algorithm to speed up its convergence and then a new Fourier basis neural network model is constructed. Finally the experimental results show that the problem of convergence speed can been solved, and the accuracy of the customer classification are ensured when the new algorithm is used in network trade customer classification practically.
An Efficient Weather Forecasting System using Radial Basis Function Neural Network
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Tiruvenkadam Santhanam
2011-01-01
Full Text Available Problem statement: Accurate weather forecasting plays a vital role for planning day to day activities. Neural network has been use in numerous meteorological applications including weather forecasting. Approach: A neural network model has been developed for weather forecasting, based on various factors obtained from meteorological experts. This study evaluates the performance of Radial Basis Function (RBF with Back Propagation (BPN neural network. The back propagation neural network and radial basis function neural network were used to test the performance in order to investigate effective forecasting technique. Results: The prediction accuracy of RBF was 88.49%. Conclusion: The results indicate that proposed radial basis function neural network is better than back propagation neural network.
Mayorga, René V; Carrera, Jonathan
2007-06-01
This Paper presents an efficient approach for the fast computation of inverse continuous time variant functions with the proper use of Radial Basis Function Networks (RBFNs). The approach is based on implementing RBFNs for computing inverse continuous time variant functions via an overall damped least squares solution that includes a novel null space vector for singularities prevention. The singularities avoidance null space vector is derived from developing a sufficiency condition for singularities prevention that conduces to establish some characterizing matrices and an associated performance index.
Chen, S; Mulgrew, B; Grant, P M
1993-01-01
The application of a radial basis function network to digital communications channel equalization is examined. It is shown that the radial basis function network has an identical structure to the optimal Bayesian symbol-decision equalizer solution and, therefore, can be employed to implement the Bayesian equalizer. The training of a radial basis function network to realize the Bayesian equalization solution can be achieved efficiently using a simple and robust supervised clustering algorithm. During data transmission a decision-directed version of the clustering algorithm enables the radial basis function network to track a slowly time-varying environment. Moreover, the clustering scheme provides an automatic compensation for nonlinear channel and equipment distortion. Computer simulations are included to illustrate the analytical results.
Radial Basis Function Networks: Generalization in Over-realizable and Unrealizable Scenarios.
Saad, David; Freeman, Jason A. S.
1996-12-01
Learning and generalization in a two-layer radial basis function network, with fixed centres of the basis functions, is examined within a stochastic training paradigm. Employing a Bayesian approach, expressions for generalization error are derived under the assumption that the generating mechanism (teacher) for the training data is also a radial basis function network, but one for which the basis function centres and widths need not correspond to those of the student network. The effects of regularization, via a weight decay term, are examined. The cases in which the student has greater representational power than the teacher (over-realizable), and in which the teacher has greater power than the student (unrealizable) are studied. Dependence on knowing the centres of the teacher is eliminated by introducing a single degree-of-confidence parameter. Finally, simulations are performed which validate the analytic results. Copyright 1996 Elsevier Science Ltd.
Satisfiability of logic programming based on radial basis function neural networks
Energy Technology Data Exchange (ETDEWEB)
Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)
2014-07-10
In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.
Satisfiability of logic programming based on radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Tilahun, Surafel Luleseged; Choon, Ong Hong
2014-07-01
In this paper, we propose a new technique to test the Satisfiability of propositional logic programming and quantified Boolean formula problem in radial basis function neural networks. For this purpose, we built radial basis function neural networks to represent the proportional logic which has exactly three variables in each clause. We used the Prey-predator algorithm to calculate the output weights of the neural networks, while the K-means clustering algorithm is used to determine the hidden parameters (the centers and the widths). Mean of the sum squared error function is used to measure the activity of the two algorithms. We applied the developed technique with the recurrent radial basis function neural networks to represent the quantified Boolean formulas. The new technique can be applied to solve many applications such as electronic circuits and NP-complete problems.
Fusion prediction based on the attribute clustering net-work and the radial basis function
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
A fusion prediction method is introduced on the basis of attribute clustering network and radial basis functions. An algorithm of quasi-self organization for developing the model for the fusion prediction is introduced. Some simulation results for chaotic time series are presented to show the performance of the method.
Directory of Open Access Journals (Sweden)
Yunfeng Wu
2014-01-01
Full Text Available This paper presents a novel adaptive linear and normalized combination (ALNC method that can be used to combine the component radial basis function networks (RBFNs to implement better function approximation and regression tasks. The optimization of the fusion weights is obtained by solving a constrained quadratic programming problem. According to the instantaneous errors generated by the component RBFNs, the ALNC is able to perform the selective ensemble of multiple leaners by adaptively adjusting the fusion weights from one instance to another. The results of the experiments on eight synthetic function approximation and six benchmark regression data sets show that the ALNC method can effectively help the ensemble system achieve a higher accuracy (measured in terms of mean-squared error and the better fidelity (characterized by normalized correlation coefficient of approximation, in relation to the popular simple average, weighted average, and the Bagging methods.
The Rate of Approximation of Gaussian Radial Basis Neural Networks in Continuous Function Space
Institute of Scientific and Technical Information of China (English)
Ting Fan XIE; Fei Long CAO
2013-01-01
There have been many studies on the dense theorem of approximation by radial basis feedforword neural networks,and some approximation problems by Gaussian radial basis feedforward neural networks (GRBFNs) in some special function space have also been investigated.This paper considers the approximation by the GRBFNs in continuous function space.It is proved that the rate of approximation by GRNFNs with nd neurons to any continuous function f defined on a compact subset K （C） Rd can be controlled by ω(f,n-1/2),where ω(f,t) is the modulus of continuity of the function f.
Ni, Shengqiao; Lv, Jiancheng; Cheng, Zhehao; Li, Mao
2015-01-01
This paper presents improvements to the conventional Topology Representing Network to build more appropriate topology relationships. Based on this improved Topology Representing Network, we propose a novel method for online dimensionality reduction that integrates the improved Topology Representing Network and Radial Basis Function Network. This method can find meaningful low-dimensional feature structures embedded in high-dimensional original data space, process nonlinear embedded manifolds, and map the new data online. Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network. Experiments illustrate the effectiveness of the proposed method.
Directory of Open Access Journals (Sweden)
Shengqiao Ni
Full Text Available This paper presents improvements to the conventional Topology Representing Network to build more appropriate topology relationships. Based on this improved Topology Representing Network, we propose a novel method for online dimensionality reduction that integrates the improved Topology Representing Network and Radial Basis Function Network. This method can find meaningful low-dimensional feature structures embedded in high-dimensional original data space, process nonlinear embedded manifolds, and map the new data online. Furthermore, this method can deal with large datasets for the benefit of improved Topology Representing Network. Experiments illustrate the effectiveness of the proposed method.
Sensitivity analysis applied to the construction of radial basis function networks.
Shi, D; Yeung, D S; Gao, J
2005-09-01
Conventionally, a radial basis function (RBF) network is constructed by obtaining cluster centers of basis function by maximum likelihood learning. This paper proposes a novel learning algorithm for the construction of radial basis function using sensitivity analysis. In training, the number of hidden neurons and the centers of their radial basis functions are determined by the maximization of the output's sensitivity to the training data. In classification, the minimal number of such hidden neurons with the maximal sensitivity will be the most generalizable to unknown data. Our experimental results show that our proposed sensitivity-based RBF classifier outperforms the conventional RBFs and is as accurate as support vector machine (SVM). Hence, sensitivity analysis is expected to be a new alternative way to the construction of RBF networks.
Directory of Open Access Journals (Sweden)
Mohammad Mehdi Mazarei
2012-01-01
Full Text Available This paper presents numerical solution of elliptic partial differential equations (Poisson's equation using a combination of logarithmic and multiquadric radial basis function networks. This method uses a special combination between logarithmic and multiquadric radial basis functions with a parameter r. Further, the condition number which arises in the process is discussed, and a comparison is made between them with our earlier studies and previously known ones. It is shown that the system is stable.
Selver, M Alper; Güzeliş, Cüneyt
2009-01-01
As being a tool that assigns optical parameters used in interactive visualization, Transfer Functions (TF) have important effects on the quality of volume rendered medical images. Unfortunately, finding accurate TFs is a tedious and time consuming task because of the trade off between using extensive search spaces and fulfilling the physician's expectations with interactive data exploration tools and interfaces. By addressing this problem, we introduce a semi-automatic method for initial generation of TFs. The proposed method uses a Self Generating Hierarchical Radial Basis Function Network to determine the lobes of a Volume Histogram Stack (VHS) which is introduced as a new domain by aligning the histograms of slices of a image series. The new self generating hierarchical design strategy allows the recognition of suppressed lobes corresponding to suppressed tissues and representation of the overlapping regions which are parts of the lobes but can not be represented by the Gaussian bases in VHS. Moreover, approximation with a minimum set of basis functions provides the possibility of selecting and adjusting suitable units to optimize the TF. Applications on different CT and MR data sets show enhanced rendering quality and reduced optimization time in abdominal studies.
Radical pruning: a method to construct skeleton radial basis function networks.
Augusteijn, M F; Shaw, K A
2000-04-01
Trained radial basis function networks are well-suited for use in extracting rules and explanations because they contain a set of locally tuned units. However, for rule extraction to be useful, these networks must first be pruned to eliminate unnecessary weights. The pruning algorithm cannot search the network exhaustively because of the computational effort involved. It is shown that using multiple pruning methods with smart ordering of the pruning candidates, the number of weights in a radial basis function network can be reduced to a small fraction of the original number. The complexity of the pruning algorithm is quadratic (instead of exponential) in the number of network weights. Pruning performance is shown using a variety of benchmark problems from the University of California, Irvine machine learning database.
Radial basis function neural network for power system load-flow
Energy Technology Data Exchange (ETDEWEB)
Karami, A.; Mohammadi, M.S. [Faculty of Engineering, The University of Guilan, P.O. Box 41635-3756, Rasht (Iran)
2008-01-15
This paper presents a method for solving the load-flow problem of the electric power systems using radial basis function (RBF) neural network with a fast hybrid training method. The main idea is that some operating conditions (values) are needed to solve the set of non-linear algebraic equations of load-flow by employing an iterative numerical technique. Therefore, we may view the outputs of a load-flow program as functions of the operating conditions. Indeed, we are faced with a function approximation problem and this can be done by an RBF neural network. The proposed approach has been successfully applied to the 10-machine and 39-bus New England test system. In addition, this method has been compared with that of a multi-layer perceptron (MLP) neural network model. The simulation results show that the RBF neural network is a simpler method to implement and requires less training time to converge than the MLP neural network. (author)
Chen, S; Wu, Y; Luk, B L
1999-01-01
The paper presents a two-level learning method for radial basis function (RBF) networks. A regularized orthogonal least squares (ROLS) algorithm is employed at the lower level to construct RBF networks while the two key learning parameters, the regularization parameter and the RBF width, are optimized using a genetic algorithm (GA) at the upper level. Nonlinear time series modeling and prediction is used as an example to demonstrate the effectiveness of this hierarchical learning approach.
Rotation Invariant Face Detection Using Wavelet, PCA and Radial Basis Function Networks
Kamruzzaman, S M; Islam, Md Saiful; Haque, Md Emdadul; Alam, Mohammad Shamsul
2010-01-01
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 effective procedure to reduce the dimension of the input vectors using PCA. Radial Basis Function (RBF) neural network is then used as a function approximation network to detect where either the input image is contained a face or not and if there is a face exists then tell about its orientation. We will show how RBF can perform well then back-propagation algorithm and give some solution for better regularization of the RBF (GRNN) network. Compared with traditional RBF networks, the proposed network demonstrates better capability of approximation to underlying functions, faster learning speed, better size of network, and high ro...
Radial basis function neural networks with sequential learning MRAN and its applications
Sundararajan, N; Wei Lu Ying
1999-01-01
This book presents in detail the newly developed sequential learning algorithm for radial basis function neural networks, which realizes a minimal network. This algorithm, created by the authors, is referred to as Minimal Resource Allocation Networks (MRAN). The book describes the application of MRAN in different areas, including pattern recognition, time series prediction, system identification, control, communication and signal processing. Benchmark problems from these areas have been studied, and MRAN is compared with other algorithms. In order to make the book self-contained, a review of t
Design of Radial Basis Function Neural Networks for Software Effort Estimation
Directory of Open Access Journals (Sweden)
Ali Idri
2010-07-01
Full Text Available In spite of the several software effort estimation models developed over the last 30 years, providing accurate estimates of the software project under development is still unachievable goal. Therefore, many researchers are working on the development of new models and the improvement of the existing ones using artificial intelligence techniques such as: case-based reasoning, decision trees, genetic algorithms and neural networks. This paper is devoted to the design of Radial Basis Function Networks for software cost estimation. It shows the impact of the RBFN network structure, especially the number of neurons in the hidden layer and the widths of the basis function, on the accuracy of the produced estimates measured by means of MMRE and Pred indicators. The empirical study uses two different software project datasets namely, artificial COCOMO'81 and Tukutuku datasets.
Dual-orthogonal radial basis function networks for nonlinear time series prediction.
Hong, X; Billings, Steve A.
1998-04-01
A new structure of Radial Basis Function (RBF) neural network called the Dual-orthogonal RBF Network (DRBF) is introduced for nonlinear time series prediction. The hidden nodes of a conventional RBF network compare the Euclidean distance between the network input vector and the centres, and the node responses are radially symmetrical. But in time series prediction where the system input vectors are lagged system outputs, which are usually highly correlated, the Euclidean distance measure may not be appropriate. The DRBF network modifies the distance metric by introducing a classification function which is based on the estimation data set. Training the DRBF networks consists of two stages. Learning the classification related basis functions and the important input nodes, followed by selecting the regressors and learning the weights of the hidden nodes. In both cases, a forward Orthogonal Least Squares (OLS) selection procedure is applied, initially to select the important input nodes and then to select the important centres. Simulation results of single-step and multi-step ahead predictions over a test data set are included to demonstrate the effectiveness of the new approach.
Liu, Jinkun
2013-01-01
Radial Basis Function (RBF) Neural Network Control for Mechanical Systems is motivated by the need for systematic design approaches to stable adaptive control system design using neural network approximation-based techniques. The main objectives of the book are to introduce the concrete design methods and MATLAB simulation of stable adaptive RBF neural control strategies. In this book, a broad range of implementable neural network control design methods for mechanical systems are presented, such as robot manipulators, inverted pendulums, single link flexible joint robots, motors, etc. Advanced neural network controller design methods and their stability analysis are explored. The book provides readers with the fundamentals of neural network control system design. This book is intended for the researchers in the fields of neural adaptive control, mechanical systems, Matlab simulation, engineering design, robotics and automation. Jinkun Liu is a professor at Beijing University of Aeronautics and Astronauti...
Upset Prediction in Friction Welding Using Radial Basis Function Neural Network
Directory of Open Access Journals (Sweden)
Wei Liu
2013-01-01
Full Text Available This paper addresses the upset prediction problem of friction welded joints. Based on finite element simulations of inertia friction welding (IFW, a radial basis function (RBF neural network was developed initially to predict the final upset for a number of welding parameters. The predicted joint upset by the RBF neural network was compared to validated finite element simulations, producing an error of less than 8.16% which is reasonable. Furthermore, the effects of initial rotational speed and axial pressure on the upset were investigated in relation to energy conversion with the RBF neural network. The developed RBF neural network was also applied to linear friction welding (LFW and continuous drive friction welding (CDFW. The correlation coefficients of RBF prediction for LFW and CDFW were 0.963 and 0.998, respectively, which further suggest that an RBF neural network is an effective method for upset prediction of friction welded joints.
[Hyperspectral remote sensing image classification based on radical basis function neural network].
Tan, Kun; Du, Pei-jun
2008-09-01
Based on the radial basis function neural network (RBFNN) theory and the specialty of hyperspectral remote sensing data, the effective feature extraction model was designed, and those extracted features were connected to the input layer of RBFNN, finally the classifier based on radial basis function neural network was constructed. The hyperspectral image with 64 bands of OMIS II made by Chinese was experimented, and the case study area was zhongguancun in Beijing. Minimum noise fraction (MNF) was conducted, and the former 20 components were extracted for further processing. The original data (20 dimension) of extraction by MNF, the texture transformation data (20 dimension) extracted from the former 20 components after MNF, and the principal component analysis data (20 dimension) of extraction were combined to 60 dimension. For classification by RBFNN, the sizes of training samples were less than 6.13% of the whole image. That classifier has a simple structure and fast convergence capacity, and can be easily trained. The classification precision of radial basis function neural network classifier is up to 69.27% in contrast with the 51.20% of back propagation neural network (BPNN) and 40. 88% of traditional minimum distance classification (MDC), so RBFNN classifier performs better than the other three classifiers. It proves that RBFNN is of validity in hyperspectral remote sensing classification.
Computing single step operators of logic programming in radial basis function neural networks
Energy Technology Data Exchange (ETDEWEB)
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)
2014-07-10
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.
Computing single step operators of logic programming in radial basis function neural networks
International Nuclear Information System (INIS)
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (Tp:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks
Computing single step operators of logic programming in radial basis function neural networks
Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong
2014-07-01
Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (Tp:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.
Gradient radial basis function networks for nonlinear and nonstationary time series prediction.
Chng, E S; Chen, S; Mulgrew, B
1996-01-01
We present a method of modifying 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 response, however, is highly sensitive to changes in the level and trend of the time series. To counter these effects, the hidden node's function is modified to one which detects and reacts to the gradient of the series. We call this new network the gradient RBF (GRBF) model. Single and multistep predictive performance for the Mackey-Glass chaotic time series were evaluated using the classical RBF and GRBF models. The simulation results for the series without and with a tine-varying mean confirm the superior performance of the GRBF predictor over the RBF predictor.
Institute of Scientific and Technical Information of China (English)
ZHAO Min; CUI Wei-cheng
2007-01-01
Improving the efficiency of ship optimization is crucial for modern ship design. Compared with traditional methods, multidisciplinary design optimization (MDO) is a more promising approach. For this reason, Collaborative Optimization (CO) is discussed and analyzed in this paper. As one of the most frequently applied MDO methods, CO promotes autonomy of disciplines while providing a coordinating mechanism guaranteeing progress toward an optimum and maintaining interdisciplinary compatibility. However, there are some difficulties in applying the conventional CO method, such as difficulties in choosing an initial point and tremendous computational requirements. For the purpose of overcoming these problems, optimal Latin hypercube design and Radial basis function network were applied to CO. Optimal Latin hypercube design is a modified Latin Hypercube design. Radial basis function network approximates the optimization model, and is updated during the optimization process to improve accuracy. It is shown by examples that the computing efficiency and robustness of this CO method are higher than with the conventional CO method.
Petković, Dalibor; Gocic, Milan; Shamshirband, Shahaboddin; Qasem, Sultan Noman; Trajkovic, Slavisa
2016-08-01
Accurate estimation of the reference evapotranspiration (ET0) is important for the water resource planning and scheduling of irrigation systems. For this purpose, the radial basis function network with particle swarm optimization (RBFN-PSO) and radial basis function network with back propagation (RBFN-BP) were used in this investigation. The FAO-56 Penman-Monteith equation was used as reference equation to estimate ET0 for Serbia during the period of 1980-2010. The obtained simulation results confirmed the proposed models and were analyzed using the root mean-square error (RMSE), the mean absolute error (MAE), and the coefficient of determination ( R 2). The analysis showed that the RBFN-PSO had better statistical characteristics than RBFN-BP and can be helpful for the ET0 estimation.
Image interpolation for progressive transmission by using radial basis function networks.
Sigitani, T; Iiguni, Y; Maeda, H
1999-01-01
This paper investigates the application of a radial basis function network (RBFN) to a hierarchical image coding for progressive transmission. The RBFN is then used to generate an interpolated image from the subsampled version. An efficient method of computing the network parameters is developed for reduction in computational and memory requirements. The coding method does not suffer from problems of blocking effect and can produce the coarsest image quickly. Quantization error effects introduced at one stage are considered in decoding images at the following stages, thus allowing lossless progressive transmission.
Radial basis function network learns ceramic processing and predicts related strength and density
Energy Technology Data Exchange (ETDEWEB)
Cios, K.J.; Baaklini, G.Y.; Vary, A.; Tjia, R.E.
1993-05-01
Radial basis function (RBF) neural networks were trained using the data from 273 Si3N4 modulus of rupture (MOR) bars which were tested at room temperature and 135 MOR bars which were tested at 1370 C. Milling time, sintering time, and sintering gas pressure were the processing parameters used as the input features. Flexural strength and density were the outputs by which the RBF networks were assessed. The 'nodes-at-data-points' method was used to set the hidden layer centers and output layer training used the gradient descent method. The RBF network predicted strength with an average error of less than 12 percent and density with an average error of less than 2 percent. Further, the RBF network demonstrated a potential for optimizing and accelerating the development and processing of ceramic materials.
Radial basis function network learns ceramic processing and predicts related strength and density
Energy Technology Data Exchange (ETDEWEB)
Cios, K.J.; Baaklini, G.Y.; Vary, A. (NASA Lewis Research Center, Cleveland, OH (United States)); Tjia, R.E. (Univ. of Toledo, OH (United States))
1994-07-01
Radial basis function (RBF) neural networks were trained using the data from 273 Si[sub 3]N[sub 4] modulus of rupture (MOR) bars that were tested at room temperature and 135 MOR bars that were tested at 1,370 C. Milling time, sintering time, and sintering gas pressure were the processing parameters used s the input features. Flexural strength and density were the outputs by which the RBF networks were assessed. The nodes at data points'' method was used to set the hidden layer centers and output layer training used the gradient descent method. The RBF network predicted strength with an average error of less than 12% and density with an average error of less than 2%. Further, the RBF network demonstrated a potential for optimizing and accelerating the development and processing of emerging ceramic materials.
Radial Basis Function Neural Network Based Super-Resolution Restoration for an Underspled Image
Institute of Scientific and Technical Information of China (English)
苏秉华; 金伟其; 牛丽红
2004-01-01
To achieve restoration of high frequency information for an underspled and degraded low-resolution image, a nonlinear and real-time processing method-the radial basis function (RBF) neural network based super-resolution method of restoration is proposed. The RBF network configuration and processing method is suitable for a high resolution restoration from an underspled low-resolution image. The soft-competition learning scheme based on the k-means algorithm is used, and can achieve higher mapping approximation accuracy without increase in the network size. Experiments showed that the proposed algorithm can achieve a super-resolution restored image from an underspled and degraded low-resolution image, and requires a shorter training time when compared with the multiplayer perception (MLP) network.
International Nuclear Information System (INIS)
This paper introduces a novel algorithm for determining the structure of a radial basis function (RBF) network (the number of hidden units) while it is used for dynamic modeling of chaotic time series. It can be seen that the hidden units in the RBF network can form hyperplanes to partition the input space into various regions in each of which it is possible to approximate the dynamics with a basis function. The number of regions corresponds to the number of hidden units. The basic idea of the proposed algorithm is to partition the input space by fractal scaling of the chaotic time series being modeled. By fractal scaling process, the number of basis functions (hidden units) as well as the number of input variables can be specified. Accordingly, the network topology is efficiently determined based on the complexity of the underlying dynamics as reflected in the observed time series. The feasibility of the proposed scheme is examined through dynamic modeling of the well-known chaotic time series. The results show that the new method can improve the predictability of chaotic time series with a suitable number of hidden units compared to that of reported in the literature
D'Souza, Adora M.; Abidin, Anas Zainul; Nagarajan, Mahesh B.; Wismüller, Axel
2016-03-01
We investigate the applicability of a computational framework, called mutual connectivity analysis (MCA), for directed functional connectivity analysis in both synthetic and resting-state functional MRI data. This framework comprises of first evaluating non-linear cross-predictability between every pair of time series prior to recovering the underlying network structure using community detection algorithms. We obtain the non-linear cross-prediction score between time series using Generalized Radial Basis Functions (GRBF) neural networks. These cross-prediction scores characterize the underlying functionally connected networks within the resting brain, which can be extracted using non-metric clustering approaches, such as the Louvain method. We first test our approach on synthetic models with known directional influence and network structure. Our method is able to capture the directional relationships between time series (with an area under the ROC curve = 0.92 +/- 0.037) as well as the underlying network structure (Rand index = 0.87 +/- 0.063) with high accuracy. Furthermore, we test this method for network recovery on resting-state fMRI data, where results are compared to the motor cortex network recovered from a motor stimulation sequence, resulting in a strong agreement between the two (Dice coefficient = 0.45). We conclude that our MCA approach is effective in analyzing non-linear directed functional connectivity and in revealing underlying functional network structure in complex systems.
An Adaptive Identification and Control Scheme Using Radial Basis Function Networks
Institute of Scientific and Technical Information of China (English)
无
1999-01-01
In this paper, adaptive identification and control of nonlinear dynamical systems are investigated using radial basis function networks (RBF). Firstly, a novel approach to train the RBF is introduced, which employs an adaptive fuzzy generalized learning vector quantization (AFGLVQ) technique and recursive least squares algorithm with variable forgetting factor (VRLS). The AFGLVQ adjusts the centers of the RBF while the VRLS updates the connection weights of the network. The identification algorithm has the properties of rapid convergence and persistent adaptability that make it suitable for real-time control. Secondly, on the basis of the one-step ahead RBF predictor, the control law is optimized iteratively through a numerical stable Davidon's least squares-based (SDLS) minimization approach. Four nonlinear examples are simulated to demonstrate the effectiveness of the identification and control algorithms.
Prediction Study on PCI Failure of Reactor Fuel Based on a Radial Basis Function Neural Network
Directory of Open Access Journals (Sweden)
Xinyu Wei
2016-01-01
Full Text Available Pellet-clad interaction (PCI is one of the major issues in fuel rod design and reactor core operation in water cooled reactors. The prediction of fuel rod failure by PCI is studied in this paper by the method of radial basis function neural network (RBFNN. The neural network is built through the analysis of the existing experimental data. It is concluded that it is a suitable way to reduce the calculation complexity. A self-organized RBFNN is used in our study, which can vary its structure dynamically in order to maintain the prediction accuracy. For the purpose of the appropriate network complexity and overall computational efficiency, the hidden neurons in the RBFNN can be changed online based on the neuron activity and mutual information. The presented method is tested by the experimental data from the reference, and the results demonstrate its effectiveness.
Construction of tunable radial basis function networks using orthogonal forward selection.
Chen, Sheng; Hong, Xia; Luk, Bing L; Harris, Chris J
2009-04-01
An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) criteria is proposed for the construction of radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines an RBF node, namely, its center vector and diagonal covariance matrix, by minimizing the LOO statistics. For regression application, the LOO criterion is chosen to be the LOO mean-square error, while the LOO misclassification rate is adopted in two-class classification application. This OFS-LOO algorithm is computationally efficient, and it is capable of constructing parsimonious RBF networks that generalize well. Moreover, the proposed algorithm is fully automatic, and the user does not need to specify a termination criterion for the construction process. The effectiveness of the proposed RBF network construction procedure is demonstrated using examples taken from both regression and classification applications.
Radial Basis Function Networks Applied in Bacterial Classification Based on MALDI-TOF-MS
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
The radial basis function networks were applied to bacterial classification based on the matrix-assisted laser desorption/ionization time-of-flight mass spectrometric (MALDI-TOF-MS) data. The classification of bacteria cultured at different time was discussed and the effect of the network parameters on the classification was investigated. The cross-validation method was used to test the trained networks. The correctness of the classification of different bacteria investigated changes in a wide range from 61.5% to 92.8%. Owing to the complexity of biological effects in bacterial growth, the more rigid control of bacterial culture conditions seems to be a critical factor for improving the rate of correctness for bacterial classification.
Ma, Shu-min; Liu, Si-dong; Zhang, Zhuo-yong; Fan, Guo-qiang
2005-06-01
The Fourier transform infrared spectrometry (FTIRS) and radial basis function neural network (RBF-NN) have been applied to develop classification models for identifying official and unofficial rhubarb samples. The original data were compressed from 775 variables to 49 variables by using wavelet transformation method. The compressed spectra with reduced variables maintain the characteristics of the IR spectra and speed up the network training process. The effects of network parameters including error goal and spread constant, were investigated. The rate of correct classification is up to 97.78% at optimized conditions. Results show that the combination of IRS and ANN can be used as fast and convenient tool for identification of Chinese herbal samples.
Shankar, Praveen
The performance of nonlinear control algorithms such as feedback linearization and dynamic inversion is heavily dependent on the fidelity of the dynamic model being inverted. Incomplete or incorrect knowledge of the dynamics results in reduced performance and may lead to instability. Augmenting the baseline controller with approximators which utilize a parametrization structure that is adapted online reduces the effect of this error between the design model and actual dynamics. However, currently existing parameterizations employ a fixed set of basis functions that do not guarantee arbitrary tracking error performance. To address this problem, we develop a self-organizing parametrization structure that is proven to be stable and can guarantee arbitrary tracking error performance. The training algorithm to grow the network and adapt the parameters is derived from Lyapunov theory. In addition to growing the network of basis functions, a pruning strategy is incorporated to keep the size of the network as small as possible. This algorithm is implemented on a high performance flight vehicle such as F-15 military aircraft. The baseline dynamic inversion controller is augmented with a Self-Organizing Radial Basis Function Network (SORBFN) to minimize the effect of the inversion error which may occur due to imperfect modeling, approximate inversion or sudden changes in aircraft dynamics. The dynamic inversion controller is simulated for different situations including control surface failures, modeling errors and external disturbances with and without the adaptive network. A performance measure of maximum tracking error is specified for both the controllers a priori. Excellent tracking error minimization to a pre-specified level using the adaptive approximation based controller was achieved while the baseline dynamic inversion controller failed to meet this performance specification. The performance of the SORBFN based controller is also compared to a fixed RBF network
Directory of Open Access Journals (Sweden)
Dongliang Guo
2014-01-01
Full Text Available Indoor localization technique has received much attention in recent years. Many techniques have been developed to solve the problem. Among the recent proposed methods, radio frequency identification (RFID indoor localization technology has the advantages of low-cost, noncontact, non-line-of-sight, and high precision. This paper proposed two radial basis function (RBF neural network based indoor localization methods. The RBF neural networks are trained to learn the mapping relationship between received signal strength indication values and position of objects. Traditional method used the received signal strength directly as the input of neural network; we added another input channel by taking the difference of the received signal strength, thus improving the reliability and precision of positioning. Fuzzy clustering is used to determine the center of radial basis function. In order to reduce the impact of signal fading due to non-line-of-sight and multipath transmission in indoor environment, we improved the Gaussian filter to process received signal strength values. The experimental results show that the proposed method outperforms the existing methods as well as improves the reliability and precision of the RFID indoor positioning system.
Radial basis function networks with linear interval regression weights for symbolic interval data.
Su, Shun-Feng; Chuang, Chen-Chia; Tao, C W; Jeng, Jin-Tsong; Hsiao, Chih-Ching
2012-02-01
This paper introduces a new structure of radial basis function networks (RBFNs) that can successfully model symbolic interval-valued data. In the proposed structure, to handle symbolic interval data, the Gaussian functions required in the RBFNs are modified to consider interval distance measure, and the synaptic weights of the RBFNs are replaced by linear interval regression weights. In the linear interval regression weights, the lower and upper bounds of the interval-valued data as well as the center and range of the interval-valued data are considered. In addition, in the proposed approach, two stages of learning mechanisms are proposed. In stage 1, an initial structure (i.e., the number of hidden nodes and the adjustable parameters of radial basis functions) of the proposed structure is obtained by the interval competitive agglomeration clustering algorithm. In stage 2, a gradient-descent kind of learning algorithm is applied to fine-tune the parameters of the radial basis function and the coefficients of the linear interval regression weights. Various experiments are conducted, and the average behavior of the root mean square error and the square of the correlation coefficient in the framework of a Monte Carlo experiment are considered as the performance index. The results clearly show the effectiveness of the proposed structure.
Gonzalez, J; Rojas, I; Ortega, J; Pomares, H; Fernandez, F J; Diaz, A F
2003-01-01
This paper presents a multiobjective evolutionary algorithm to optimize radial basis function neural networks (RBFNNs) in order to approach target functions from a set of input-output pairs. The procedure allows the application of heuristics to improve the solution of the problem at hand by including some new genetic operators in the evolutionary process. These new operators are based on two well-known matrix transformations: singular value decomposition (SVD) and orthogonal least squares (OLS), which have been used to define new mutation operators that produce local or global modifications in the radial basis functions (RBFs) of the networks (the individuals in the population in the evolutionary procedure). After analyzing the efficiency of the different operators, we have shown that the global mutation operators yield an improved procedure to adjust the parameters of the RBFNNs.
Zhang, Zhuoyong; Wang, Dan; Harrington, Peter de B; Voorhees, Kent J; Rees, Jon
2004-06-17
Forward selection improved radial basis function (RBF) network was applied to bacterial classification based on the data obtained by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). The classification of each bacterium cultured at different time was discussed and the effect of parameters of the RBF network was investigated. The new method involves forward selection to prevent overfitting and generalized cross-validation (GCV) was used as model selection criterion (MSC). The original data was compressed by using wavelet transformation to speed up the network training and reduce the number of variables of the original MS data. The data was normalized prior training and testing a network to define the area the neural network to be trained in, accelerate the training rate, and reduce the range the parameters to be selected in. The one-out-of-n method was used to split the data set of p samples into a training set of size p-1 and a test set of size 1. With the improved method, the classification correctness for the five bacteria discussed in the present paper are 87.5, 69.2, 80, 92.3, and 92.8%, respectively.
Sun, Gang; Hoff, Steven J; Zelle, Brian C; Nelson, Minda A
2008-12-01
It is vital to forecast gas and particle matter concentrations and emission rates (GPCER) from livestock production facilities to assess the impact of airborne pollutants on human health, ecological environment, and global warming. Modeling source air quality is a complex process because of abundant nonlinear interactions between GPCER and other factors. The objective of this study was to introduce statistical methods and radial basis function (RBF) neural network to predict daily source air quality in Iowa swine deep-pit finishing buildings. The results show that four variables (outdoor and indoor temperature, animal units, and ventilation rates) were identified as relative important model inputs using statistical methods. It can be further demonstrated that only two factors, the environment factor and the animal factor, were capable of explaining more than 94% of the total variability after performing principal component analysis. The introduction of fewer uncorrelated variables to the neural network would result in the reduction of the model structure complexity, minimize computation cost, and eliminate model overfitting problems. The obtained results of RBF network prediction were in good agreement with the actual measurements, with values of the correlation coefficient between 0.741 and 0.995 and very low values of systemic performance indexes for all the models. The good results indicated the RBF network could be trained to model these highly nonlinear relationships. Thus, the RBF neural network technology combined with multivariate statistical methods is a promising tool for air pollutant emissions modeling.
Circular antenna array pattern analysis using radial basis function neural network
International Nuclear Information System (INIS)
A method is proposed to design circular antenna array for the given gain and beam width using Artificial Neural Networks. In optimizing circular arrays, the parameters to be controlled are excitation of the elements, their separation, lengths and the circle radius. This paper deals about finding the parameters of radiation pattern of given uniform circular antenna array. Initially, the network is trained with a set of input-output data pairs. The trained network is used for testing. The training data set is generated from MATLAB simulation with number of elements N=5, 10, 15 and 20 elements of uniform circular array, respectively, distributed over a given circle, assuming 20 training cases. The number of input nodes, hidden nodes and output nodes are 20, 20 and 1, respectively. Predicted values of the neural network are compared with those of MATLAB simulation results and are found to be in agreement. This work establishes the application of Radial Basis Function Neural Network (RBFNN) for circular array pattern optimization. RBFNN is able to predict the output values with 97% of accuracy. This work proves that RBFNN can be used for circular antenna array design.
Numerical solution of differential equations using multiquadric radial basis functions networks.
Mai-Duy, N; Tran-Cong, T
2001-03-01
This paper presents mesh-free procedures for solving linear differential equations (ODEs and elliptic PDEs) based on multiquadric (MQ) radial basis function networks (RBFNs). Based on our study of approximation of function and its derivatives using RBFNs that was reported in an earlier paper (Mai-Duy, N. & Tran-Cong, T. (1999). Approximation of function and its derivatives using radial basis function networks. Neural networks, submitted), new RBFN approximation procedures are developed in this paper for solving DEs, which can also be classified into two types: a direct (DRBFN) and an indirect (IRBFN) RBFN procedure. In the present procedures, the width of the RBFs is the only adjustable parameter according to a(i) = betad(i), where d(i) is the distance from the ith centre to the nearest centre. The IRBFN method is more accurate than the DRBFN one and experience so far shows that beta can be chosen in the range 7 < or = beta 10 for the former. Different combinations of RBF centres and collocation points (uniformly and randomly distributed) are tested on both regularly and irregularly shaped domains. The results for a 1D Poisson's equation show that the DRBFN and the IRBFN procedures achieve a norm of error of at least O(1.0 x 10(-4)) and O(1.0 x 10(-8)), respectively, with a centre density of 50. Similarly, the results for a 2D Poisson's equation show that the DRBFN and the IRBFN procedures achieve a norm of error of at least O(1.0 x 10(-3)) and O(1.0 x10(-6)) respectively, with a centre density of 12 X 12.
Research on motion compensation method based on neural network of radial basis function
Institute of Scientific and Technical Information of China (English)
Zuo Yunbo
2014-01-01
The machining precision not only depends on accurate mechanical structure but also depends on motion compensation method. If manufacturing precision of mechanical structure cannot be improved, the motion compensation is a reasonable way to improve motion precision. A motion compensation method based on neural network of radial basis function (RBF) was presented in this paper. It utilized the infinite approximation advantage of RBF neural network to fit the motion error curve. The best hidden neural quantity was optimized by training the motion error data and calculating the total sum of squares. The best curve coefficient matrix was got and used to calculate motion compensation values. The experiments showed that the motion errors could be reduced obviously by utilizing the method in this paper.
Institute of Scientific and Technical Information of China (English)
Jun Wang; Guoqing Chen; Tuo Zhu; Shumei Gao; Bailin Wei; Linna Bi
2009-01-01
@@ The fluorescence spectra of synthetic food dyes of sunset yellow and tartrazine are analyzed.The fluorescence peak wavelengths of sunset yellow and tartrazine are 576 and 569 nm, respectively, while the fluorescence spectra widths are 480-750 and 500-750 nm induced by ultraviolet light between 310-400 nm.The fluorescence spectra of sunset yellow overlap heavily with those of tartrazine, so it is diffic ult to distinguish them.Based on the principle of radial basis function neural network, a neural network is obtained from the training of the 14 groups of experimental data.The results show that the species of sunset yellow and tartrazine could be recognized accurately.This method has potential applications in other synthetic food dyes detection and food safety inspection.
On-line Cutting Quality Recognition in Milling Using a Radical Basis Function Neural Network
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Tool wear, chatter vibration, chip breaking and built-up edge are main phenomena to be monitored in modern manufacturing processes, which are considered as important factors to the quality of products.They are closely related to the cutting parameters, which are to be selected in manufacturing process.However, it is very difficult to measure directly the cutting quality based on on-line monitoring.In this study, the relationship between the cutting parameters and cutting quality is analyzed.A Radical Basis Function (RBF) neural network based on-line quality recognition scheme is also presented, which monitors the level of surface roughness.The experimental results reveal that the RBF neural network has a high prediction success rate.
Institute of Scientific and Technical Information of China (English)
HUANG He; BAI Ji-cheng; LU Ze-sheng; GUO Yong-feng
2009-01-01
Milling electrical discharge machining (EDM) enables the machining of complex cavities using cylindrical or tubular electrodes. To ensure acceptable machining accuracy the process requires some methods of compensating for electrode wear. Due to the complexity and random nature of the process, existing methods of compensating for such wear usually involve off-line prediction. This paper discusses an innovative model of electrode wear prediction for milling EDM based upon a radial basis function (RBF) network. Data gained from an orthogonal experiment were used to provide training samples for the RBF network. The model established was used to forecast the electrode wear, making it possible to calculate the real-time tool wear in the milling EDM process and, to lay the foundations for dynamic compensation of the electrode wear on-line. This paper demonstrates that by using this model prediction errors can be controlled within 8%.
Filtered-X Radial Basis Function Neural Networks for Active Noise Control
Directory of Open Access Journals (Sweden)
Bambang Riyanto
2004-05-01
Full Text Available This paper presents active control of acoustic noise using radial basis function (RBF networks and its digital signal processor (DSP real-time implementation. The neural control system consists of two stages: first, identification (modeling of secondary path of the active noise control using RBF networks and its learning algorithm, and secondly neural control of primary path based on neural model obtained in the first stage. A tapped delay line is introduced in front of controller neural, and another tapped delay line is inserted between controller neural networks and model neural networks. A new algorithm referred to as Filtered X-RBF is proposed to account for secondary path effects of the control system arising in active noise control. The resulting algorithm turns out to be the filtered-X version of the standard RBF learning algorithm. We address centralized and decentralized controller configurations and their DSP implementation is carried out. Effectiveness of the neural controller is demonstrated by applying the algorithm to active noise control within a 3 dimension enclosure to generate quiet zones around error microphones. Results of the real-time experiments show that 10-23 dB noise attenuation is produced with moderate transient response.
DENSENESS OF RADIAL-BASIS FUNCTIONS IN L2（Rn） AND ITS APPLICATIONS IN NEURAL NETWORKS
Institute of Scientific and Technical Information of China (English)
CHENTIANPING; CHENHONG
1996-01-01
The authors discuss problems of approximation to functions in L2 (Rn)and operators from L2(Rn1)to L2(Rn2)by Radial-Basis Functions. The results obtained solve the parblem of capability of RBF neural networks,a basic problem in neural networks.
An improved method using radial basis function neural networks to speed up optimization algorithms
Bazan, M; Russenschuck, Stephan
2002-01-01
The paper presents a method using radial basis function (RBF) neural networks to speed up deterministic search algorithms used for the optimization of superconducting magnets for the LHC accelerator project at CERN. The optimization of the iron yoke of the main LHC dipoles requires a number of numerical field computations per trial solution as the field quality depends on the excitation and local iron saturation in the yoke. This results in computation times of about 30 min for each objective function evaluation (on DEC-Alpha 600 /333). In this paper, we present a method for constructing an RBF neural network for a local approximation of the objective function. The computational time required for such a construction is negligible compared to the deterministic function evaluation, and, thus, yields a speed-up of the overall search process. The effectiveness of this method is demonstrated by means of two- and three-parametric optimization examples. The achieved speed-up of the search routine is up to 30%. (12 r...
Voigt, M.; Lorenz, P.; Kruschke, T.; Osinski, R.; Ulbrich, U.; Leckebusch, G. C.
2012-04-01
Winterstorms and related gusts can cause extensive socio-economic damages. Knowledge about the occurrence and the small scale structure of such events may help to make regional estimations of storm losses. For a high spatial and temporal representation, the use of dynamical downscaling methods (RCM) is a cost-intensive and time-consuming option and therefore only applicable for a limited number of events. The current study explores a methodology to provide a statistical downscaling, which offers small scale structured gust fields from an extended large scale structured eventset. Radial-basis-function (RBF) networks in combination with bidirectional Kohonen (BDK) maps are used to generate the gustfields on a spatial resolution of 7 km from the 6-hourly mean sea level pressure field from ECMWF reanalysis data. BDK maps are a kind of neural network which handles supervised classification problems. In this study they are used to provide prototypes for the RBF network and give a first order approximation for the output data. A further interpolation is done by the RBF network. For the training process the 50 most extreme storm events over the North Atlantic area from 1957 to 2011 are used, which have been selected from ECMWF reanalysis datasets ERA40 and ERA-Interim by an objective wind based tracking algorithm. These events were downscaled dynamically by application of the DWD model chain GME → COSMO-EU. Different model parameters and their influence on the quality of the generated high-resolution gustfields are studied. It is shown that the statistical RBF network approach delivers reasonable results in modeling the regional gust fields for untrained events.
Chen, Jiajia; Zhao, Pan; Liang, Huawei; Mei, Tao
2014-01-01
The autonomous vehicle is an automated system equipped with features like environment perception, decision-making, motion planning, and control and execution technology. Navigating in an unstructured and complex environment is a huge challenge for autonomous vehicles, due to the irregular shape of road, the requirement of real-time planning, and the nonholonomic constraints of vehicle. This paper presents a motion planning method, based on the Radial Basis Function (RBF) neural network, to guide the autonomous vehicle in unstructured environments. The proposed algorithm extracts the drivable region from the perception grid map based on the global path, which is available in the road network. The sample points are randomly selected in the drivable region, and a gradient descent method is used to train the RBF network. The parameters of the motion-planning algorithm are verified through the simulation and experiment. It is observed that the proposed approach produces a flexible, smooth, and safe path that can fit any road shape. The method is implemented on autonomous vehicle and verified against many outdoor scenes; furthermore, a comparison of proposed method with the existing well-known Rapidly-exploring Random Tree (RRT) method is presented. The experimental results show that the proposed method is highly effective in planning the vehicle path and offers better motion quality. PMID:25237902
Design and Modeling of RF Power Amplifiers with Radial Basis Function Artificial Neural Networks
Directory of Open Access Journals (Sweden)
Ali Reza Zirak
2016-06-01
Full Text Available A radial basis function (RBF artificial neural network model for a designed high efficiency radio frequency class-F power amplifier (PA is presented in this paper. The presented amplifier is designed at 1.8 GHz operating frequency with 12 dB of gain and 36 dBm of 1dB output compression point. The obtained power added efficiency (PAE for the presented PA is 76% under 26 dBm input power. The proposed RBF model uses input and DC power of the PA as inputs variables and considers output power as the output variable. The presented RBF network models the designed class-F PA as a block, which could be applied in circuit design. The presented model could be used to model any RF power amplifier. The obtained results show a good agreement between real data and predicted values by RBF model. The results clearly show that the presented RBF network is more precise than multilayer perceptron (MLP model. According to the results, better than 84% and 92% improvement is achieved in MAE and RMSE, respectively.
Approximating Gaussian mixture model or radial basis function network with multilayer perceptron.
Patrikar, Ajay M
2013-07-01
Gaussian mixture models (GMMs) and multilayer perceptron (MLP) are both popular pattern classification techniques. This brief shows that a multilayer perceptron with quadratic inputs (MLPQ) can accurately approximate GMMs with diagonal covariance matrices. The mapping equations between the parameters of GMM and the weights of MLPQ are presented. A similar approach is applied to radial basis function networks (RBFNs) to show that RBFNs with Gaussian basis functions and Euclidean norm can be approximated accurately with MLPQ. The mapping equations between RBFN and MLPQ weights are presented. There are well-established training procedures for GMMs, such as the expectation maximization (EM) algorithm. The GMM parameters obtained by the EM algorithm can be used to generate a set of initial weights of MLPQ. Similarly, a trained RBFN can be used to generate a set of initial weights of MLPQ. MLPQ training can be continued further with gradient-descent based methods, which can lead to improvement in performance compared to the GMM or RBFN from which it is initialized. Thus, the MLPQ can always perform as well as or better than the GMM or RBFN.
Ryu, Duchwan
2013-03-01
The sea surface temperature (SST) is an important factor of the earth climate system. A deep understanding of SST is essential for climate monitoring and prediction. In general, SST follows a nonlinear pattern in both time and location and can be modeled by a dynamic system which changes with time and location. In this article, we propose a radial basis function network-based dynamic model which is able to catch the nonlinearity of the data and propose to use the dynamically weighted particle filter to estimate the parameters of the dynamic model. We analyze the SST observed in the Caribbean Islands area after a hurricane using the proposed dynamic model. Comparing to the traditional grid-based approach that requires a supercomputer due to its high computational demand, our approach requires much less CPU time and makes real-time forecasting of SST doable on a personal computer. Supplementary materials for this article are available online. © 2013 American Statistical Association.
Higher-order-statistics-based radial basis function networks for signal enhancement.
Lin, Bor-Shyh; Lin, Bor-Shing; Chong, Fok-Ching; Lai, Feipei
2007-05-01
In this paper, a higher-order-statistics (HOS)-based radial basis function (RBF) network for signal enhancement is introduced. In the proposed scheme, higher order cumulants of the reference signal were used as the input of HOS-based RBF. An HOS-based supervised learning algorithm, with mean square error obtained from higher order cumulants of the desired input and the system output as the learning criterion, was used to adapt weights. The motivation is that the HOS can effectively suppress Gaussian and symmetrically distributed non-Gaussian noise. The influence of a Gaussian noise on the input of HOS-based RBF and the HOS-based learning algorithm can be mitigated. Simulated results indicate that HOS-based RBF can provide better performance for signal enhancement under different noise levels, and its performance is insensitive to the selection of learning rates. Moreover, the efficiency of HOS-based RBF under the nonstationary Gaussian noise is stable.
Institute of Scientific and Technical Information of China (English)
李昕; 郑宇; 等
2002-01-01
The performance of speaker verification systems is often compromised under real-world environments.For example,variations in handset characteristics could cause severe performance degradation.This paper presents a novel method to overcome this problem by using a non-linear handset mapper.Under this method,a mapper is constructed by training an elliptical basis function network using distorted speech features as inputs and the corresponding clean features as the desired outputs.During feature recuperation,clean features are recovered by feeding the distorted features to the feature mapper.The recovered features are then presented to a speaker model as if they were derived from clean speech.Experimental evaluation based on 258 speakers of the TIMIT and NTIMIT corpuses suggest that the feature mappers improve the verification performance remarkably.
Radial Basis Function Neural Networks Based QSPR for the Prediction of log P
Institute of Scientific and Technical Information of China (English)
YAO,Xiao-Jun(姚小军); LIU,Man-Cang(刘满仓); ZHANG,Xiao-Yun(张晓昀); ZHANG,Rui-Sheng(张瑞生); HU,Zhi-De(胡之德); FAN,Bo-Tao(范波涛)
2002-01-01
Quantitative structure-property relatioonship (QSPR) method is used to study the correlation models between the structures of a set of diverse organic compounds and their log P. Molecular descriptors calculated from structure alone are used to describe the molecular structures. A subset of the calculated descriptors, selected using forward stepwise regression, is used in the QSPR models development. Multiple linear regression (MLR)and radial basis function neural networks (RBFNNs) are urilized to construct the linear and non-linear correlation model,respectively. The optimal QSPR model developedis based on a 7-17-1 RBFNNs architecture using seven calculated molecular descriptors. The root mean square errorsin predictions for the training, predicting and overall data sets are 0.284, 0.327 and 0.291 log P units, respectively.
Radial Basis Function Neural Networks Based QSPR for the Prediction of log P
Institute of Scientific and Technical Information of China (English)
姚小军; 范波涛; 等
2002-01-01
Quantitative structure-property relationship(QSPR) method is used to study the correlation models between the structures of a set of diverse organic compounds and their log P.Molecular descriptors calculated from strucure alone are used to describe the molecular structures.A subset of the calcualted descriptors,selected using forward stepwise regression,is used in the QSPR models development.Multiple linear regression (MLR) and radial basis function neural networks (RBFNNs) are utilied to construct the linear and non-linear correlation model,respectively,The optimal QSPR model developed is based on a 7-17-1 RBFNNs architecture using sever calculated molecular descriptors .The root mean square errors in predictions for the training,predicting and overall data sets are 0.284,0.327 and 0.291 log P units respectively.
Energy Technology Data Exchange (ETDEWEB)
Hancock, M.F. Jr. [Rollins College, Winter Park, FL (United States)
1995-12-31
The National Council on Compensation Insurance (NCCI) maintains a national data base of outcomes of workers` compensation claims. We consider whether a radial basis function network can predict the total dollar value of a claim based upon medical and demographic indicators (MDI`s). This work used data from 12,130 workers` compensation claims collected over a period of four years from the state of New Mexico. Two problems were addressed: (1) How well can the total incurred medical expense for all claims be predicted from available MDI`s? For individual claims? (2) How well can the duration of disability be predicted from available MDI`s? The available features intuitively correlated with total medical cost were selected, including type of injury, part of body injured, person`s age at time of injury, gender, marital status, etc. These features were statistically standardized and sorted by correlation with outcome valuation. Principal component analysis was applied. A radial basis function neural network was applied to the feature sets in both supervised and unsupervised training modes. For sets used in training, individual case valuations could consistently be predicted to within $1000 over 98% of the time. For these sets, it was possible to predict total medical expense for the training sets themselves to within 10%. When applied as blind tests against sets which were NOT part of the training data, the prediction was within 15% on the whole sets. Results on individual cases were very poor in only 30% of the cases were the predictions for the training sets within $1000 of their actual valuations. Single-factor analysis suggested that the presence of an attorney strongly decorrelated the data. A simple stratification was performed to remove cases involving attorneys and contested claims, and the procedures above repeated. Preliminary results based upon the very limited effort applied indicate that NCCI data support population estimates, but not single-point estimates.
Babu, G S; Suresh, S
2013-02-01
In this paper, we present a sequential projection-based metacognitive learning algorithm in a radial basis function network (PBL-McRBFN) for classification problems. The algorithm is inspired by human metacognitive learning principles and has two components: a cognitive component and a metacognitive component. The cognitive component is a single-hidden-layer radial basis function network with evolving architecture. The metacognitive component controls the learning process in the cognitive component by choosing the best learning strategy for the current sample and adapts the learning strategies by implementing self-regulation. In addition, sample overlapping conditions and past knowledge of the samples in the form of pseudosamples are used for proper initialization of new hidden neurons to minimize the misclassification. The parameter update strategy uses projection-based direct minimization of hinge loss error. The interaction of the cognitive component and the metacognitive component addresses the what-to-learn, when-to-learn, and how-to-learn human learning principles efficiently. The performance of the PBL-McRBFN is evaluated using a set of benchmark classification problems from the University of California Irvine machine learning repository. The statistical performance evaluation on these problems proves the superior performance of the PBL-McRBFN classifier over results reported in the literature. Also, we evaluate the performance of the proposed algorithm on a practical Alzheimer's disease detection problem. The performance results on open access series of imaging studies and Alzheimer's disease neuroimaging initiative datasets, which are obtained from different demographic regions, clearly show that PBL-McRBFN can handle a problem with change in distribution.
Institute of Scientific and Technical Information of China (English)
YANG Xiao-hua; HUANG Jing-feng; WANG Jian-wen; WANG Xiu-zhen; LIU Zhan-yu
2007-01-01
Hyperspectral reflectance (350-2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices (VIs) were used to predict the rice agronomic parameters including Leaf Area Index (LAI, m2 green leaf/m2 soil) and Green Leaf Chlorophyll Density (GLCD,mg chlorophyll/m2 soil) by the traditional regression models and Radial Basis Function Neural Network (RBF). RBF emerged as a variant of Artificial Neural Networks (ANNs) in the late 1980's. A large variety of training algorithms has been tested for training RBF networks. In this study, Original RBF (ORBF), Gradient Descent RBF (GDRBF), and Generalized Regression Neural Network (GRNN) were employed. Results showed that green waveband Normalized Difference Vegetation Index (NDVIgreen) and TCARI/OSAVI have the best prediction power for LAI by exponent model and ORBF respectively, and that TCARI/OSAVI has the best prediction power for GLCD by exponent model and GDRBF. The best performances of RBF are compared with the traditional models, showing that the relationship between VIs and agronomic variables are further improved when RBF is used.Compared with the best traditional models, ORBF using TCARI/OSAVI improves the prediction power for LAI by lowering the Root Mean Square Error (RMSE) for 0.1119, and GDRBF using TCARI/OSAVI improves the prediction power for GLCD by lowering the RMSE for 26.7853. It is concluded that RBF provides a useful exploratory and predictive tool when applied to the sensitive VIs.
Near and long-term load prediction using radial basis function networks
Energy Technology Data Exchange (ETDEWEB)
Hancock, M.F. [Rollins College, Winter Park, FL (United States)
1995-12-31
A number of researchers have investigated the application of multi-layer perceptrons (MLP`s), a variety of neural network, to the problem of short-term load forecasting for electric utilities (e.g., Rahman & Hazin, IEEE Trans. Power Systems, May 1993). {open_quotes}Short-term{close_quotes} in this context typically means {open_quotes}next day{close_quotes}. These forecasts have been based upon previous day actual loads and meteorological factors (e.g., max-min temperature, relative humidity). We describe the application of radial basis function networks (RBF`s) to the {open_quotes}long-term{close_quotes} (next year) load forecasting problem. The RBF network performs a two-stage classification based upon annual average loads and meteorological data. During stage 1, discrete classification is performed using radius-limited elements. During stage 2, a multi-layer perceptron may be applied. The quantized output is used to correct a prediction template. The stage 1 classifier is trained by maximizing an objective function (the {open_quotes}disambiguity{close_quotes}). The stage 2 MLP`s are trained by standard back-propagation. This work uses 12 months of hourly meteorological data, and the corresponding hourly load data for both commercial and residential feeders. At the current stage of development, the RBF machine can train on 20% of the weather/load data (selected by simple linear sampling), and estimate the hourly load for an entire year (8,760 data points) with 9.1% error (RMS, relative to daily peak load). (By comparison, monthly mean profiles perform at c. 12% error.) The best short-term load forecasters operate in the 2% error range. The current system is an engineering prototype, and development is continuing.
Ensembles of radial basis function networks for spectroscopic detection of cervical precancer
Tumer, K.; Ramanujam, N.; Ghosh, J.; Richards-Kortum, R.
1998-01-01
The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, noninvasively and quantitatively probes the biochemical and morphological changes that occur in precancerous tissue. A multivariate statistical algorithm was used to extract clinically useful information from tissue spectra acquired from 361 cervical sites from 95 patients at 337-, 380-, and 460-nm excitation wavelengths. The multivariate statistical analysis was also employed to reduce the number of fluorescence excitation-emission wavelength pairs required to discriminate healthy tissue samples from precancerous tissue samples. The use of connectionist methods such as multilayered perceptrons, radial basis function (RBF) networks, and ensembles of such networks was investigated. RBF ensemble algorithms based on fluorescence spectra potentially provide automated and near real-time implementation of precancer detection in the hands of nonexperts. The results are more reliable, direct, and accurate than those achieved by either human experts or multivariate statistical algorithms.
Yoo, Sung-Hoon; Oh, Sung-Kwun; Pedrycz, Witold
2015-09-01
In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Component Analysis (PCA) algorithm. At this step, we use a CCD (Charge Coupled Device) camera to acquire a facial image by using AdaBoost and then Histogram Equalization (HE) is employed to improve the quality of the image. ASM extracts the face contour and image shape to produce a personal profile. Then we use a PCA method to reduce dimensionality of face images. In the recognition part, we consider the improved Radial Basis Function Neural Networks (RBF NNs) to identify a unique pattern associated with each person. The proposed RBF NN architecture consists of three functional modules realizing the condition phase, the conclusion phase, and the inference phase completed with the help of fuzzy rules coming in the standard 'if-then' format. In the formation of the condition part of the fuzzy rules, the input space is partitioned with the use of Fuzzy C-Means (FCM) clustering. In the conclusion part of the fuzzy rules, the connections (weights) of the RBF NNs are represented by four kinds of polynomials such as constant, linear, quadratic, and reduced quadratic. The values of the coefficients are determined by running a gradient descent method. The output of the RBF NNs model is obtained by running a fuzzy inference method. The essential design parameters of the network (including learning rate, momentum coefficient and fuzzification coefficient used by the FCM) are optimized by means of Differential Evolution (DE). The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is quantified from the viewpoint of the output performance and recognition rate. PMID:26163042
A Fast Incremental Learning for Radial Basis Function Networks Using Local Linear Regression
Ozawa, Seiichi; Okamoto, Keisuke
To avoid the catastrophic interference in incremental learning, we have proposed Resource Allocating Network with Long Term Memory (RAN-LTM). In RAN-LTM, not only new training data but also some memory items stored in long-term memory are trained either by a gradient descent algorithm or by solving a linear regression problem. In the latter approach, radial basis function (RBF) centers are not trained but selected based on output errors when connection weights are updated. The proposed incremental learning algorithm belongs to the latter approach where the errors not only for a training data but also for several retrieved memory items and pseudo training data are minimized to suppress the catastrophic interference. The novelty of the proposed algorithm is that connection weights to be learned are restricted based on RBF activation in order to improve the efficiency in learning time and memory size. We evaluate the performance of the proposed algorithm in one-dimensional and multi-dimensional function approximation problems in terms of approximation accuracy, learning time, and average memory size. The experimental results demonstrate that the proposed algorithm can learn fast and have good performance with less memory size compared to memory-based learning methods.
Kuo, R J.; Cohen, P H.
1999-03-01
On-line tool wear estimation plays a very critical role in industry automation for higher productivity and product quality. In addition, appropriate and timely decision for tool change is significantly required in the machining systems. Thus, this paper is dedicated to develop an estimation system through integration of two promising technologies, artificial neural networks (ANN) and fuzzy logic. An on-line estimation system consisting of five components: (1) data collection; (2) feature extraction; (3) pattern recognition; (4) multi-sensor integration; and (5) tool/work distance compensation for tool flank wear, is proposed herein. For each sensor, a radial basis function (RBF) network is employed to recognize the extracted features. Thereafter, the decisions from multiple sensors are integrated through a proposed fuzzy neural network (FNN) model. Such a model is self-organizing and self-adjusting, and is able to learn from the experience. Physical experiments for the metal cutting process are implemented to evaluate the proposed system. The results show that the proposed system can significantly increase the accuracy of the product profile.
Oyang, Yen-Jen; Hwang, Shien-Ching; Ou, Yu-Yen; Chen, Chien-Yu; Chen, Zhi-Wei
2005-01-01
This paper presents a novel learning algorithm for efficient construction of the radial basis function (RBF) networks that can deliver the same level of accuracy as the support vector machines (SVMs) in data classification applications. The proposed learning algorithm works by constructing one RBF subnetwork to approximate the probability density function of each class of objects in the training data set. With respect to algorithm design, the main distinction of the proposed learning algorithm is the novel kernel density estimation algorithm that features an average time complexity of O(n log n), where n is the number of samples in the training data set. One important advantage of the proposed learning algorithm, in comparison with the SVM, is that the proposed learning algorithm generally takes far less time to construct a data classifier with an optimized parameter setting. This feature is of significance for many contemporary applications, in particular, for those applications in which new objects are continuously added into an already large database. Another desirable feature of the proposed learning algorithm is that the RBF networks constructed are capable of carrying out data classification with more than two classes of objects in one single run. In other words, unlike with the SVM, there is no need to resort to mechanisms such as one-against-one or one-against-all for handling datasets with more than two classes of objects. The comparison with SVM is of particular interest, because it has been shown in a number of recent studies that SVM generally are able to deliver higher classification accuracy than the other existing data classification algorithms. As the proposed learning algorithm is instance-based, the data reduction issue is also addressed in this paper. One interesting observation in this regard is that, for all three data sets used in data reduction experiments, the number of training samples remaining after a naive data reduction mechanism is
A new discrete-continuous algorithm for radial basis function networks construction.
Zhang, Long; Li, Kang; He, Haibo; Irwin, George W
2013-11-01
The construction of a radial basis function (RBF) network involves the determination of the model size, hidden nodes, and output weights. Least squares-based subset selection methods can determine a RBF model size and its parameters simultaneously. Although these methods are robust, they may not achieve optimal results. Alternatively, gradient methods are widely used to optimize all the parameters. The drawback is that most algorithms may converge slowly as they treat hidden nodes and output weights separately and ignore their correlations. In this paper, a new discrete-continuous algorithm is proposed for the construction of a RBF model. First, the orthogonal least squares (OLS)-based forward stepwise selection constructs an initial model by selecting model terms one by one from a candidate term pool. Then a new Levenberg-Marquardt (LM)-based parameter optimization is proposed to further optimize the hidden nodes and output weights in the continuous space. To speed up the convergence, the proposed parameter optimization method considers the correlation between the hidden nodes and output weights, which is achieved by translating the output weights to dependent parameters using the OLS method. The correlation is also used by the previously proposed continuous forward algorithm (CFA). However, unlike the CFA, the new method optimizes all the parameters simultaneously. In addition, an equivalent recursive sum of squared error is derived to reduce the computation demanding for the first derivatives used in the LM method. Computational complexity is given to confirm the new method is much more computationally efficient than the CFA. Different numerical examples are presented to illustrate the effectiveness of the proposed method. Further, Friedman statistical tests on 13 classification problems are performed, and the results demonstrate that RBF networks built by the new method are very competitive in comparison with some popular classifiers.
Radial Basis Function Based Neural Network for Motion Detection in Dynamic Scenes.
Huang, Shih-Chia; Do, Ben-Hsiang
2014-01-01
Motion detection, the process which segments moving objects in video streams, is the first critical process and plays an important role in video surveillance systems. Dynamic scenes are commonly encountered in both indoor and outdoor situations and contain objects such as swaying trees, spouting fountains, rippling water, moving curtains, and so on. However, complete and accurate motion detection in dynamic scenes is often a challenging task. This paper presents a novel motion detection approach based on radial basis function artificial neural networks to accurately detect moving objects not only in dynamic scenes but also in static scenes. The proposed method involves two important modules: a multibackground generation module and a moving object detection module. The multibackground generation module effectively generates a flexible probabilistic model through an unsupervised learning process to fulfill the property of either dynamic background or static background. Next, the moving object detection module achieves complete and accurate detection of moving objects by only processing blocks that are highly likely to contain moving objects. This is accomplished by two procedures: the block alarm procedure and the object extraction procedure. The detection results of our method were evaluated by qualitative and quantitative comparisons with other state-of-the-art methods based on a wide range of natural video sequences. The overall results show that the proposed method substantially outperforms existing methods with Similarity and F1 accuracy rates of 69.37% and 65.50%, respectively. PMID:24108721
Parameter estimation for stiff equations of biosystems using radial basis function networks
Directory of Open Access Journals (Sweden)
Sugimoto Masahiro
2006-04-01
Full Text Available Abstract Background The modeling of dynamic systems requires estimating kinetic parameters from experimentally measured time-courses. Conventional global optimization methods used for parameter estimation, e.g. genetic algorithms (GA, consume enormous computational time because they require iterative numerical integrations for differential equations. When the target model is stiff, the computational time for reaching a solution increases further. Results In an attempt to solve this problem, we explored a learning technique that uses radial basis function networks (RBFN to achieve a parameter estimation for biochemical models. RBFN reduce the number of numerical integrations by replacing derivatives with slopes derived from the distribution of searching points. To introduce a slight search bias, we implemented additional data selection using a GA that searches data-sparse areas at low computational cost. In addition, we adopted logarithmic transformation that smoothes the fitness surface to obtain a solution simply. We conducted numerical experiments to validate our methods and compared the results with those obtained by GA. We found that the calculation time decreased by more than 50% and the convergence rate increased from 60% to 90%. Conclusion In this work, our RBFN technique was effective for parameter optimization of stiff biochemical models.
Radial basis function networks applied to DNBR calculation in digital core protection systems
Energy Technology Data Exchange (ETDEWEB)
Lee, Gyu-Cheon E-mail: gclee@kopec.co.kr; Heung Chang, Soon
2003-10-01
The nuclear power plant has to be operated with sufficient margin from the specified DNBR limit for assuring its safety. The digital core protection system calculates on-line real-time DNBR by using a complex subchannel analysis program, and triggers a reliable reactor shutdown if the calculated DNBR approaches the specified limit. However, it takes a relatively long calculation time even for a steady state condition, which may have an adverse effect on the operation flexibility. To overcome the drawback, a new method using a radial basis function network is presented in this paper. Nonparametric training approach is utilized, which shows dramatic reduction of the training time, no tedious heuristic process for optimizing parameters, and no local minima problem during the training. The test results show that the predicted DNBR is within about {+-}2% deviation from the target DNBR for the fixed axial flux shape case. For the variable axial flux case including severely skewed shapes that appeared during accidents, the deviation is within about {+-}10%. The suggested method could be the alternative that can calculate DNBR very quickly while guaranteeing the plant safety.
Fast and efficient second-order method for training radial basis function networks.
Xie, Tiantian; Yu, Hao; Hewlett, Joel; Rózycki, Paweł; Wilamowski, Bogdan
2012-04-01
This paper proposes an improved second order (ISO) algorithm for training radial basis function (RBF) networks. Besides the traditional parameters, including centers, widths and output weights, the input weights on the connections between input layer and hidden layer are also adjusted during the training process. More accurate results can be obtained by increasing variable dimensions. Initial centers are chosen from training patterns and other parameters are generated randomly in limited range. Taking the advantages of fast convergence and powerful search ability of second order algorithms, the proposed ISO algorithm can normally reach smaller training/testing error with much less number of RBF units. During the computation process, quasi Hessian matrix and gradient vector are accumulated as the sum of related sub matrices and vectors, respectively. Only one Jacobian row is stored and used for multiplication, instead of the entire Jacobian matrix storage and multiplication. Memory reduction benefits the computation speed and allows the training of problems with basically unlimited number of patterns. Several practical discrete and continuous classification problems are applied to test the properties of the proposed ISO training algorithm.
Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction
Venkatesan, R.
2016-01-01
Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and release phases. Generally, software testing is a critical task in the software development process wherein it is to save time and budget by detecting defects at the earliest and deliver a product without defects to the customers. This testing phase should be carefully operated in an effective manner to release a defect-free (bug-free) software product to the customers. In order to improve the software testing process, fault prediction methods identify the software parts that are more noted to be defect-prone. This paper proposes a prediction approach based on conventional radial basis function neural network (RBFNN) and the novel adaptive dimensional biogeography based optimization (ADBBO) model. The developed ADBBO based RBFNN model is tested with five publicly available datasets from the NASA data program repository. The computed results prove the effectiveness of the proposed ADBBO-RBFNN classifier approach with respect to the considered metrics in comparison with that of the early predictors available in the literature for the same datasets.
The Application of Direction Basis Function Neural Networks to the Prediction of Chaotic Time Series
Institute of Scientific and Technical Information of China (English)
CAOWenming
2004-01-01
In this paper we have examined the ability of Direction basis function networks (DBFN) to predict the output of a chaotic time series generated from a model of a physical system. DBFNs are known to be universal approximators, and chaotic systems are known to exhibit “random” behavior. Therefore the challenge is to apply the DBFN to the prediction of the output of a chaotic system, which we have chosen here to be the Mackey-Glass delay differential equation. The DBFN has been trained with off-line supervised learning using a Recursive Least Squares optimization for obtaining weights. Key issues which are addressed are the estimation of the order of the system and dependence of prediction error on various factors such as placement of DBF centers, selection of perceptive widths, and number of training samples. Included in this study is an implementation of Moody and Darken's K Means Clustering approach to optimally place DBF centers and a heuristic nearest neighbor method for determining perceptive widths.
RADIAL BASIS FUNCTION NETWORK DEPENDENT EXCLUSIVE MUTUAL INTERPOLATION FOR MISSING VALUE IMPUTATION
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R. S. Somasundaram
2013-01-01
Full Text Available The success of data mining relies on the purity of the data set. Before performing the data mining, the data has to be cleaned. An unprocessed data set may contain noisy or missing values which is a critical research issue in the pre-processing stage. Imputation methods are being used to solve the missing value problems. In this proposed work, a machine learning based imputation method is proposed by using the mutual information by exclusively interpolating two different section of the same dataset. For designing the proposed model, a radial basis function based neural network has been used. The performance of the proposed algorithm has been measured with respect to different rate or percentage of missing values in the data set and the results has been compared with existing simple and efficient imputation methods also. To evaluate the performance, the standard WDBC data set has been used. The proposed algorithm performs well and was able to impute the missing values even in the worst cases with more than 50% of missing values. Instead of using simple quality measure such as Mean Square Error (MSE to evaluate the imputed data quality, in this study, the quality is measured in terms of classification performance. The results arrived were more significant and comparable.
On-line Supervised Adaptive Training Using Radial Basis Function Networks.
Luo, Wan; Billings, Steve A.; Fung, Chi F.
1996-12-01
A new recursive supervised training algorithm is derived for the radial basis neural network architecture. The new algorithm combines the procedures of on-line candidate regressor selection with the conventional Givens QR based recursive parameter estimator to provide efficient adaptive supervised network training. A new concise on-line correlation based performance monitoring scheme is also introduced as an auxiliary device to detect structural changes in temporal data processing applications. Practical and simulated examples are included to demonstrate the effectiveness of the new procedures. Copyright 1996 Elsevier Science Ltd.
Jingwen Tian; Meijuan Gao; Yonggang He
2013-01-01
Since the control system of the welding gun pose in whole‐position welding is complicated and nonlinear, an intelligent control system of welding gun pose for a pipeline welding robot based on an improved radial basis function neural network (IRBFNN) and expert system (ES) is presented in this paper. The structure of the IRBFNN is constructed and the improved genetic algorithm is adopted to optimize the network structure. This control system makes full use of the characteristics of the IRBFNN...
DEFF Research Database (Denmark)
Lee, Kyo-Beum; Blaabjerg, Frede
2005-01-01
. To estimate the motor inertia value, the observer using the Radial Basis Function Network (RBFN) is applied. A control law for stabilizing the system and adaptive laws for updating both of the weights in the RBFN and a bounding constant are established so that the whole closed-loop system is stable...
Lee, Tzong-Yi; Chen, Shu-An; Hung, Hsin-Yi; Ou, Yu-Yen
2011-03-09
Ubiquitin (Ub) is a small protein that consists of 76 amino acids about 8.5 kDa. In ubiquitin conjugation, the ubiquitin is majorly conjugated on the lysine residue of protein by Ub-ligating (E3) enzymes. Three major enzymes participate in ubiquitin conjugation. They are E1, E2 and E3 which are responsible for activating, conjugating and ligating ubiquitin, respectively. Ubiquitin conjugation in eukaryotes is an important mechanism of the proteasome-mediated degradation of a protein and regulating the activity of transcription factors. Motivated by the importance of ubiquitin conjugation in biological processes, this investigation develops a method, UbSite, which uses utilizes an efficient radial basis function (RBF) network to identify protein ubiquitin conjugation (ubiquitylation) sites. This work not only investigates the amino acid composition but also the structural characteristics, physicochemical properties, and evolutionary information of amino acids around ubiquitylation (Ub) sites. With reference to the pathway of ubiquitin conjugation, the substrate sites for E3 recognition, which are distant from ubiquitylation sites, are investigated. The measurement of F-score in a large window size (-20∼+20) revealed a statistically significant amino acid composition and position-specific scoring matrix (evolutionary information), which are mainly located distant from Ub sites. The distant information can be used effectively to differentiate Ub sites from non-Ub sites. As determined by five-fold cross-validation, the model that was trained using the combination of amino acid composition and evolutionary information performs best in identifying ubiquitin conjugation sites. The prediction sensitivity, specificity, and accuracy are 65.5%, 74.8%, and 74.5%, respectively. Although the amino acid sequences around the ubiquitin conjugation sites do not contain conserved motifs, the cross-validation result indicates that the integration of distant sequence features of Ub
Directory of Open Access Journals (Sweden)
Tzong-Yi Lee
Full Text Available Ubiquitin (Ub is a small protein that consists of 76 amino acids about 8.5 kDa. In ubiquitin conjugation, the ubiquitin is majorly conjugated on the lysine residue of protein by Ub-ligating (E3 enzymes. Three major enzymes participate in ubiquitin conjugation. They are E1, E2 and E3 which are responsible for activating, conjugating and ligating ubiquitin, respectively. Ubiquitin conjugation in eukaryotes is an important mechanism of the proteasome-mediated degradation of a protein and regulating the activity of transcription factors. Motivated by the importance of ubiquitin conjugation in biological processes, this investigation develops a method, UbSite, which uses utilizes an efficient radial basis function (RBF network to identify protein ubiquitin conjugation (ubiquitylation sites. This work not only investigates the amino acid composition but also the structural characteristics, physicochemical properties, and evolutionary information of amino acids around ubiquitylation (Ub sites. With reference to the pathway of ubiquitin conjugation, the substrate sites for E3 recognition, which are distant from ubiquitylation sites, are investigated. The measurement of F-score in a large window size (-20∼+20 revealed a statistically significant amino acid composition and position-specific scoring matrix (evolutionary information, which are mainly located distant from Ub sites. The distant information can be used effectively to differentiate Ub sites from non-Ub sites. As determined by five-fold cross-validation, the model that was trained using the combination of amino acid composition and evolutionary information performs best in identifying ubiquitin conjugation sites. The prediction sensitivity, specificity, and accuracy are 65.5%, 74.8%, and 74.5%, respectively. Although the amino acid sequences around the ubiquitin conjugation sites do not contain conserved motifs, the cross-validation result indicates that the integration of distant sequence
Directory of Open Access Journals (Sweden)
Shuhuan Wen
2012-01-01
Full Text Available This paper works on hybrid force/position control in robotic manipulation and proposes an improved radial basis functional (RBF neural network, which is a robust relying on the Hamilton Jacobi Issacs principle of the force control loop. The method compensates uncertainties in a robot system by using the property of RBF neural network. The error approximation of neural network is regarded as an external interference of the system, and it is eliminated by the robust control method. Since the conventionally fixed structure of RBF network is not optimal, resource allocating network (RAN is proposed in this paper to adjust the network structure in time and avoid the underfit. Finally the advantage of system stability and transient performance is demonstrated by the numerical simulations.
DEFF Research Database (Denmark)
Lee, Kyo-Beum; Bae, C.H.; Blaabjerg, Frede
2005-01-01
A scheme to estimate the moment of inertia in a servo motor drive system at very low speed is proposed. The typical speed estimation scheme used in most servo systems operated at low speed is highly sensitive to variations in the moment of inertia. An observer that uses a radial basis function...... network to estimate the motor inertia value is proposed. The effectiveness of this observer is confirmed by both simulations and experiments....
Shahbaz A. Siddiqui; Kusum Verma; K. R. Niazi; Manoj Fozdar
2014-01-01
On-line Transient Stability Assessment (TSA) is challenging task due to the large number of variables involved and continuously varying operating conditions. This study proposes an on-line transient stability assessment methodology based on the predicted values of generator rotor angles under varying operating conditions for predefined contingency set through Radial Basis Function Neural Network (RBFNN). The real and reactive power loads are taken as input features for training of the neural ...
Energy Technology Data Exchange (ETDEWEB)
Al-Amoudi, A.; Zhang, L. [University of Leeds (United Kingdom). School of Electronic and Electrical Engineering
2000-09-01
A neural-network-based approach for solar array modelling is presented. The logic hidden unit of the proposed network consists of a set of nonlinear radial basis functions (RBFs) which are connected directly to the input vector. The links between hidden and output units are linear. The model can be trained using a random set of data collected from a real photovoltaic (PV) plant. The training procedures are fast and the accuracy of the trained models is comparable with that of the conventional model. The principle and training procedures of the RBF-network modelling when applied to emulate the I/V characteristics of PV arrays are discussed. Simulation results of the trained RBF networks for modelling a PV array and predicting the maximum power points of a real PV panel are presented. (author)
Analysis of CT Brain Images using Radial Basis Function Neural Network
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T. Joshva Devadas
2012-07-01
Full Text Available Medical image processing and analysis is the tool to assist radiologists in the diagnosis process to obtain a moreaccurate and faster diagnosis. In this work, we have developed a neural network to classify the computer tomography(CT brain tumor image for automatic diagnosis. This system is divided into four steps namely enhancement, segmentation, feature extraction and classification. In the first phase, an edge-based selective median filter is usedto improve the visibility of the loss of the gray-white matter interface in CT brain tumor images. Second phaseuses a modified version of shift genetic algorithm for the segmentation. Next phase extracts the textural featuresusing statistical texture analysis method. These features are fed into classifiers like BPN, Fuzzy k-NN, and radialbasis function network. The performances of these classifiers are analyzed in the final phase with receiver operating characteristic and precision-recall curve. The result shows that the CAD system is only to develop the tool for braintumor and proposed method is very accurate and computationally more efficient and less time consuming.Defence Science Journal, 2012, 62(4, pp.212-218, DOI:http://dx.doi.org/10.14429/dsj.62.1830
Li, Meina; Kwak, Keun-Chang; Kim, Youn Tae
2016-01-01
Conventionally, indirect calorimetry has been used to estimate oxygen consumption in an effort to accurately measure human body energy expenditure. However, calorimetry requires the subject to wear a mask that is neither convenient nor comfortable. The purpose of our study is to develop a patch-type sensor module with an embedded incremental radial basis function neural network (RBFNN) for estimating the energy expenditure. The sensor module contains one ECG electrode and a three-axis accelerometer, and can perform real-time heart rate (HR) and movement index (MI) monitoring. The embedded incremental network includes linear regression (LR) and RBFNN based on context-based fuzzy c-means (CFCM) clustering. This incremental network is constructed by building a collection of information granules through CFCM clustering that is guided by the distribution of error of the linear part of the LR model. PMID:27669249
Li, Meina; Kwak, Keun-Chang; Kim, Youn Tae
2016-09-22
Conventionally, indirect calorimetry has been used to estimate oxygen consumption in an effort to accurately measure human body energy expenditure. However, calorimetry requires the subject to wear a mask that is neither convenient nor comfortable. The purpose of our study is to develop a patch-type sensor module with an embedded incremental radial basis function neural network (RBFNN) for estimating the energy expenditure. The sensor module contains one ECG electrode and a three-axis accelerometer, and can perform real-time heart rate (HR) and movement index (MI) monitoring. The embedded incremental network includes linear regression (LR) and RBFNN based on context-based fuzzy c-means (CFCM) clustering. This incremental network is constructed by building a collection of information granules through CFCM clustering that is guided by the distribution of error of the linear part of the LR model.
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Meina Li
2016-09-01
Full Text Available Conventionally, indirect calorimetry has been used to estimate oxygen consumption in an effort to accurately measure human body energy expenditure. However, calorimetry requires the subject to wear a mask that is neither convenient nor comfortable. The purpose of our study is to develop a patch-type sensor module with an embedded incremental radial basis function neural network (RBFNN for estimating the energy expenditure. The sensor module contains one ECG electrode and a three-axis accelerometer, and can perform real-time heart rate (HR and movement index (MI monitoring. The embedded incremental network includes linear regression (LR and RBFNN based on context-based fuzzy c-means (CFCM clustering. This incremental network is constructed by building a collection of information granules through CFCM clustering that is guided by the distribution of error of the linear part of the LR model.
Albrecht, S; Busch, J; Kloppenburg, M; Metze, F; Tavan, P
2000-12-01
By adding reverse connections from the output layer to the central layer it is shown how a generalized radial basis functions (GRBF) network can self-organize to form a Bayesian classifier, which is also capable of novelty detection. For this purpose, three stochastic sequential learning rules are introduced from biological considerations which pertain to the centers, the shapes, and the widths of the receptive fields of the neurons and allow ajoint optimization of all network parameters. The rules are shown to generate maximum-likelihood estimates of the class-conditional probability density functions of labeled data in terms of multivariate normal mixtures. Upon combination with a hierarchy of deterministic annealing procedures, which implement a multiple-scale approach, the learning process can avoid the convergence problems hampering conventional expectation-maximization algorithms. Using an example from the field of speech recognition, the stages of the learning process and the capabilities of the self-organizing GRBF classifier are illustrated.
Oh, Sung-Kwun; Kim, Wook-Dong; Pedrycz, Witold
2016-05-01
In this paper, we introduce a new architecture of optimized Radial Basis Function neural network classifier developed with the aid of fuzzy clustering and data preprocessing techniques and discuss its comprehensive design methodology. In the preprocessing part, the Linear Discriminant Analysis (LDA) or Principal Component Analysis (PCA) algorithm forms a front end of the network. The transformed data produced here are used as the inputs of the network. In the premise part, the Fuzzy C-Means (FCM) algorithm determines the receptive field associated with the condition part of the rules. The connection weights of the classifier are of functional nature and come as polynomial functions forming the consequent part. The Particle Swarm Optimization algorithm optimizes a number of essential parameters needed to improve the accuracy of the classifier. Those optimized parameters include the type of data preprocessing, the dimensionality of the feature vectors produced by the LDA (or PCA), the number of clusters (rules), the fuzzification coefficient used in the FCM algorithm and the orders of the polynomials of networks. The performance of the proposed classifier is reported for several benchmarking data-sets and is compared with the performance of other classifiers reported in the previous studies.
Directory of Open Access Journals (Sweden)
Xiaodong Mao
2014-06-01
Full Text Available In this study, near-infrared reflectance spectroscopy and radial basis function (RBF neural network algorithm were used to measure the protein content of wheat owing to their nondestructiveness and quick speed as well as better performance compared to the traditional measuring method (semimicro-Kjeldahl in actual practice. To simplify the complex structure of the RBF network caused by the excessive wave points of samples obtained by near-infrared reflectance spectroscopy, we proposed the particle swarm optimization (PSO algorithm to optimize the cluster center in the hidden layers of the RBF neural network. In addition, a series of improvements for the PSO algorithm was also made to deal with its drawbacks in premature convergence and mechanical inertia weight setting. The experimental analysis demonstrated that the improved PSO algorithm greatly reduced the complexity of the network structure and improved the training speed of the RBF network. Meanwhile, the research result also proved the high performance of the model with its root-mean-square error of prediction (RMSEP and prediction correlation coefficient (R at 0.26576 and 0.975, respectively, thereby fulfilling the modern agricultural testing requirements featuring nondestructiveness, real-timing, and abundance in the number of samples.
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Shahbaz A. Siddiqui
2014-10-01
Full Text Available On-line Transient Stability Assessment (TSA is challenging task due to the large number of variables involved and continuously varying operating conditions. This study proposes an on-line transient stability assessment methodology based on the predicted values of generator rotor angles under varying operating conditions for predefined contingency set through Radial Basis Function Neural Network (RBFNN. The real and reactive power loads are taken as input features for training of the neural network. Principal Component Analysis (PCA is used for dimensionality reduction of the input data set to select informative features. The proposed method is tested on IEEE-39 bus test system and the results obtained for transient stability assessment through predicted rotor angles are promising.
Directory of Open Access Journals (Sweden)
Peng Zhang
Full Text Available Optimal guidance is essential for the soft landing task. However, due to its high computational complexities, it is hardly applied to the autonomous guidance. In this paper, a computationally inexpensive optimal guidance algorithm based on the radial basis function neural network (RBFNN is proposed. The optimization problem of the trajectory for soft landing on asteroids is formulated and transformed into a two-point boundary value problem (TPBVP. Combining the database of initial states with the relative initial co-states, an RBFNN is trained offline. The optimal trajectory of the soft landing is determined rapidly by applying the trained network in the online guidance. The Monte Carlo simulations of soft landing on the Eros433 are performed to demonstrate the effectiveness of the proposed guidance algorithm.
Mixtures of truncated basis functions
DEFF Research Database (Denmark)
Langseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael;
2012-01-01
In this paper we propose a framework, called mixtures of truncated basis functions (MoTBFs), for representing general hybrid Bayesian networks. The proposed framework generalizes both the mixture of truncated exponentials (MTEs) framework and the mixture of polynomials (MoPs) framework. Similar...... for efficiently approximating an arbitrary density function using the MoTBF framework. The transla- tion method is more flexible than existing MTE or MoP-based methods, and it supports an online/anytime tradeoff between the accuracy and the complexity of the approxima- tion. Experimental results show...
An on-line training radial basis function neural network for optimum operation of the UPFC
Farrag, Mohamed; Putrus, Ghanim
2011-01-01
The concept of Flexible A.C. Transmission Systems (FACTS) technology was developed to enhance the performance of electric power networks (both in steady-state and transient-state) and to make better utilization of existing power transmission facilities. The continuous improvement in power ratings and switching performance of power electronic devices together with advances in circuit design and control techniques are making this concept and devices employed in FACTS more commercially attractiv...
Energy Technology Data Exchange (ETDEWEB)
Casadio, R.; Fariselli, P.; Vivarelli, F. [Univ. of Bologna (Italy); Compiani, M. [Univ. of Camerino (Italy)
1995-12-31
Radial basis function neural networks are trained on a data base comprising 38 globular proteins of well resolved crystallographic structure and the corresponding free energy contributions to the overall protein stability (as computed partially from crystallographic analysis and partially with multiple regression from experimental thermodynamic data by Ponnuswamy and Gromiha (1994)). Starting from the residue sequence and using as input code the percentage of each residue and the total residue number of the protein, it is found with a cross-validation method that neural networks can optimally predict the free energy contributions due to hydrogen bonds, hydrophobic interactions and the unfolded state. Terms due to electrostatic and disulfide bonding free energies are poorly predicted. This is so also when other input codes, including the percentage of secondary structure type of the protein and/or residue-pair information are used. Furthermore, trained on the computed and/or experimental {Delta}G values of the data base, neural networks predict a conformational stability ranging from about 10 to 20 kcal mol{sup -1} rather independently of the residue sequence, with an average error per protein of about 9 kcal mol{sup -1}.
Casadio, R; Compiani, M; Fariselli, P; Vivarelli, F
1995-01-01
Radial basis function neural networks are trained on a data base comprising 38 globular proteins of well resolved crystallographic structure and the corresponding free energy contributions to the overall protein stability (as computed partially from chrystallographic analysis and partially with multiple regression from experimental thermodynamic data by Ponnuswamy and Gromiha (1994)). Starting from the residue sequence and using as input code the percentage of each residue and the total residue number of the protein, it is found with a cross-validation method that neural networks can optimally predict the free energy contributions due to hydrogen bonds, hydrophobic interactions and the unfolded state. Terms due to electrostatic and disulfide bonding free energies are poorly predicted. This is so also when other input codes, including the percentage of secondary structure type of the protein and/or residue-pair information are used. Furthermore, trained on the computed and/or experimental delta G values of the data base, neural networks predict a conformational stability ranging from about 10 to 20 kcal mol-1 rather independently of the residue sequence, with an average error per protein of about 9 kcal mol-1.
Directory of Open Access Journals (Sweden)
Chun-Cheng Lin
2016-09-01
Full Text Available Abnormal intra-QRS potentials (AIQPs are commonly observed in patients at high risk for ventricular tachycardia. We present a method for approximating a measured QRS complex using a non-linear neural network with all radial basis functions having the same smoothness. We extracted the high frequency, but low amplitude intra-QRS potentials using the approximation error to identify possible ventricular tachycardia. With a specified number of neurons, we performed an orthogonal least squares algorithm to determine the center of each Gaussian radial basis function. We found that the AIQP estimation error arising from part of the normal QRS complex could cause clinicians to misjudge patients with ventricular tachycardia. Our results also show that it is possible to correct this misjudgment by combining multiple AIQP parameters estimated using various spread parameters and numbers of neurons. Clinical trials demonstrate that higher AIQP-to-QRS ratios in the X, Y and Z leads are visible in patients with ventricular tachycardia than in normal subjects. A linear combination of 60 AIQP-to-QRS ratios can achieve 100% specificity, 90% sensitivity, and 95.8% total prediction accuracy for diagnosing ventricular tachycardia.
Institute of Scientific and Technical Information of China (English)
DONG Li-xin; XIAO Deng-ming Xiao; LIU Yi-lu
2007-01-01
Rough set (RS) and radial basis function neural network (RBFNN) based insulation data mining fault diagnosis for power transformer is proposed. On the one hand rough set is used as front of RBFNN to simplify the input of RBFNN and mine the rules. The mined rules whose "confidence" and "support" is higher than requirement are used to offer fault diagnosis service for power transformer directly. On the other hand the mining samples corresponding to the mined rule, whose "confidence and support" is lower than requirement,are used to be training samples set of RBFNN and these samples are clustered by rough set. The center of each clustering set is used to be center of radial basis function, i.e. , as the hidden layer neuron. The RBFNN is structured with above base, which is used to diagnose the case that can not be diagnosed by mined simplified valuable rules based on rough set. The advantages and effectiveness of this method are verified by testing.
Institute of Scientific and Technical Information of China (English)
Lei Wang; Cheng Shao; Hai Wang; Hong Wu
2006-01-01
Membrane technology has found wide applications in the petrochemical industry, mainly in the purification and recovery of the hydrogen resources. Accurate prediction of the membrane separation performance plays an important role in carrying out advanced process control (APC). For the first time, a soft-sensor model for the membrane separation process has been established based on the radial basis function (RBF) neural networks. The main performance parameters, i.e, permeate hydrogen concentration, permeate gas flux, and residue hydrogen concentration, are estimated quantitatively by measuring the operating temperature, feed-side pressure, permeate-side pressure, residue-side pressure, feed-gas flux, and feed-hydrogen concentration excluding flow structure, membrane parameters, and other compositions. The predicted results can gain the desired effects. The effectiveness of this novel approach lays a foundation for integrating control technology and optimizing the operation of the gas membrane separation process.
Meng, Qinggang; Lee, M. H.
2007-03-01
Advanced autonomous artificial systems will need incremental learning and adaptive abilities similar to those seen in humans. Knowledge from biology, psychology and neuroscience is now inspiring new approaches for systems that have sensory-motor capabilities and operate in complex environments. Eye/hand coordination is an important cross-modal cognitive function, and is also typical of many of the other coordinations that must be involved in the control and operation of embodied intelligent systems. This paper examines a biologically inspired approach for incrementally constructing compact mapping networks for eye/hand coordination. We present a simplified node-decoupled extended Kalman filter for radial basis function networks, and compare this with other learning algorithms. An experimental system consisting of a robot arm and a pan-and-tilt head with a colour camera is used to produce results and test the algorithms in this paper. We also present three approaches for adapting to structural changes during eye/hand coordination tasks, and the robustness of the algorithms under noise are investigated. The learning and adaptation approaches in this paper have similarities with current ideas about neural growth in the brains of humans and animals during tool-use, and infants during early cognitive development.
A Novel Carbon Steel Pipe Protection Based on Radial Basis Function Neural Network
Directory of Open Access Journals (Sweden)
Sami A. Ajeel
2010-01-01
Full Text Available Problem statement: The cost due to corrosion Damage have estimated to be 3-4% of their gross national product which significantly Countries problem around the world. Approach: In this study, a novel carbon steel pipe protection based on RBFNN was proposed. The RBFNN used to predict the minimum current density required in impressed current cathodic protection to protect low carbon steel pipe. Learning data was performed by using a 30 samples test with different concentration C%, temperature T, distance D and pH. The RBFNN model has four input nodes representing the (concentration C%, temperature T, distance D and pH, eight nodes at hidden layer and one output node representing the min. current density. Results: Generalization test used 5 data samples taken from the experimental results other than those data samples used in the learning process to check the performance of the neural network on these data. Conclusion: In addition, the experimental results indicate that proposed system can be used successfully to obtain minimum cathodic protection current density to protect low carbon steel pipes.
Directory of Open Access Journals (Sweden)
Seerat Fatima
2011-10-01
Full Text Available The purpose of this study is to classify the networks according to functions they performed, especially scrutinize their structures. The research concentrates on the influence of these functional networks on the internationalization process of small and medium sized companies (SME in developing countries. What are the different types of support being provided by network partners? What is the structure of the existing network? The research part is inductive, qualitative and based on case study. The study’s findings illustrate the subtleties of how various network partners interact with entrepreneurs to penetrate, integrate and extend their international markets. Networks can help entrepreneurs expose themselves to new opportunities, obtain knowledge, learn from experiences, and benefit from the synergistic effect of pooled resources. Another contribution of this paper is that it identifies structures of the functional networks, till date networks are classified on the basis of extent of support they provide, not on what support they provide, thus advancing the literature.
Meghabghab, George
2001-01-01
Discusses the evaluation of search engines and uses neural networks in stochastic simulation of the number of rejected Web pages per search query. Topics include the iterative radial basis functions (RBF) neural network; precision; response time; coverage; Boolean logic; regression models; crawling algorithms; and implications for search engine…
Rai, H. M.; Trivedi, A.; Chatterjee, K.; Shukla, S.
2014-01-01
This paper employed the Daubechies wavelet transform (WT) for R-peak detection and radial basis function neural network (RBFNN) to classify the electrocardiogram (ECG) signals. Five types of ECG beats: normal beat, paced beat, left bundle branch block (LBBB) beat, right bundle branch block (RBBB) beat and premature ventricular contraction (PVC) were classified. 500 QRS complexes were arbitrarily extracted from 26 records in Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, which are available on Physionet website. Each and every QRS complex was represented by 21 points from p1 to p21 and these QRS complexes of each record were categorized according to types of beats. The system performance was computed using four types of parameter evaluation metrics: sensitivity, positive predictivity, specificity and classification error rate. The experimental result shows that the average values of sensitivity, positive predictivity, specificity and classification error rate are 99.8%, 99.60%, 99.90% and 0.12%, respectively with RBFNN classifier. The overall accuracy achieved for back propagation neural network (BPNN), multilayered perceptron (MLP), support vector machine (SVM) and RBFNN classifiers are 97.2%, 98.8%, 99% and 99.6%, respectively. The accuracy levels and processing time of RBFNN is higher than or comparable with BPNN, MLP and SVM classifiers.
Directory of Open Access Journals (Sweden)
Jingwen Tian
2013-02-01
Full Text Available Since the control system of the welding gun pose in whole‐position welding is complicated and nonlinear, an intelligent control system of welding gun pose for a pipeline welding robot based on an improved radial basis function neural network (IRBFNN and expert system (ES is presented in this paper. The structure of the IRBFNN is constructed and the improved genetic algorithm is adopted to optimize the network structure. This control system makes full use of the characteristics of the IRBFNN and the ES. The ADXRS300 micro‐mechanical gyro is used as the welding gun position sensor in this system. When the welding gun position is obtained, an appropriate pitch angle can be obtained through expert knowledge and the numeric reasoning capacity of the IRBFNN. ARM is used as the controller to drive the welding gun pitch angle step motor in order to adjust the pitch angle of the welding gun in real‐time. The experiment results show that the intelligent control system of the welding gun pose using the IRBFNN and expert system is feasible and it enhances the welding quality. This system has wide prospects for application.
Ou, Yu-Yen; Chen, Shu-An; Chang, Yun-Min; Velmurugan, Devadasan; Fukui, Kazuhiko; Michael Gromiha, M
2013-09-01
Efflux proteins are membrane proteins, which are involved in the transportation of multidrugs. The annotation of efflux proteins in genomic sequences would aid to understand the function. Although the percentage of membrane proteins in genomes is estimated to be 25-30%, there is no information about the content of efflux proteins. For annotating such class of proteins it is necessary to develop a reliable method to identify efflux proteins from amino acid sequence information. In this work, we have developed a method based on radial basis function networks using position specific scoring matrices (PSSM) and amino acid properties. We noticed that the C-terminal domain of efflux proteins contain vital information for discrimination. Our method showed an accuracy of 78 and 92% in discriminating efflux proteins from transporters and membrane proteins, respectively using fivefold cross-validation. We utilized our method for annotating the genomes E. coli and P. aeruginosa and it predicted 8.7 and 9.2% of proteins as efflux proteins in these genomes, respectively. The predicted efflux proteins have been compared with available experimental data and we observed a very good agreement between them. Further, we developed a web server for classifying efflux proteins and it is freely available at http://rbf.bioinfo.tw/∼sachen/EFFLUXpredict/Efflux-RBF.php. We suggest that our method could be an effective tool for annotating efflux proteins in genomic sequences.
Ou, Yu-Yen; Chen, Shu-An; Gromiha, M Michael
2010-05-15
Transporters are proteins that are involved in the movement of ions or molecules across biological membranes. Transporters are generally classified into channels/pores, electrochemical transporters, and active transporters. Discriminating the specific class of transporters and their subfamilies are essential tasks in computational biology for the advancement of structural and functional genomics. We have systematically analyzed the amino acid composition, residue pair preference and amino acid properties in six different families of transporters. Utilizing the information, we have developed a radial basis function (RBF) network method based on profiles obtained with position specific scoring matrices for discriminating transporters belonging to three different classes and six families. Our method showed a fivefold cross validation accuracy of 76%, 73%, and 69% for discriminating transporters and nontransporters, three different classes and six different families of transporters, respectively. Further, the method was tested with independent datasets, which showed similar level of accuracy. A web server has been developed for discriminating transporters based on three classes and six families, and it is available at http://rbf.bioinfo.tw/ approximately sachen/tcrbf.html. We suggest that our method could be effectively used to identify transporters and discriminating them into different classes and families.
Huang, Wei; Oh, Sung-Kwun; Pedrycz, Witold
2014-12-01
In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature.
Prediction of fMRI time series of a single voxel using radial basis function neural network
Song, Sutao; Zhang, Jiacai; Yao, Li
2011-03-01
A great deal of current literature regarding functional neuroimaging has elucidated the relationships of neurons distributed all over the brain. Modern neuroimaging techniques, such as the functional MRI (fMRI), provide a convenient tool for people to study the correlation among different voxels as well as the spatio-temporal patterns of brain activity. In this study, we present a computational model using radial basis function neural network (RBF-NN) to predict the fMRI voxel activation with the activation of other voxels acquired at the same time. The fMRI data from a visual images stimuli presentation experiment was separated into two sets; one was used to train the model, and the other to validate the accuracy or generalizability of the model. In the visual stimuli presentation experiment, the subject did simple one-back-repetition tasks when four categories of stimuli (houses, faces, cars, and cats) were presented. Voxel sets A and B were selected from fMRI data by two different voxel selection criterion: (1) Voxel set A are those activated for any kind of object stronger than the other three objects in regions of interest (ROIs) without correction (P=0.001); (2) Voxel set B are those activated for at least one of the categories of stimuli within the ROIs (FWE correction, P=0.05). RBF-NN regression models construct the nonlinear relationship between the activation of voxels in A and B. Our test results showed that RBF-NN can capture the nonlinear relationship existing in neurons and reveal the relationship between voxel's activation from different brain regions.
Ai, Yuewei; Shao, Xinyu; Jiang, Ping; Li, Peigen; Liu, Yang; Yue, Chen
2015-11-01
The welded joints of dissimilar materials have been widely used in automotive, ship and space industries. The joint quality is often evaluated by weld seam geometry, microstructures and mechanical properties. To obtain the desired weld seam geometry and improve the quality of welded joints, this paper proposes a process modeling and parameter optimization method to obtain the weld seam with minimum width and desired depth of penetration for laser butt welding of dissimilar materials. During the process, Taguchi experiments are conducted on the laser welding of the low carbon steel (Q235) and stainless steel (SUS301L-HT). The experimental results are used to develop the radial basis function neural network model, and the process parameters are optimized by genetic algorithm. The proposed method is validated by a confirmation experiment. Simultaneously, the microstructures and mechanical properties of the weld seam generated from optimal process parameters are further studied by optical microscopy and tensile strength test. Compared with the unoptimized weld seam, the welding defects are eliminated in the optimized weld seam and the mechanical properties are improved. The results show that the proposed method is effective and reliable for improving the quality of welded joints in practical production.
Ebtehaj, Isa; Bonakdari, Hossein; Zaji, Amir Hossein
2016-01-01
In this study, an expert system with a radial basis function neural network (RBF-NN) based on decision trees (DT) is designed to predict sediment transport in sewer pipes at the limit of deposition. First, sensitivity analysis is carried out to investigate the effect of each parameter on predicting the densimetric Froude number (Fr). The results indicate that utilizing the ratio of the median particle diameter to pipe diameter (d/D), ratio of median particle diameter to hydraulic radius (d/R) and volumetric sediment concentration (C(V)) as the input combination leads to the best Fr prediction. Subsequently, the new hybrid DT-RBF method is presented. The results of DT-RBF are compared with RBF and RBF-particle swarm optimization (PSO), which uses PSO for RBF training. It appears that DT-RBF is more accurate (R(2) = 0.934, MARE = 0.103, RMSE = 0.527, SI = 0.13, BIAS = -0.071) than the two other RBF methods. Moreover, the proposed DT-RBF model offers explicit expressions for use by practicing engineers. PMID:27386995
Neurons with radial basis like rate functions.
Kovács, Zsolt László
2005-01-01
Artificial neural networks constructed with "locally tuned processing units" and more generally referred to as "radial basis function networks" have been proposed by a number of workers. In this communication, I submit a conjecture, based on indirect experimental and direct computational evidence of the Hodgkin-Huxley model, that there may be biological neurons in nervous systems for which the rate function is locally tuned. If proved to be valid, this conjecture may simplify neurodynamic models of some functions of nervous systems.
Fidêncio, Paulo H; Poppi, Ronei J; de Andrade, João C; de Abreu, Mônica F
2008-07-01
Total nitrogen has been determined by using a model developed between the conventional chemical measurements and diffuse reflectance spectra in the near-infrared region. Samples (244) from different types of soils with total nitrogen contents ranging from 0.20 to 13.60% (m/m) were modeled by partial least-squares regression (PLS), multi-layer perceptron feed-forward networks (MLP) and radial basis function networks (RBFN). The RBFN model produced a better square error of prediction (SEP) of 0.048 and R(2) = 0.93 in a procedure that is simpler, faster and less dependent on the initial conditions.
Baraldi, Andrea; Parmiggiani, Flavio
1997-10-01
In the first part of this paper a new on-line fully self- organizing artificial neural network model (FSONN), pursuing dynamic generation and removal of neurons and synaptic links, is proposed. The model combines properties of the self- organizing map (SOM), fuzzy c-means (FCM), growing neural gas (GNG) and fuzzy simplified adaptive resonance theory (Fuzzy SART) algorithms. In the second part of the paper experimental results are provided and discussed. Our conclusion is that the proposed connectionist model features several interesting properties, such as the following: (1) the system requires no a priori knowledge of the dimension, size and/or adjacency structure of the network; (2) with respect to other connectionist models found in the literature, the system can be employed successfully in: (a) a vector quantization; (b) density function estimation; and (c) structure detection in input data to be mapped topologically correctly onto an output lattice pursuing dimensionality reduction; and (3) the system is computationally efficient, its processing time increasing linearly with the number of neurons and synaptic links.
Functional Basis of Microorganism Classification.
Directory of Open Access Journals (Sweden)
Chengsheng Zhu
2015-08-01
Full Text Available Correctly identifying nearest "neighbors" of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion. Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1 the inconsistency of functional diversity levels among different taxa and (2 an (unsurprising bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. Fusion is a more practical fit for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment and are less
Goudarzi, Shidrokh; Haslina Hassan, Wan; Abdalla Hashim, Aisha-Hassan; Soleymani, Seyed Ahmad; Anisi, Mohammad Hossein; Zakaria, Omar M.
2016-01-01
This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover. PMID:27438600
Afkhami, Abbas; Abbasi-Tarighat, Maryam
2008-06-01
In the present study, chemometric analysis of visible spectral data of phospho-and silico-molybdenum blue complexes was used to develop artificial neural networks (ANNs) for the simultaneous determination of the phosphate and silicate. Combinations of principal component analysis (PCA) with feed-forward neural networks (FFNNs) and radial basis function networks (RBFNs) were built and investigated. The structures of the models were simplified by using the corresponding important principal components as input instead of the original spectra. Number of inputs and hidden nodes, learning rate, transfer functions and number of epochs and SPREAD values were optimized. Performances of methods were tested with root mean square errors prediction (RMSEP, %), using synthetic solutions. The obtained satisfactory results indicate the applicability of this ANN approach based on PCA input selection for determination in highly spectral overlapping. The results obtained by FFNNs and by RBF networks were compared. The applicability of methods was investigated for synthetic samples, for detergent formulations, and for a river water sample.
Indian Academy of Sciences (India)
Izzet Y Önel; K Burak Dalci; İbrahim Senol
2006-06-01
This paper investigates the application of induction motor stator current signature analysis (MCSA) using Park’s transform for the detection of rolling element bearing damages in three-phase induction motor. The paper ﬁrst discusses bearing faults and Park’s transform, and then gives a brief overview of the radial basis function (RBF) neural networks algorithm. Finally, system information and the experimental results are presented. Data acquisition and Park’s transform algorithm are achieved by using LabVIEW and the neural network algorithm is achieved by using MATLAB programming language. Experimental results show that it is possible to detect bearing damage in induction motors using an ANN algorithm.
Directory of Open Access Journals (Sweden)
Shiva Kumar
2012-01-01
Full Text Available Radial basis function neural networks (RBFNNs, which is a relatively new class of neural networks, have been investigated for their applicability for prediction of performance and emission characteristics of a diesel engine fuelled with waste cooking oil (WCO. The RBF networks were trained using the experimental data, where in load percentage, compression ratio, blend percentage, injection timing, and injection pressure were taken as the input parameters, and brake thermal efficiency (BTE, brake specific energy consumption (BSEC, exhaust gas temperature (, and engine emissions were used as the output parameters. The number of RBF centers was selected randomly. The network was initially trained using variable width values for the RBF units using a heuristic and then was trained by using fixed width values. Studies showed that RBFNN predicted results matched well with the experimental results over a wide range of operating conditions. Prediction accuracy for all the output parameters was above 90% in case of performance parameters and above 70% in case of emission parameters.
Institute of Scientific and Technical Information of China (English)
陈刚; 田志平
2011-01-01
针对电力系统预想事故自动选择问题,提出了一种基于模糊推理系统FIS(fuzzy inference system)和径向基函数网络RBFN(radial basis function network)算法.定义了一种有功行为指标PIpf,该指标添加了一个模糊补偿系数用以改善遮蔽现象；同时构造了一个三层的RBFN,该网络以发电机功率、负荷功率和网络拓扑结构作为输入,以PIpf作为输出,并通过离线潮流计算获得训练样本；对算例进行计算并与其他算法比较,结果显示该算法能使事故排序更为合理,且计算精度和速度都令人满意.%In view of the problems of automatic contingency selection of power system, an advanced algorithm is proposed, which is based on fuzzy inference system(FIS) and radial basis function network(RBFN). Firstly an active performance index is defined, which adds a fuzzy compensation factor coefficient to improve shelter phenomenon. Meanwhile a three-layer RBFN is constructed, which treats generator power, load power and network topology as inputs, while treats the active performance index as output. The results of off-line load flow calculation are used to train the RBFN. Finally, the proposed method is demonstrated by an example, compared with several other algorithms. And the results show that the ranking of contingency is much more reasonable, and the calculation accuracy and speed are satisfied.
Afkhami, Abbas; Abbasi-Tarighat, Maryam; Bahram, Morteza
2008-03-15
In this work feed-forward neural networks and radial basis function networks were used for the determination of enantiomeric composition of alpha-phenylglycine using UV spectra of cyclodextrin host-guest complexes and the data provided by two techniques were compared. Wavelet transformation (WT) and principal component analysis (PCA) were used for data compression prior to neural network construction and their efficiencies were compared. The structures of the wavelet transformation-radial basis function networks (WT-RBFNs) and wavelet transformation-feed-forward neural networks (WT-FFNNs), were simplified by using the corresponding wavelet coefficients of three mother wavelets (Mexican hat, daubechies and symlets). Dilation parameters, number of inputs, hidden nodes, learning rate, transfer functions, number of epochs and SPREAD values were optimized. Performances of the proposed methods were tested with regard to root mean square errors of prediction (RMSE%), using synthetic solutions containing a fixed concentration of beta-cyclodextrin (beta-CD) and fixed concentration of alpha-phenylglycine (alpha-Gly) with different enantiomeric compositions. Although satisfactory results with regard to some statistical parameters were obtained for all the investigated methods but the best results were achieved by WT-RBFNs.
Seerat Fatima; Prof. Dr. Mujahid Ali; Sheraz Arif
2011-01-01
The purpose of this study is to classify the networks according to functions they performed, especially scrutinize their structures. The research concentrates on the influence of these functional networks on the internationalization process of small and medium sized companies (SME) in developing countries. What are the different types of support being provided by network partners? What is the structure of the existing network? The research part is inductive, qualitative and based on case stud...
改进RBF神经网络用于降雨量预测%PRECIPITATION PREDICTING BASED ON IMPROVED RADIAL BASIS FUNCTION NEURAL NETWORKS
Institute of Scientific and Technical Information of China (English)
周佩玲; 陶小丽; 傅忠谦; 彭虎; 王新跃
2001-01-01
利用基于GA的改进RBF网络建立了为时间序列为对象的预测模型，并提出了基于模型的数据处理方法，在此基础上，对安徽省蚌埠地区42年来6—8月份的降水量进行预测，结果表明了该模型在时间序列预测中有良好的推广和应用能力.%The paper suggests the forecasting model about objects that havetime sequence by using improved Radial Basis Function network model based on GA and data proceding method based on the model,and verifies it through precipitation pre dicting . A good product is obtained and proved its well spread and application.
Institute of Scientific and Technical Information of China (English)
Li-juan XIE; Xing-qian YE; Dong-hong LIU; Yi-bin YING
2008-01-01
Near-infrared (NIR) spectroscopy combined with chemometrics techniques was used to classify the pure bayberry juice and the one adulterated with 10% (w/w) and 20% (w/w) water. Principal component analysis (PCA) was applied to reduce the dimensions of spectral data, give information regarding a potential capability of separation of objects, and provide principal component (PC) scores for radial basis function neural networks (RBFNN). RBFNN was used to detect bayberry juice adulterant. Multiplicative scatter correction (MSC) and standard normal variate (SNV) transformation were used to preprocess spectra. The results demonstrate that PC-RBFNN with optimum parameters can separate pure bayberry juice samples from water-adulterated bayberry at a recognition rate of 97.62%, but cannot clearly detect water levels in the adulterated bayberry juice. We conclude that NIR technology can be successfully applied to detect water-adulterated bayberry juice.
International Nuclear Information System (INIS)
Highlights: • It is presented a new method based on Artificial Neural Network (ANN) developed to deal with accident identification in PWR nuclear power plants. • Obtained results have shown the efficiency of the referred technique. • Results obtained with this method are as good as or even better to similar optimization tools available in the literature. - Abstract: The task of monitoring a nuclear power plant consists on determining, continuously and in real time, the state of the plant’s systems in such a way to give indications of abnormalities to the operators and enable them to recognize anomalies in system behavior. The monitoring is based on readings of a large number of meters and alarm indicators which are located in the main control room of the facility. On the occurrence of a transient or of an accident on the nuclear power plant, even the most experienced operators can be confronted with conflicting indications due to the interactions between the various components of the plant systems; since a disturbance of a system can cause disturbances on another plant system, thus the operator may not be able to distinguish what is cause and what is the effect. This cognitive overload, to which operators are submitted, causes a difficulty in understanding clearly the indication of an abnormality in its initial phase of development and in taking the appropriate and immediate corrective actions to face the system failure. With this in mind, computerized monitoring systems based on artificial intelligence that could help the operators to detect and diagnose these failures have been devised and have been the subject of research. Among the techniques that can be used in such development, radial basis functions (RBFs) neural networks play an important role due to the fact that they are able to provide good approximations to functions of a finite number of real variables. This paper aims to present an application of a neural network of Gaussian radial basis
International Nuclear Information System (INIS)
On the assumption that random interruptions in the observation process are modeled by a sequence of independent Bernoulli random variables, we firstly generalize two kinds of nonlinear filtering methods with random interruption failures in the observation based on the extended Kalman filtering (EKF) and the unscented Kalman filtering (UKF), which were shortened as GEKF and GUKF in this paper, respectively. Then the nonlinear filtering model is established by using the radial basis function neural network (RBFNN) prototypes and the network weights as state equation and the output of RBFNN to present the observation equation. Finally, we take the filtering problem under missing observed data as a special case of nonlinear filtering with random intermittent failures by setting each missing data to be zero without needing to pre-estimate the missing data, and use the GEKF-based RBFNN and the GUKF-based RBFNN to predict the ground radioactivity time series with missing data. Experimental results demonstrate that the prediction results of GUKF-based RBFNN accord well with the real ground radioactivity time series while the prediction results of GEKF-based RBFNN are divergent. (geophysics, astronomy, and astrophysics)
Institute of Scientific and Technical Information of China (English)
DU Lin-na; WU Li-hang; LU Jia-hui; GUO Wei-liang; MENG Qing-fan; JIANG Chao-jun; SHEN Si-le; TENG Li-rong
2007-01-01
Partial least squares(PLS), back-propagation neural network (BPNN) and radial basis function neural network(RBFNN) were respectively used for estalishing quantative analysis models with near infrared(NIR) diffuse reflectance spectra for determining the contents of rifampincin(RMP), isoniazid(INH) and pyrazinamide(PZA) in rifampicin isoniazid and pyrazinamide tablets. Savitzky-Golay smoothing, first derivative, second derivative, fast Fourier transform(FFT) and standard normal variate(SNV) transformation methods were applied to pretreating raw NIR diffuse reflectance spectra. The raw and pretreated spectra were divided into several regions, depending on the average spectrum and RSD spectrum. Principal component analysis(PCA) method was used for analyzing the raw and pretreated spectra in different regions in order to reduce the dimensions of input data. The optimum spectral regions and the models' parameters were chosen by comparing the root mean square error of cross-validation(RMSECV) values which were obtained by leave-one-out cross-validation method. The RMSECV values of the RBFNN models for determining the contents of RMP, INH and PZA were 0.00288, 0.00226 and 0.00341, respectively. Using these models for predicting the contents of INH, RMP and PZA in prediction set, the RMSEP values were 0.00266, 0.00227 and 0.00411, respectively. These results are better than those obtained from PLS models and BPNN models. With additional advantages of fast calculation speed and less dependence on the initial conditions, RBFNN is a suitable tool to model complex systems.
Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J
2015-01-01
In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron. PMID:25849483
一种由RBF网络驱动的模糊产生式系统%A Fuzzy Production System Driven by Radial Basis Function Networks
Institute of Scientific and Technical Information of China (English)
王长琼; 孙国正
2000-01-01
Radial Basis Function Networks(RBFN)and fuzzy production systems are introduced.The conditions by which RBFN can drive fuzzy systems are analyzed,and a new learning scheme combining the unsupervised and the supervised algorithm is presented for training RBFN to determine the structure and the parameters of fuzzy systems.Using the new learning scheme,the number of fuzzy rules and parameters of the rules can be determined simultaneously.Simulation illustrates the effectiveness of the method.%介绍了RBF网络和模糊产生式系统.分析了由RBF网络实现模糊产生式系统的条件,并研究了RBF网络的训练算法,以确定模糊系统的结构和参数.运用这种学习算法,可同时确定模糊系统规则的数目和规则的参数.仿真结果说明了该方法的有效性.
Institute of Scientific and Technical Information of China (English)
Bi Jun; Shao Sai; Guan Wei; Wang Lu
2012-01-01
The on-line estimation of the state of charge (SOC) of the batteries is important for the reliable running of the pure electric vehicle in practice.Because a nonlinear feature exists in the batteries and the radial-basis-function neural network (RBF NN) has good characteristics to solve the nonlinear problem,a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed.Firstly,in this paper,the model of on-line SOC estimation with the RBF NN is set.Secondly,four important factors for estimating the SOC are confirmed based on the contribution analysis method,which simplifies the input variables of the RBF NN and enhances the real-time performance of estimation.Finally,the pure electric buses with LiFePO4Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object.The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.
Directory of Open Access Journals (Sweden)
Zhiqiang Guo
Full Text Available In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D2PCA and a Radial Basis Function Neural Network (RBFNN to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA and independent component analysis (ICA. The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.
Institute of Scientific and Technical Information of China (English)
刘朝晖; 黄榕波; 陈庆强; 温预关; 李明亚
2011-01-01
目的:评价用径向基(RBF)神经网络所建立的预测氯氮平稳态血药浓度模型的预测性能.方法:将数据分为训练集、校验集和测试集来建立获取输入、输出变量两者间关系的RBF网络模型,其中以患者的性别、年龄、体重、剂量、血压、多项生理生化指标等37项参数为输入变量,氯氮平稳态血药浓度为输出变量.用训练集和校验集的网络计算输出值与目标输出值之间的均方差(MSE)和相关系数(R)来综合评价网络模型的学习效果,用测试集的网络计算输出值与目标输出值之间的MSE和R来评价网络模型的预测性能.结果:当扩展系数(SP)值为3.0时,训练集的MSE为1.33 ×10(-5),R值为0.99985,校验集的MSE为0.002 833,R值为0.971 86,测试集的MSE为0.005 439,R值为0.93676,网络模型的预测效果和泛化能力较好.结论:RBF网络用于预测氯氮平稳态血药浓度的研究是可行和有效的.%OBJECTIVE: To evaluate the performance of a model for predicting the steady-state plasma concentration of clozapine established by radial basis function (RBF) neural network. METHODS: The data was divided into training set, validation set and test set to establish the RBF neural network model which had obtained the relationships between input variables and output variable. Input variables included 37 parameters, such as patients' gender, age, body weight, dosage, blood pressure and multiple physiological and biochemical indexes. Output variable was steady-state plasma concentration of clozapine. The effect of RBF neural network model was evaluated comprehensively using mean square (MSE) and coefficient correlation (R) between the computed output value and objective output value of training set and validation set. And predictive performance of the model was evaluated by MSE and R between the computed output value and objective output value of test set. RESULTS: When the value of SP was 3.0, the MSE and R values of the
Cerveri, P; Forlani, C; Pedotti, A; Ferrigno, G
2003-03-01
Global polynomial (GP) methods have been widely used to correct geometric image distortion of small-size (up to 30 cm) X-ray image intensifiers (XRIIs). This work confirms that this kind of approach is suitable for 40 cm XRIIs (now increasingly used). Nonetheless, two local methods, namely 3rd-order local un-warping polynomials (LUPs) and hierarchical radial basis function (HRBF) networks are proposed as alternative solutions. Extensive experimental tests were carried out to compare these methods with classical low-order local polynomial and GP techniques, in terms of residual error (RMSE) measured at points not used for parameter estimation. Simulations showed that the LUP and HRBF methods had accuracies comparable with that attained using GP methods. In detail, the LUP method (0.353 microm) performed worse than HRBF (0.348 microm) only for small grid spacing (15 x 15 control points); the accuracy of both HRBF (0.157 microm) and LUP (0.160 microm) methods was little affected by local distortions (30 x 30 control points); weak local distortions made the GP method poorer (0.320 microm). Tests on real data showed that LUP and HRBF had accuracies comparable with that of GP for both 30 cm (GP: 0.238 microm; LUP: 0.240 microm; HRBF: 0.238 microm) and 40 cm (GP: 0.164 microm; LUP: 0.164 microm; HRBF: 0.164 microm) XRIIs. The LUP-based distortion correction was implemented in real time for image correction in digital tomography applications.
Conventional modeling of the multilayer perceptron using polynomial basis functions
Chen, Mu-Song; Manry, Michael T.
1993-01-01
A technique for modeling the multilayer perceptron (MLP) neural network, in which input and hidden units are represented by polynomial basis functions (PBFs), is presented. The MLP output is expressed as a linear combination of the PBFs and can therefore be expressed as a polynomial function of its inputs. Thus, the MLP is isomorphic to conventional polynomial discriminant classifiers or Volterra filters. The modeling technique was successfully applied to several trained MLP networks.
Hassanzadeh, Zeinabe; Kompany-Zareh, Mohsen; Ghavami, Raouf; Gholami, Somayeh; Malek-Khatabi, Atefe
2015-10-01
The configuring of a radial basis function neural network (RBFN) consists of optimizing the architecture and the network parameters (centers, widths, and weights). Methods such as genetic algorithm (GA), K-means and cluster analysis (CA) are among center selection methods. In the most of reports on RBFN modeling optimum centers are selected among rows of descriptors matrix. A combination of RBFN and GA is introduced for better description of quantitative structure-property relationships (QSPR) models. In this method, centers are not exactly rows of the independent matrix and can be located in any point of the samples space. In the proposed approach, initial centers are randomly selected from the calibration set. Then GA changes the locations of the initially selected centers to find the optimum positions of centers from the whole space of scores matrix, in order to obtain highest prediction ability. This approach is called whole space GA-RBFN (wsGA-RBFN) and applied to predict the adsorption coefficients (logk), of 40 small molecules on the surface of multi-walled carbon nanotubes (MWCNTs). The data consists of five solute descriptors [R, π, α, β, V] of the molecules and known as data set1. Prediction ability of wsGA-RBFN is compared to GA-RBFN and MLR models. The obtained Q2 values for wsGA-RBFN, GA-RBFN and MLR are 0.95, 0.85, and 0.78, respectively, which shows the merit of wsGA-RBFN. The method is also applied on the logarithm of surface area normalized adsorption coefficients (logKSA), of organic compounds (OCs) on MWCNTs surface. The data set2 includes 69 aromatic molecules with 13 physicochemical properties of the OCs. Thirty-nine of these molecules were similar to those of data set1 and the others were aromatic compounds included of small and big molecules. Prediction ability of wsGA-RBFN for second data set was compared to GA-RBF. The Q2 values for wsGA-RBFN and GA-RBF are obtained as 0.89 and 0.80, respectively.
径向基函数预测网络拓扑确定新方法%Topology Determination Method for Radial Basis Function Prediction Network
Institute of Scientific and Technical Information of China (English)
吕永乐
2012-01-01
预测能力相对薄弱,已经成为制约PHM(Prognostics and Health Management)技术发展和应用的瓶颈.随着传感器和BIT (Built-in Test)设计技术的日益进步,采用序列分析的方法对复杂系统装备进行故障预测已经成为可能.在基于序列分析的预测方法研究中,径向基函数预测网络具有结构简单、学习速度快、具备非线性建模能力等诸多优点.为了改进其预测性能,在深入分析网络拓扑对模型性能及建模时间影响的基础上,综合考察了序列最佳线性自相关长度、建模精度和模型复杂度等多种因素,提出了基于偏自相关函数统计检测的输入层节点数目确定算法和基于BIC(Bayesian Information Criteria)准则的隐层节点数目确定算法,用以构建径向基函数预测网络；并对算法的有效性进行了分析.仿真结果表明,同传统建模算法相比较,由新算法构建的径向基函数预测网络具有最佳的预测性能,且建模时间不足传统算法的3％.%With the progress of sensor and built-in test (BIT) technology, it is realizable to prognose the health status of complicated system by employing the methodology of time series analysis. In the research of prediction methods based on time series, the radial basis function prediction network (RBFPN) was widely paid attention because of the merits such as brief construction, fast learning and the nonlinear modeling ability. In order to improve the RBFPN prediction performance, the influence of network topology on the model performance and the consumed modeling time was analyzed. After that, the factors including optimal linear correlation length, the modeling precision and complexity were researched, and then the number decision algorithm of input layer nodes based on partial autocorrelation function statistical testing and the neuron number decision algorithm of hidden layer based on the Bayesian information criteria were respectively put forward to
Dynamic programming using radial basis functions
Junge, Oliver; Schreiber, Alex
2014-01-01
We propose a discretization of the optimality principle in dynamic programming based on radial basis functions and Shepard's moving least squares approximation method. We prove convergence of the approximate optimal value function to the true one and present several numerical experiments.
Generalized Network Externality Function
A. Paothong; G.S. Ladde
2012-01-01
In this work, we focus on the development of mathematical modeling of network externality processes. The introduction of the generalized network externality function provides a unified source of a tool for developing and analyzing the planning, policy and performance of the network externality process and network goods/services in a systematic way. This leads to fulfill all existing network externality assumptions as special cases. We study its properties and applications. This study provides...
Luengo, Julián; García, Salvador; Herrera, Francisco
2010-04-01
The presence of Missing Values in a data set can affect the performance of a classifier constructed using that data set as a training sample. Several methods have been proposed to treat missing data and the one used more frequently is the imputation of the Missing Values of an instance. In this paper, we analyze the improvement of performance on Radial Basis Function Networks by means of the use of several imputation methods in the classification task with missing values. The study has been conducted using data sets with real Missing Values, and data sets with artificial Missing Values. The results obtained show that EventCovering offers a very good synergy with Radial Basis Function Networks. It allows us to overcome the negative impact of the presence of Missing Values to a certain degree.
Five Lectures on Radial Basis Functions
DEFF Research Database (Denmark)
Powell, Mike J.D.
2005-01-01
Professor Mike J. D. Powell spent three weeks at IMM in November - December 2004. During the visit he gave five lectures on radial basis functions. These notes are a TeXified version of his hand-outs, made by Hans Bruun Nielsen, IMM.......Professor Mike J. D. Powell spent three weeks at IMM in November - December 2004. During the visit he gave five lectures on radial basis functions. These notes are a TeXified version of his hand-outs, made by Hans Bruun Nielsen, IMM....
Wideband Characteristic Basis Functions in Radiation Problems
Directory of Open Access Journals (Sweden)
A. A. Kucharski
2012-06-01
Full Text Available In this paper, the use of characteristic basis function (CBF method, augmented by the application of asymptotic waveform evaluation (AWE technique is analyzed in the context of the application to radiation problems. Both conventional and wideband CBFs are applied to the analysis of wire and planar antennas.
Analysis of radial basis function interpolation approach
Institute of Scientific and Technical Information of China (English)
Zou You-Long; Hu Fa-Long; Zhou Can-Can; Li Chao-Liu; Dunn Keh-Jim
2013-01-01
The radial basis function (RBF) interpolation approach proposed by Freedman is used to solve inverse problems encountered in well-logging and other petrophysical issues. The approach is to predict petrophysical properties in the laboratory on the basis of physical rock datasets, which include the formation factor, viscosity, permeability, and molecular composition. However, this approach does not consider the effect of spatial distribution of the calibration data on the interpolation result. This study proposes a new RBF interpolation approach based on the Freedman's RBF interpolation approach, by which the unit basis functions are uniformly populated in the space domain. The inverse results of the two approaches are comparatively analyzed by using our datasets. We determine that although the interpolation effects of the two approaches are equivalent, the new approach is more flexible and beneficial for reducing the number of basis functions when the database is large, resulting in simplification of the interpolation function expression. However, the predicted results of the central data are not sufficiently satisfied when the data clusters are far apart.
Institute of Scientific and Technical Information of China (English)
陈向东; 唐景山; 宋爱国
2000-01-01
In this paper,an improved radial basis function networks named hidden neuron modifiable radial basis function (IINMRBF) networks is proposed for target classification,and evolutionary programming (EP) is used as a learning algorithm to determine and modify the hidden neuron of HNMRBF nets.The result of passive sonar target classification shows that HNMRBF nets can effectively solve the problem of traditional neural networks,i.e.learning new target patterns on-line will cause forgetting of the old patterns.%本文提出了一种改进的称为隐神经元可调径向基函数神经网络(HNMRBF)，并且应用进化规划算法作为聚类算法来决定和修改HNMRBF网络的隐神经元.最后，我们使用基于进化规划算法的HMRBF网络来进行被动声纳信号目标的分类，结果表明HNMRBF网络能有效地解决用传统神经网络时所遇到的问题，即在线学习新的目标模式时往往会遗忘旧的模式.
Institute of Scientific and Technical Information of China (English)
翟红林; 陈晓峰; 陈兴国; 胡之德
2004-01-01
结合了径向基神经网络较强模式分类能力与概率神经网络运算简单的优点,提出了一种径向基概率神经网络模型,并应用于小儿厌食症的辅助诊断,通过对119例样本数据的处理,获得了92.4%的准确率.此外,偏最小二乘法的分析结果表明,Zn元素与小儿厌食症关系最为紧密.%Based on a radial basis function probabilistic neural network model, which combined the powerful capability of the pattern classification of radial basis function neural network and the simple operation of probabilistic neural network, a new approach of assisted diagnosis for infancy anorexia was developed and applied to 119 samples, with an accuracy rate of 92%. In addition, the result of partial least squares analysis indicated that Zn was the most important element that was closely related to infancy anorexia..
Spherical radial basis functions, theory and applications
Hubbert, Simon; Morton, Tanya M
2015-01-01
This book is the first to be devoted to the theory and applications of spherical (radial) basis functions (SBFs), which is rapidly emerging as one of the most promising techniques for solving problems where approximations are needed on the surface of a sphere. The aim of the book is to provide enough theoretical and practical details for the reader to be able to implement the SBF methods to solve real world problems. The authors stress the close connection between the theory of SBFs and that of the more well-known family of radial basis functions (RBFs), which are well-established tools for solving approximation theory problems on more general domains. The unique solvability of the SBF interpolation method for data fitting problems is established and an in-depth investigation of its accuracy is provided. Two chapters are devoted to partial differential equations (PDEs). One deals with the practical implementation of an SBF-based solution to an elliptic PDE and another which describes an SBF approach for solvi...
Institute of Scientific and Technical Information of China (English)
黎峻宇; 刘立龙; 蔡成辉; 林国标; 黄良珂
2015-01-01
The necessity of data pretreatment to the elevation fitting on radial basis function network modeling is described.The basic principle and implementation steps of the conversion modeling is depicted based on radial basis function networks elevation.With engineering examples,normalization,centralization,standardization of three different data pretreatment methods is used to calculate the impact of good conversion on radial basis func-tion network.It is shown that data pretreatment model can achieve better conversion accuracy in a large area with elevation changes over the outliers.Different pretreatment methods result in different conversion.The cen-tralization can improve the elevation accuracy on radial conversion function network in a greater degree.Normal-ization wouldn't be the best data pretreatment method.It is necessary for the selection of pretreatment method before elevation conversion.%介绍了数据预处理对基于径向基函数网络的高程转换的必要性，提出了基于径向基函数网络高程转换建模的基本原理和实现步骤。结合工程实例，重点对归一化、中心化、标准化3种不同的数据预处理方法对基于径向基函数网络高程转换的影响进行计算分析。结果表明：针对大面积、高程异常值变化较大的区域，经过数据预处理后模型能够达到更高的转换精度；不同的预处理方法对转换结果影响不同，中心化预处理较其他两种方法能够更大程度地提高径向基函数网络高程转换的精度；归一化预处理方法不一定是最优的，进行高程转换前有必要对预处理方法进行选择。
Basis Function Sampling for Material Property Computations
Whitmer, Jonathan K.; Chiu, Chi-Cheng; Joshi, Abhijeet A.; de Pablo, Juan J.
2014-03-01
Wang-Landau sampling, and the associated class of flat histogram simulation methods, have been particularly successful for free energy calculations in a wide array of physical systems. Practically, the convergence of these calculations to a target free energy surface is hampered by reliance on parameters which are unknown a priori. We derive and implement a method based on orthogonal (basis) functions which is fast, parameter-free, and geometrically robust. An important feature of this method is its ability to achieve arbitrary levels of description for the free energy. It is thus ideally suited to in silico measurement of elastic moduli and other quantities related to free energy perturbations. We demonstrate the utility of such applications by applying our method to calculation of the Frank elastic constants of the Lebwohl-Lasher model.
Urban Ecosystem Pressure Based on Radial Basis Function Neural Network%基于径向基函数神经网络方法的城市生态压力预测
Institute of Scientific and Technical Information of China (English)
吴明; 姚尧; 贾冯睿; 王雷; 高艳波
2013-01-01
针对城市生态压力影响因素复杂,难以对城市未来可持续发展状况做出准确判断的问题,提出了城市生态压力的径向基函数神经网络预测模型,分析了影响城市生态系统的主要因素.以抚顺市1995-2009年数据为基础,验证了模型的准确性并预测了该市2010-2015年城市生态系统的压力情况.研究结果表明:能源消耗指标是影响城市生态系统压力的主要因素；运用径向基函数神经网络模型对训练样本的拟合精度以及对测试样本的仿真精度分别达97.91％和94.16％；抚顺市2015年的人均生态足迹、生态承载力和生态赤字分别达到7.013、0.523和6.49 hm2/人.%Considering the difficulty in the estimation of sustainable development due to the complex ecological pressure influencing factors, this paper proposed the radial basis function neural network model for predicting urban ecological system pressure and analyzed the primary influencing factors on urban ecological system pressure. The model was studied on the basis of Fushun' s data during 1995 -2009 , the accuracy of the radial basis function neural network prediction model is validated and then the situation of urban ecological system pressure was predicted from 2010 to 2015. The results of the study showed that the energy consumption indicator was the primary influencing factor for urban ecological system pressure; the fitting and simulation precision for training and testing samples were 97. 91% and 94. 16% by using radial basis function neural network model, respectively; the ecological footprint, the ecological carrying capacity and the ecological deficit would be 7. 013 , 0. 523 and 6. 49 hmVcap for Fushun in 2015 , respectively.
The Role of the Basis Set: Assessing Density Functional Theory
Boese, A D; Handy, N C; Martin, Jan M. L.; Handy, Nicholas C.
2003-01-01
When developing and assessing density functional theory methods, a finite basis set is usually employed. In most cases, however, the issue of basis set dependency is neglected. Here, we assess several basis sets and functionals. In addition, the dependency of the semiempirical fits to a given basis set for a generalised gradient approximation and a hybrid functional is investigated. The resulting functionals are then tested for other basis sets, evaluating their errors and transferability.
Institute of Scientific and Technical Information of China (English)
樊明璐; 陈旻; 张义平; 罗迪
2014-01-01
提出一种用于解决递推估计问题的优化算法，该算法基于递推粒子群优化。递推估计问题获得的数据并非一次性获得，而是陆续获得。在递推的粒子群算法中，粒子基于过去的数据信息和新获取的数据递推地更新自己位置。实验结果表明，基于递推算法的径向基函数网络模拟系统只需要较少的径向基函数，同时在解决动态问题时能够比传统粒子群算法获得更准确的结果。%A Recursive Particle Swarm Optimization (R-PSO)is proposed to solve dynamic optimization problems where the data is ob-tained not once but one by one.In R-PSO,the position of each particle swarm is updated recursively based on the continuous data and the historical knowledge.The experiment results indicate that RPSO-based radial basis function networks needs fewer radial basis func-tions and meanwhile gives more accurate results than traditional PSO in solving dynamic problems.
Institute of Scientific and Technical Information of China (English)
张邦礼; 王小平; 杨小文
2001-01-01
连铸板坯缺陷形成的诸多影响因素之间的关系错综复杂，造成缺陷预报和诊断都非常困难.提出用粗糙集理论简化这些因素，然后采用径向基函数网络对连铸板坯缺陷进行预报诊断.仿真实验证明了该方法的有效性、先进性、实用性.%The relation of the factors which cause the continuous casting slab defect are anfractuous ,which cause the prediction and diagnosis of the defect diffcult. This research uses rough sets to reduce this factors ,then adopts radial basis function networks to predict and diagnose the continuous casting slab defect .The experiment indicates the validity advancement and practicability of this method.
Institute of Scientific and Technical Information of China (English)
李昕; 郑宇; 江芳泽
2002-01-01
The performance of speaker verification systems is often compromised under real-world environments. For example, variations in handset characteristics could cause severe performance degradation. This paper presents a novel method to overcome this problem by using a non-linear handset mapper. Under this method, a mapper is constructed by training an elliptical basis function network using distorted speech features as inputs and the corresponding clean features as the desired outputs. During feature recuperation, clean features are recovered by feeding the distorted features to the feature mapper. The recovered features are then presented to a speaker model as if they were derived from clean speech. Experimental evaluations based on 258 speakers of the TIMIT and NTIMIT corpuses suggest that the feature mappers improve the verification performance remarkably.
Institute of Scientific and Technical Information of China (English)
徐圆; 冯晶; 朱群雄
2011-01-01
针对径向基函数（RBF）神经网络构造时其结构和参数难以确定的问题,结合可拓理论对输入样本和基函数的中心向量建立物元模型,并借鉴第2类型可拓神经网络（ENN2）的聚类思想,根据样本分布,采用可拓分析及可拓变换动态调整隐节点数目和基函数中心,从而提出基于可拓理论的RBF（ERBF）神经网络.同时,通过UCI标准数据集进行了测试,并通过应用实例进行了验证,结果表明,ERBF结构和参数的确定方法简单、收敛速度快,且泛化精度、鲁棒性和稳定性均显著提高.%During the construction process of radical basis function（RBF） neural network,the structure and parameters are hard to be determined.Therefore,combining with the extension theory,an extension theory-based RBF（ERBF） neural network is proposed,in which the matter-element models including input samples and center vectors of the basis function are established,the clustering method of extension neural network type 2（ENN2） is introduced,and the hidden layer nodes number and center vectors of the basis function are dynamically adjusted by using extension analysis and extension transformation according to the sample distribution.Meanwhile,UCI standard data sets are tested,and application object is validated.Through the verification and comparison,the proposed ERBF algorithm has the advantages of simple calculation and fast convergence,which significantly enhances the generalization accuracy,robustness and stability.
涡喷发动机风车启动工况的神经网络建模%Turbojet modeling in windmilling based on radial basis function networks
Institute of Scientific and Technical Information of China (English)
于达仁; 郭钰锋; 牛军; 史新兴; 何保成
2001-01-01
弹用涡喷发动机的风车启动工况是复杂的非线性过程，由于此时压气机处于非设计工况(膨胀)而造成机理建模的困难。神经网络对于非线性映射具有任意逼近能力，应用径向基函数神经网络（RBFN）对涡喷发动机风车启动阶段进行了实验建模，通过适当地选取网络参数及训练样本，达到了很高的精度，对确定发动机可靠点火点和启动过程仿真等都有一定的价值。%The windmilling process of missile turbojet is such a complex nonlinear process that to obtain its dynamic model theoretically is very difficult , because the compressor works in expending mode ( non-normal operating mode) in this condition. Considering the great capacity of handling nonlinearity of the neural network , an experimental model of the windmilling process using radial basis function networks (RBFN) was established and a good precision through selecting the parameters and the training samples of the network properly was gained. The neural network model is of great value for computing the point of ignition or simulating the windmilling process.
Institute of Scientific and Technical Information of China (English)
刘朝晖; 黄榕波; 陈庆强; 温预关; 李明亚
2012-01-01
目的 评价用径向基(RBF)神经网络所建立的预测氯丙嗪稳态血药浓度模型的预测性能.方法 将数据分为训练集、校验集和测试集,来建立获取输出变量(37项参数)与输出变量(氯丙嗪稳态血药浓度)两者间关系的RBF网络模型,并评价其预测性能.结果 当扩展速度(SP)值为2.8时,所建立的RBF网络模型,预测奋乃静稳态血药浓度的效果和泛化能力较好.结论 RBF网络用于预测氯丙嗪稳态血药浓度是可行的和有效的.%Objective To evaluate the performance of a model for predicting the steady - state plasma concentration of chlorpromazine established by using radial basis function (RBF) neural network. Methods The data was divided into training set, validation set and test set to establish the RBF neural network model which had captured the relationships between the input variables (37 parametes) and the output variable ( steady - state plasma concentration of chlorpromazine) and evaluate predictive performance of the model. Results When the SPREAD (SP) value was 2. 8, the RBF neural network model had the better effect on predicting the steady - state plasma concentration of chlorpromazine and better generalization. Conclusion It is practical and valid for RBF neural network model to be applied to the study of steady - state plasma concentration prediction of chlorpromazine.
Institute of Scientific and Technical Information of China (English)
陈晓艳; 董朝轶; 刘月文
2011-01-01
农作物疾病的人工诊断效果常受到个人诊断经验和能力的限制,无法达到最令人满意的诊断结果.将丰富的植物病理学诊断经验和知识编入专家系统,利用模式识别算法对农作物常见疾病进行诊断,可以大大提高诊断准确率,显著地提高其产量和质量.本文研究基于一种人工神经元网络(Artificial Neural Network,ANN)--径向基函数(Radial BasisFunction,RBF)网络的模式识别技术在大豆疾病诊断中的应用.径向基函数神经网络是基于人脑的神经元细胞对外界反应的局部性而提出的一种前馈式神经网络,这种网络具有结构简单、全局逼近能力强、训练方法快速易行的优点.本文首先对大豆常见19种疾病症状进行收集和整理,构建试验样本集.然后利用人工神经元网络理论,建立基于径向基函数(RBF)的网络模型,实现对该网络的训练和测试.测试结果表明,该模型具有较高的农作物疾病诊断正确率和良好的泛化能力.%The manual diagnosis for the diseases of agricultural crops is often restricted by the individual ability and experiences so that one cannot obtain the precise results of diagnosis. To overcome this pitfall, the merge of expert systems with the rich pathological knowledge and the utilization of pattern recognition algorithm can significantly improve the precision of diagnosis. Therefore, it greatly increases the quantity and the quality of crop's production. In this paper, a pattern recognition technique, based on a radial basis function ( RBF) neural network is applied to the diagnosis of soybean diseases. The RBF neural network, which is a novel and efficient feed-forward network, is based on the local reflections of cortical neurons on the external stimulus. This network possesses variety of characteristics, for example, the simple structure, strong global convergence, and fast-speed training behavior, which together make RBF network to be used widely
Learning Mixtures of Truncated Basis Functions from Data
DEFF Research Database (Denmark)
Langseth, Helge; Nielsen, Thomas Dyhre; Pérez-Bernabé, Inmaculada;
2014-01-01
-likelihood), they are significantly faster, and therefore indicate that the MoTBF framework can be used for inference and learning in reasonably sized domains. Furthermore, we show how a particular sub- class of MoTBF potentials (learnable by the proposed methods) can be exploited to significantly reduce complexity during inference.......In this paper we investigate methods for learning hybrid Bayesian networks from data. First we utilize a kernel density estimate of the data in order to translate the data into a mixture of truncated basis functions (MoTBF) representation using a convex optimization technique. When utilizing...... propose an alternative learning method that relies on the cumulative distribution function of the data. Empirical results demonstrate the usefulness of the approaches: Even though the methods produce estimators that are slightly poorer than the state of the art (in terms of log...
Institute of Scientific and Technical Information of China (English)
罗青; 李智军; 等
2002-01-01
与监督学习从范例中学习的方式不同,强化学习不需要先验知识,而是具有从经验中学习的能力.将强化学习应用在大状态空间中,必须应用函数逼近的方法,如使用径向基函数网络建立输入和输出的映射关系.本文对基于径向基函数网络的强化学习在机器人足球这样的动态的多智能体环境中的应用进行了研究.实验结果证明了研究方法的可行性.%Reinforcement learning has the ability to learn from experience as opposed to supervised learning which learns from examples. Application of reinforcement learning to large state spaces necessitates the use of function approximators like Radial Basis Function Networks (RBFNs) to map between inputs and outputs. This study investigates the applicability of RBFNs based reinforcement learning methods in a dynamic multi-agent scenario of robot soccer. And the result of experiment proves that is a suitable approach.
Face Detection Based on Skin Color Model and Radial Basis Function Network%基于肤色模型和径向基函数网络的脸部检测
Institute of Scientific and Technical Information of China (English)
陈栋; 王丽荣
2014-01-01
The calculation of the closure of human eyes is commonly adopted to detect driver fatigue. In order to realize human eyes closure calculation, correct and rapid detection of human face is accomplished firstly, for the specific environment of cabs, this paper proposes a fast face detection algorithm based on skin color model and radial basis function network, which makes input image carry out RGB and YCbCr color space conversion, then establishes relevant skin model to achieve the coarse positioning of face region, finally, combines radial basis function network to train input image, so that whether it is the skin color is determined according to the training results, and the detection on face is finished. Simulation results show that the algorithm improves the human face correct detection un-der strong light, laying a foundation for drivers’ fatigue driving research.%驾驶员疲劳状态检测一般采用对人眼的闭合度进行计算，若实现对人眼的闭合度计算首先是对人脸的正确快速检测，针对驾驶室的特定环境，本文研究一种基于肤色模型和径向基函数网络为基础的快速人脸检测算法，该算法首先对输入图像进行RGB和YCbCr颜色空间的转换，其次建立相关的肤色模型，实现人脸区域的粗定位，然后结合径向基函数网络对输入的图像进行训练，这样就可以根据训练的结果判断是否是肤色，从而实现人脸检测。仿真结果表明，所研究的算法较好的提高了强光下人脸的正确检测，为驾驶员疲劳驾驶的研究奠定前期基础。
Dynamics Model Abstraction Scheme Using Radial Basis Functions
Directory of Open Access Journals (Sweden)
Silvia Tolu
2012-01-01
Full Text Available This paper presents a control model for object manipulation. Properties of objects and environmental conditions influence the motor control and learning. System dynamics depend on an unobserved external context, for example, work load of a robot manipulator. The dynamics of a robot arm change as it manipulates objects with different physical properties, for example, the mass, shape, or mass distribution. We address active sensing strategies to acquire object dynamical models with a radial basis function neural network (RBF. Experiments are done using a real robot’s arm, and trajectory data are gathered during various trials manipulating different objects. Biped robots do not have high force joint servos and the control system hardly compensates all the inertia variation of the adjacent joints and disturbance torque on dynamic gait control. In order to achieve smoother control and lead to more reliable sensorimotor complexes, we evaluate and compare a sparse velocity-driven versus a dense position-driven control scheme.
Molecular basis of whey protein functionality
Zoran Herceg; Anet Režek; Suzana Rimac Brnčić
2008-01-01
Whey proteins constitute 18-20% of total milk protein content. Their nutritive value, accompanied by diverse physico-chemical and functional properties, make whey proteins widely applicable in food industry. Highly risen demands of consumers for tastier, healthier, suitable and more natural food products have given dairy industry the opportunity for development and enrichment of food products with whey protein supplements in order to increase their functional and nutritive properties. Develop...
Institute of Scientific and Technical Information of China (English)
张智; 胡晓辉; 邹志荣
2014-01-01
Summary Tomato is one of the main vegetables cultivated in greenhouse for its rich nutrition and good taste. In recent years,the occurrence of tomato disease brought a big threat to the yield and quality of tomato. Therefore,it is becoming more important to forecast and prevent the disease occurrence. In greenhouse, environmental factors,such as temperature and humidity,have great effect on occurrence of grey mould disease. Environmental factors are interactional in greenhouse,and the relationships between these factors and disease occurrence are complex,nonlinear and not easy to articulate.The traditional methods of mathematical statistics have some limitations in modeling the effects of environmental factors on the disease occurrence.Radical basis function (RBF) neural network is an ideal tool that could be applied to predict the grey mould disease from greenhouse tomato.It has fine characteristics of approximation performance and the global optimum which can overcome the limitations. It is difficult to build up the prediction model with all the involved factors,so the most correlated factors with the disease occurrence should be determined as predictors. A good prediction can strengthen foresight for preventing and treating diseases,which will provide scientific basis to formulate the most reasonable scheme for control diseases.In this paper,we used a neural network toolbox provided by Matlab to establish the RBF neural networks.The data of grey mould disease from greenhouse tomato and corresponding environmental data used in the experiments had been collected from one of the greenhouses in Xintiandi demonstration garden (Yangling, shaanxi Province) during the dates from 2009 04 01 to 2010 05 18.There were 102 sets of sample data in all,among which 90 sets were used to train the neural network models,and the other 12 sets were used to test. The RBF network consisted of input layer,hidden layer and output layer.Grey mould disease grade of tomato was target
Differential Forms Basis Functions for Better Conditioned Integral Equations
Energy Technology Data Exchange (ETDEWEB)
Fasenfest, B; White, D; Stowell, M; Rieben, R; Sharpe, R; Madsen, N; Rockway, J D; Champagne, N J; Jandhyala, V; Pingenot, J
2005-01-13
Differential forms offer a convenient way to classify physical quantities and set up computational problems. By observing the dimensionality and type of derivatives (divergence,curl,gradient) applied to a quantity, an appropriate differential form can be chosen for that quantity. To use these differential forms in a simulation, the forms must be discretized using basis functions. The 0-form through 2-form basis functions are formed for surfaces. Twisted 1-form and 2-form bases will be presented in this paper. Twisted 1-form (1-forms) basis functions ({Lambda}) are divergence-conforming edge basis functions with units m{sup -1}. They are appropriate for representing vector quantities with continuous normal components, and they belong to the same function space as the commonly used RWG bases [1]. They are used here to formulate the frequency-domain EFIE with Galerkin testing. The 2-form basis functions (f) are scalar basis functions with units m{sup -2} and with no enforced continuity between elements. At lowest order, the 2-form basis functions are similar to pulse basis functions. They are used here to formulate an electrostatic integral equation. It should be noted that the derivative of an n-form differential form basis function is an (n+1)-form, i.e. the derivative of a 1-form basis function is a 2-form. Because the basis functions are constructed such that they have spatial units, the spatial units are removed from the degrees of freedom, leading to a better-conditioned system matrix. In this conference paper, we look at the performance of these differential forms and bases by examining the conditioning of matrix systems for electrostatics and the EFIE. The meshes used were refined across the object to consider the behavior of these basis transforms for elements of different sizes.
Institute of Scientific and Technical Information of China (English)
赵宇红; 汪普林; 梁海滨
2011-01-01
电力系统短期负荷预测是电力生产部门的重要工作之一，本文利用径向基函数网络（RBF）进行负荷预测，针对RBF在负荷预测中隐含层节点数难求问题，提出了一种改进的最近邻聚类学习算法即可解决该难点，又可提高RBF神经网络收敛速度和负荷预测精度．根据某地区电网的实例进行研究，结果发现本文算法比改进前的算法预测的最小、最大相对误差分别减小0．14和1．12，证明了改进后算法有效性和可行性，为电力系统负荷预测提供了一种新途径．%Power system Short term load forecasting is one important work of the electricity production sector. In this paper,radial basis function network （RBF） is used in load forecast ing. Load forecasting for the RBF in the hidden layer nodes is hard to find. An improved nearest neighbor clustering algorithm is proposed to solve the difficulties and improve RBF neural network convergence speed and load forecasting accuracy. According to the instance of a regional power grid study,we found that the minimum,maximmn relative error were reduced by 0. 14 and 1.12,if we used the improved algorithm to predict. Case study results prove its effectiveness and feasibility. It provides a new way for the power system load forecasting.
Institute of Scientific and Technical Information of China (English)
赵高强; 傅(王乐)
2011-01-01
风速预测对风电场和电力系统的运行都具有重要意义.为了提高风速预测的精度,提出了一种基于量子粒子群-径向基神经网络模型,在确定网络隐含层节点数后,将RBF网络的参数编码成优化算法中的粒子个体进行优化,在全局空间搜索最优适应值的参数.用优化后的神经网络进行风速预测,实例结果表明该算法在预测速度和精度上都得到了提高.%Forecasting of wind speed is very important to the operation of wind power plants and power systems.To improve the wind speed forecasting accuracy, a model based on quantum- behaved particle swarm optimization and radial basis function neural network algorithm is proposed.After the number of nodes in hidden layer is confirmed and all parameters of RBF nets are coded to individual particles to optimize learning algorithm, the parameter of optimal-adaptive values can be searched in global space.Using the optimized neural network to forecast wind speed,and some calculation examples were abtained.The results showed that the new method can improve the speed and accuracy of prediction.
Chennubhotla Chakra; Wu Chuang; Farkas Illés J; Bahar Ivet; Oltvai Zoltán N
2006-01-01
Abstract Background Signal recognition and information processing is a fundamental cellular function, which in part involves comprehensive transcriptional regulatory (TR) mechanisms carried out in response to complex environmental signals in the context of the cell's own internal state. However, the network topological basis of developing such integrated responses remains poorly understood. Results By studying the TR network of the yeast Saccharomyces cerevisiae we show that an intermediate l...
Institute of Scientific and Technical Information of China (English)
胡焱弟; 李新欣; 白志鹏; 冯银厂; 赵玉杰; 吴建会
2006-01-01
将径向基函数网络(Radial Basis Function Network,RBFN)应用于城市环境颗粒物来源解析工作.模拟数据计算的解析结果表明:RBFN可以实现对多源(14个可能源,其中13个为有效源)的解析,在5%～15%的源和受体测量误差的情况下,对于分担率大于15%的主要源,其解析结果与真实值的相对误差均不高于5%;对于分担率大于5%的源,其解析相对误差均低于15%.RBF网络可以很好地识别无效源.因此,在充分掌握可能污染源成分谱信息的基础上,该方法具有源解析应用潜力.
一类基于径向基函数网的分工协作混合系统%A DIVIDE-AND-COOPERATE HYBRID SYSTEM BASED ON RADIAL BASIS FUNCTION NETWORKS
Institute of Scientific and Technical Information of China (English)
黄榕波; 朱思铭
2004-01-01
径向基函数网络(Radial Basis Function Network,RBFN)是二十世纪八十年代末提出的一种神经网络.当网络的输入维数较大时,RBFN的系统复杂性大大提高,从而使RBFN的行为受到影响,因此降低RBFN输入维数已成为RBFN的研究热点.本文提出一类基于RBFN的分工协作系统及其学习算法(A Divide-and-Cooperate HybridSystem Based RBFN,DCRBFN).DCRBFN是一种由多个子RBFN组成的混合结构,每个子RBFN具有自己的输入空间.由于DCRBFN把高维模型分解为低维模型,所以DCRBFN不仅明显降低了RBFN的复杂性而且网络的收敛速度更快.实验表明,DCRBFN在处理高维模型的行为明显优于RBFN.
Institute of Scientific and Technical Information of China (English)
吴洪岩; 刘淑华; 张嵛
2009-01-01
在复杂连续环境下,强化学习系统的状态空间面临维数灾难问题,需要采取量化的方法,降低输入空间的复杂度.径向基神经网络(RBFNN:Radial Basis Function Neural Networks)具有较强的函数逼近能力及泛化能力,由此提出了基于径向基神经网络的Q学习方法,并将其应用于单机器人的自主导航.在基于径向基神经网络的强化学习系统中,用径向基神经网络逼近状态空间和Q函数,使学习系统具有良好的泛化能力.仿真结果表明,该导航方法具有较强的避碰能力,提高了机器人对环境的适应能力.
DEFF Research Database (Denmark)
Madsen, Per Printz
1999-01-01
The purpose of this paper is to describe a neural network (SNN), that is based on Shannons ideas of reconstruction of a real continuous function from its samples. The basic function, used in this network, is the Sinc-function. Two learning algorithms are described. A simple one called IM...
DEFF Research Database (Denmark)
Madsen, Per Printz
1998-01-01
The purpose of this paper is to describe a neural network (SNN), that is based on Shannons ideas of reconstruction of a real continuous function from its samples. The basic function, used in this network, is the Sinc-function. Two learning algorithms are described. A simple one called IM...
Institute of Scientific and Technical Information of China (English)
2016-01-01
采用脉冲涡流技术进行检测时,为准确得到被测缺陷的轮廓,提出了一种基于径向基神经网络的缺陷轮廓重构方法.该方法为降低网络结构对重构结果的影响,采用主成分分析法对网络隐层应选择的最少节点数进行了计算,进而确定了较合理的网络结构;而后采用混合学习算法求得了网络参数,并通过引入梯度信息衰减系数对求解过程进行了优化;最后将其应用于脉冲涡流检测的缺陷轮廓重构实验,结果表明:基于径向基神经网络的缺陷轮廓重构方法不仅具有较高的重构精度而且具有较强的抗噪声干扰能力,是一种有效可行的脉冲涡流缺陷轮廓重构方法.%In order to obtain the defect profile when the pulsed eddy current testing was used, an approach of defect profile reconstruction from pulsed eddy current signals based on radial basis function neural network( RBFNN) was proposed in this paper. To reduce the effect of network structure on reconstruction result, principal components analysis was used to determine the least number of hidden nodes, then the appropriate network structure was determined. The hybrid learning algorithm was used to solve the parameter of RBFNN, and the solving process was optimized by attenuation coefficient of gradient information. Then the pro-posed approach was utilized in the experiment of defect profile reconstruction, the results indicate that the defect profile can be re-constructed accurately and the performance of noise interference suppression of the method is high, thus it is an effective and feasi-ble approach for pulsed eddy current defect profile reconstruction.
Higher-Order Hierarchical Legendre Basis Functions in Applications
DEFF Research Database (Denmark)
Kim, Oleksiy S.; Jørgensen, Erik; Meincke, Peter;
2007-01-01
degree of orthogonality. The basis functions are well-suited for solution of complex electromagnetic problems involving multiple homogeneous or inhomogeneous dielectric regions, metallic surfaces, layered media, etc. This paper presents real-life complex antenna radiation problems modeled...... with electromagnetic simulation tools based on the higher-order hierarchical Legendre basis functions....
EEG Source Reconstruction using Sparse Basis Function Representations
DEFF Research Database (Denmark)
Hansen, Sofie Therese; Hansen, Lars Kai
2014-01-01
State of the art performance of 3D EEG imaging is based on reconstruction using spatial basis function representations. In this work we augment the Variational Garrote (VG) approach for sparse approximation to incorporate spatial basis functions. As VG handles the bias variance trade-off with cross...
Institute of Scientific and Technical Information of China (English)
连丽婷; 肖昌汉; 杨明明
2012-01-01
在解决闭环消磁绕组电流优化计算问题时,会面临将外部磁场推算误差带入电流反演计算或完备的基函数难以确定等问题.为了降低这些因素对舰船最终补偿效果的影响,从智能优化的角度出发,在讨论散布常数对模型预测误差的影响后,确定了适宜的散布常数,建立了内部磁场与补偿电流之间的径向基函数神经网络预报模型.该方法通过样本对网络进行训练,无须推算内外磁场,就能直接得到使绕组磁场与目标磁场拟合误差最小的补偿电流向量.对比其他数值建模方法,其换算精度有所提高,且选择不同的同维向量作为基函数对补偿结果影响较小.船模实验验证了该方法的有效性.%As the errors from off-board magnetic field evaluation and difficulties in determining basis functions tend to affect the result of calculating the degaussing currents, an intelligent control method was introduced. After discussing the influence from spread coefficient, a radial basis function (RBF) neural network model was established for predicting optimal currents from onboard measurements directly. The magnetic field produced by degaussing coils is very similar to ship' s object field. The method can avoid many problems from the numerical model. Its high accuracy and effectiveness were verified by mockup experiments.
Energy Technology Data Exchange (ETDEWEB)
Edwin, K.W.; Niehage, U.
1987-04-13
In the first part of this article the authors initially formulate the function of the network reliability calculation. They then proceed to show concrete questions of the network planner, the basic answers to which can necessitate a quantifying of the network reliability. In conclusion the authors describe unsolved problems from their viewpoint, where further research in the field of reliability calculation is made necessary. Taking into account these fundamental considerations the authors devote the second part of the article to the investigation of two specific questions on planning extensions of municipal 110 kV cable networks. In supplying the answers to these questions the authors appley a new basis for the precise quantification of network reliability, developed from extensive analysis of such cable networks, and leave this open to discussion.
Institute of Scientific and Technical Information of China (English)
陈晶; 王文圣; 李跃清
2011-01-01
将集对分析与径向基函数神经网络结合,提出了集对分析径向基函数神经网络预测模型.模型思路是将研究对象t-1时和t时的影响因子集构造为集对并计算联系度,由联系度的同一度、差异度、对立度及研究对象t-1时的值为输入,研究对象t时的值为输出,构建径向基函数神经网络.以年径流预测为例研究表明,模型结构清晰、步骤明确、预测精度较高,为集对分析应用于水文预测提供了新思路.%The proposed SPA-RBFNN prediction model is a combination of set pair analysis (SPA) and radial basis function neural network (RBFNN). The idea of SPA-RBFNN, firstly sets the impact factors of research object in both t-1 and t period of time as a pair, and calculates the connection degree of the pair, then uses its calculated homology degree, difference degree and antinomy degree, along with the situation of research object in t-1 period of time as model input, the situation of research object in t period of time as model output, finally finishes the model establishment. The case study of annual runoff prediction shows that SPARBFNN prediction model is characterized by explicit structure, easy realization and good prediction ability. The model construction idea provides a new thinking for the application of SPA in solving the hydrological prediction problems.
Representation of Functional Data in Neural Networks
Rossi, Fabrice; Conan-Guez, Brieuc; Verleysen, Michel
2005-01-01
Functional Data Analysis (FDA) is an extension of traditional data analysis to functional data, for example spectra, temporal series, spatio-temporal images, gesture recognition data, etc. Functional data are rarely known in practice; usually a regular or irregular sampling is known. For this reason, some processing is needed in order to benefit from the smooth character of functional data in the analysis methods. This paper shows how to extend the Radial-Basis Function Networks (RBFN) and Multi-Layer Perceptron (MLP) models to functional data inputs, in particular when the latter are known through lists of input-output pairs. Various possibilities for functional processing are discussed, including the projection on smooth bases, Functional Principal Component Analysis, functional centering and reduction, and the use of differential operators. It is shown how to incorporate these functional processing into the RBFN and MLP models. The functional approach is illustrated on a benchmark of spectrometric data ana...
Construction of `Wachspress Type' Rational Basis Functions over Rectangles
Indian Academy of Sciences (India)
P L Powar; S S Rana
2000-02-01
In the present paper, we have constructed rational basis functions of 0 class over rectangular elements with wider choice of denominator function. This construction yields additional number of interior nodes. Hence, extra nodal points and the flexibility of denominator function suggest better approximation.
Dynamic Analysis of Wind Turbine Blades Using Radial Basis Functions
Ming-Hung Hsu
2011-01-01
Wind turbine blades play important roles in wind energy generation. The dynamic problems associated with wind turbine blades are formulated using radial basis functions. The radial basis function procedure is used to transform partial differential equations, which represent the dynamic behavior of wind turbine blades, into a discrete eigenvalue problem. Numerical results demonstrate that rotational speed significantly impacts the first frequency of a wind turbine blade. Moreover, the...
Higher Order Hierarchical Legendre Basis Functions for Electromagnetic Modeling
DEFF Research Database (Denmark)
Jørgensen, Erik; Volakis, John L.; Meincke, Peter;
2004-01-01
This paper presents a new hierarchical basis of arbitrary order for integral equations solved with the Method of Moments (MoM). The basis is derived from orthogonal Legendre polynomials which are modified to impose continuity of vector quantities between neighboring elements while maintaining most....... In addition, all higher-order terms in the expansion have two vanishing moments.In contrast to existing formulations, these properties allow the use of very high-order basis functions without introducing ill-conditioning of the resulting MoM matrix. Numerical results confirm that the condition number...... of the MoM matrix obtained with this new basis is much lower than existing higher-order interpolatory and hierarchical basis functions. As a consequence of the excellent condition numbers, we demonstrate that even very high-order MoM systems, e.g. 10th order, can be solved efficiently with an iterative...
Institute of Scientific and Technical Information of China (English)
周泰; 王亚玲
2011-01-01
To effectively improve regional logistics capability and promote regional economic growth, the authors established investment structural optimization model of regional logistics capability. First of all, the authors analyzed the reasons why optimization of investment structure of regional industries can enhance regional logistics capability detailedly, and revealed the complex nonlinear relationship between regional logistics capability and investment structural from the perspective of the industrial structure; Then the authors implemented the nonlinear mapping by using radial basis function (RBF) network, and set up a nonlinear programming (NLP) optimization model with constraint conditions; Finally, based on the true data of industry's investment of Sichuan province in 2005, the authors solved the model by improved genetic algorithm(IGA), and obtained the approximate optimal solution of the optimization problem as well as the optimal direction of investment structural. The optimization results indicate that the model is effective and reasonable for optimization of industry's investment structure; it is a new practical and operable method for improving regional logistics capability.%针对如何有效地提高区域物流能力,以推动区域经济增长的问题,构建了区域物流能力的投资结构优化模型.首先详细分析了优化区域产业投资结构能增强区域物流能力的原因,从产业结构的角度揭示了区域物流能力与产业投资分配之间复杂的非线性关系;然后采用径向基函数神经网络实现了它们之间的非线性映射,进而建立了有约束条件限制的非线性规划投资结构优化模型;最后以四川省2005年的产业投资实际数据为基础,采用改进遗传算法对该模型进行求解,获得了优化问题的近似最优解以及投资结构的优化方向.优化结果表明:建立的模型对产业投资结构的优化是合理、有效的,从而提供了一个能提高区域
Institute of Scientific and Technical Information of China (English)
李翔; 朱全银; 王尊
2013-01-01
针对传统小波神经网络（ wavelet neural network， WNN）受隐含层节点数影响大、网络误差易陷入局部极小、预测结果不稳定的问题，提出使用GentleAdaBoost和小波神经网络相结合的方法，提高网络预测精度和泛化能力。该方法首先对样本数据进行预处理并初始化测试数据分布权值；然后通过选取不同的隐含层节点数、小波基函数构造出不同类型的小波神经网络弱预测器序列并对样本数据进行反复训练；最后使用GentleAdaBoost算法将得到的多个小波神经网络弱预测器组成新的强预测器并进行回归预测。对UCI数据库中数据集进行仿真实验，结果表明，本方法比传统小波神经网络预测平均误差减少40％以上，有效地提高了神经网络预测精度，为小波神经网络应用提供借鉴。%In view that the traditional wavelet neural network ( WNN) was affected largely by the number of hidden lay-er nodes, easy to fall into local minimum and had unstable forecast results, a method of combining the GentleAdaBoost algorithm with WNN was put forward to improve the forecasting accuracy and generalization ability.First, this method performed the pretreatment for the historical data and initialized the distribution weights of test data.Second, different hidden layer nodes and wavelet basis functions were selected randomly to construct weak predictors of WNN and trained the sample data repeatedly.Finally, the multiple weak predictors of WNN were used to form a new strong predictor by GentleAdaBoost algorithm for regression forecasting.A simulation experiment using datasets from the UCI database was carried out.The results showed that this method had reduced the average error value by more than 40%compared to the traditional WNN, improved the forecasting accuracy of neural network, and could provide references for the WNN fore-casting.
Institute of Scientific and Technical Information of China (English)
江虹; 杨彦超; 伍春
2012-01-01
Cognitive radio (CR) is an intelligent wireless communication system, which can dynamically adjust the parameters to improve system performance depending on the environmental change and quality of service (Qos). The core technology for CR is the design of cognitive engine, which can introduce reasoning and learning methods to achieve the perception, adaptation and learning. Considering the dynamical environment and demands, a scheme of cognitive engine was proposed based on the radial basis function (RBF) neural network. The scheme could study from experience and environment to reconfigure communication parameters and improve system performance. The cognitive engine was composed of two RBF_NN layers to solve the learning configurations of routing protocol and local parameters. The outer layer learned the global properties, while the inner layer learned the local attributes. After training, the learning model performance was evaluated according to two defined benchmark functions. The simulation results show that the learning model is effective and the cognitive engine can effectively achieve the study and reconfiguration function.%认知无线电（CR）是一种智能无线通信系统，它能根据环境变化、业务需求动态调整参数，提高系统性能，其核心技术是认知引擎的设计。认知引擎可引入人工智能领域的推理与学习方法来实现CR的感知、自适应与学习能力。为适应变化的无线环境和用户需求，提曲基于径向基神经网络（RBF）的CR认知引擎设计方法，该法通过对经验知识和环境的学习，重配置通信参数，以达到资源合理分配，提高系统性能。该引擎由两层RBF神经网络组成，外层神经网络学习全局属性，内层神经网络学习局部属性，以解决路由协议及局部参数的学习配置。在训练RBF神经网络学习模型后，根据定义的两个测试基准函数，评估模型性能，仿真验证了该学习模型
Functional Aspects of Biological Networks
Sneppen, Kim
2007-03-01
We discuss biological networks with respect to 1) relative positioning and importance of high degree nodes, 2) function and signaling, 3) logic and dynamics of regulation. Visually the soft modularity of many real world networks can be characterized in terms of number of high and low degrees nodes positioned relative to each other in a landscape analogue with mountains (high-degree nodes) and valleys (low-degree nodes). In these terms biological networks looks like rugged landscapes with separated peaks, hub proteins, which each are roughly as essential as any of the individual proteins on the periphery of the hub. Within each sup-domain of a molecular network one can often identify dynamical feedback mechanisms that falls into combinations of positive and negative feedback circuits. We will illustrate this with examples taken from phage regulation and bacterial uptake and regulation of small molecules. In particular we find that a double negative regulation often are replaced by a single positive link in unrelated organisms with same functional requirements. Overall we argue that network topology primarily reflects functional constraints. References: S. Maslov and K. Sneppen. ``Computational architecture of the yeast regulatory network." Phys. Biol. 2:94 (2005) A. Trusina et al. ``Functional alignment of regulatory networks: A study of temerate phages". Plos Computational Biology 1:7 (2005). J.B. Axelsen et al. ``Degree Landscapes in Scale-Free Networks" physics/0512075 (2005). A. Trusina et al. ``Hierarchy and Anti-Hierarchy in Real and Scale Free networks." PRL 92:178702 (2004) S. Semsey et al. ``Genetic Regulation of Fluxes: Iron Homeostasis of Escherichia coli". (2006) q-bio.MN/0609042
Institute of Scientific and Technical Information of China (English)
葛彦强; 汪向征; 于江德
2012-01-01
We propose an adaptive Kohonen Self-Organizing Maps and Radial Basis Function Network-based method (KR) for network blocking forecasting in the paper. It shows that there are some problems in the network blocking forecasting now, especially when the data set is just small. Therefore, for achieving high accuracy in the network blocking forecasting, it is necessary to consider the relationships between each data within the original data set in the forecasting process. Now to get more valuable position information, a series of processes including Kohonen neural network and RBF network is proposed to meet the types of different data. The process makes the network can meet the different kinds of data. In this application to a city's network blocking forecasting, we investigate KR's and two other algorithms performance on a original data set. The comparison of experimental results shows that KR is better location performance than others.%文中提出了一种利用自组织映射（KSOM）和径向基函数（KR）神经网络进行网络拥塞预测的方法．目前的研究表明，预测网络拥塞还存在一些问题，尤其在数据集比较小的时候．因此，为了使网络拥塞问题预测精度高，在预测过程中有必要考虑原有的数据集中每个数据之间的关系．现在为了获得更多的有价值的位置信息，采取了一系列的措施去满足不同数据的情况，包括使用自组织映射神经网络和径向基函数神经网络算法．这一过程使网络能满足不同类型的数据．在本文网络拥塞预测中，采用同一原始数据集，分别对利用自组织映射和径向基函数神经网络的算法和另外两种算法的性能进行比较．实验结果表明，利用自组织映射和径向基函数神经网络的算法具有更好的效果．
Point Set Denoising Using Bootstrap-Based Radial Basis Function
Ramli, Ahmad; Abd. Majid, Ahmad
2016-01-01
This paper examines the application of a bootstrap test error estimation of radial basis functions, specifically thin-plate spline fitting, in surface smoothing. The presence of noisy data is a common issue of the point set model that is generated from 3D scanning devices, and hence, point set denoising is one of the main concerns in point set modelling. Bootstrap test error estimation, which is applied when searching for the smoothing parameters of radial basis functions, is revisited. The main contribution of this paper is a smoothing algorithm that relies on a bootstrap-based radial basis function. The proposed method incorporates a k-nearest neighbour search and then projects the point set to the approximated thin-plate spline surface. Therefore, the denoising process is achieved, and the features are well preserved. A comparison of the proposed method with other smoothing methods is also carried out in this study. PMID:27315105
EEG Source Reconstruction using Sparse Basis Function Representations
DEFF Research Database (Denmark)
Hansen, Sofie Therese; Hansen, Lars Kai
2014-01-01
State of the art performance of 3D EEG imaging is based on reconstruction using spatial basis function representations. In this work we augment the Variational Garrote (VG) approach for sparse approximation to incorporate spatial basis functions. As VG handles the bias variance trade-off with cross......-validation this approach is more automated than competing approaches such as Multiple Sparse Priors (Friston et al., 2008) or Champagne (Wipf et al., 2010) that require manual selection of noise level and auxiliary signal free data, respectively. Finally, we propose an unbiased estimator of the reproducibility...
Optimal Piecewise Linear Basis Functions in Two Dimensions
Energy Technology Data Exchange (ETDEWEB)
Brooks III, E D; Szoke, A
2009-01-26
We use a variational approach to optimize the center point coefficients associated with the piecewise linear basis functions introduced by Stone and Adams [1], for polygonal zones in two Cartesian dimensions. Our strategy provides optimal center point coefficients, as a function of the location of the center point, by minimizing the error induced when the basis function interpolation is used for the solution of the time independent diffusion equation within the polygonal zone. By using optimal center point coefficients, one expects to minimize the errors that occur when these basis functions are used to discretize diffusion equations, or transport equations in optically thick zones (where they approach the solution of the diffusion equation). Our optimal center point coefficients satisfy the requirements placed upon the basis functions for any location of the center point. We also find that the location of the center point can be optimized, but this requires numerical calculations. Curiously, the optimum center point location is independent of the values of the dependent variable on the corners only for quadrilaterals.
47 CFR 51.307 - Duty to provide access on an unbundled basis to network elements.
2010-10-01
... network elements. 51.307 Section 51.307 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED... Carriers § 51.307 Duty to provide access on an unbundled basis to network elements. (a) An incumbent LEC... service, nondiscriminatory access to network elements on an unbundled basis at any technically...
Network Decomposition and Maximum Independent Set Part Ⅰ: Theoretic Basis
Institute of Scientific and Technical Information of China (English)
朱松年; 朱嫱
2003-01-01
The structure and characteristics of a connected network are analyzed, and a special kind of sub-network, which can optimize the iteration processes, is discovered. Then, the sufficient and necessary conditions for obtaining the maximum independent set are deduced. It is found that the neighborhood of this sub-network possesses the similar characters, but both can never be allowed incorporated together. Particularly, it is identified that the network can be divided into two parts by a certain style, and then both of them can be transformed into a pair sets network, where the special sub-networks and their neighborhoods appear alternately distributed throughout the entire pair sets network. By use of this characteristic, the network decomposed enough without losing any solutions is obtained. All of these above will be able to make well ready for developing a much better algorithm with polynomial time bound for an odd network in the the application research part of this subject.
Artificial neural network modeling of fixed bed biosorption using radial basis approach
Saha, Dipendu; Bhowal, Avijit; Datta, Siddhartha
2010-04-01
In modern day scenario, biosorption is a cost effective separation technology for the removal of various pollutants from wastewater and waste streams from various process industries. The difficulties associated in rigorous mathematical modeling of a fixed bed bio-adsorbing systems due to the complexities of the process often makes the development of pure black-box artificial neural network (ANN) models particularly useful in this field. In this work, radial basis function network has been employed as ANN to model the breakthrough curves in fixed bed biosorption. The prediction has been compared to the experimental breakthrough curves of Cadmium, Lanthanum and a dye available in the literature. Results show that this network gives fairly accurate representation of the actual breakthrough curves. The results obtained from ANN modeling approach shows the better agreement between experimental and predicted breakthrough curves as the error for all these situations are within 6%.
Directory of Open Access Journals (Sweden)
Chennubhotla Chakra
2006-10-01
Full Text Available Abstract Background Signal recognition and information processing is a fundamental cellular function, which in part involves comprehensive transcriptional regulatory (TR mechanisms carried out in response to complex environmental signals in the context of the cell's own internal state. However, the network topological basis of developing such integrated responses remains poorly understood. Results By studying the TR network of the yeast Saccharomyces cerevisiae we show that an intermediate layer of transcription factors naturally segregates into distinct subnetworks. In these topological units transcription factors are densely interlinked in a largely hierarchical manner and respond to external signals by utilizing a fraction of these subnets. Conclusion As transcriptional regulation represents the 'slow' component of overall information processing, the identified topology suggests a model in which successive waves of transcriptional regulation originating from distinct fractions of the TR network control robust integrated responses to complex stimuli.
The Functional Requirements and Design Basis for Information Barriers
Energy Technology Data Exchange (ETDEWEB)
Fuller, James L.
2012-05-01
This report summarizes the results of the Information Barrier Working Group workshop held at Sandia National Laboratory in Albuquerque, NM, February 2-4, 1999. This workshop was convened to establish the functional requirements associated with warhead radiation signature information barriers, to identify the major design elements of any such system or approach, and to identify a design basis for each of these major elements. Such information forms the general design basis to be used in designing, fabricating, and evaluating the complete integrated systems developed for specific purposes.
AN IMPROVED RADIAL BASIS FUNCTION BASED METHOD FOR IMAGE WARPING
Institute of Scientific and Technical Information of China (English)
Nie Xuan; Zhao Rongchun; Zhang Cheng; Zhang Xiaoyan
2005-01-01
A new image warping method is proposed in this letter, which can warp a given image by some manual defined features. Based on the radial basis interpolation function algorithm, the proposed method can transform the original optimized problem into nonsingular linear problem by adding one-order term and affine differentiable condition. This linear system can get the steady unique solution by choosing suitable kernel function. Furthermore, the proposed method demonstrates how to set up the radial basis function in the target image so as to achieve supports to adopt the backward re-sampling technology accordingly which could gain the very slippery warping image. Theexperimental result shows that the proposed method can implement smooth and gradual image warping with multi-anchor points' accurate interpolation.
Algebraic evaluation of matrix elements in the Laguerre function basis
McCoy, A. E.; Caprio, M. A.
2016-02-01
The Laguerre functions constitute one of the fundamental basis sets for calculations in atomic and molecular electron-structure theory, with applications in hadronic and nuclear theory as well. While similar in form to the Coulomb bound-state eigenfunctions (from the Schrödinger eigenproblem) or the Coulomb-Sturmian functions (from a related Sturm-Liouville problem), the Laguerre functions, unlike these former functions, constitute a complete, discrete, orthonormal set for square-integrable functions in three dimensions. We construct the SU(1, 1) × SO(3) dynamical algebra for the Laguerre functions and apply the ideas of factorization (or supersymmetric quantum mechanics) to derive shift operators for these functions. We use the resulting algebraic framework to derive analytic expressions for matrix elements of several basic radial operators (involving powers of the radial coordinate and radial derivative) in the Laguerre function basis. We illustrate how matrix elements for more general spherical tensor operators in three dimensional space, such as the gradient, may then be constructed from these radial matrix elements.
SATWG networked quality function deployment
Brown, Don
1992-01-01
The initiative of this work is to develop a cooperative process for continual evolution of an integrated, time phased avionics technology plan that involves customers, technologists, developers, and managers. This will be accomplished by demonstrating a computer network technology to augment the Quality Function Deployment (QFD). All results are presented in viewgraph format.
Caffeine Modulates Attention Network Function
Brunye, Tad T.; Mahoney, Caroline R.; Lieberman, Harris R.; Taylor, Holly A.
2010-01-01
The present work investigated the effects of caffeine (0 mg, 100 mg, 200 mg, 400 mg) on a flanker task designed to test Posner's three visual attention network functions: alerting, orienting, and executive control [Posner, M. I. (2004). "Cognitive neuroscience of attention". New York, NY: Guilford Press]. In a placebo-controlled, double-blind…
Boolean networks with veto functions
Ebadi, Haleh; Klemm, Konstantin
2014-08-01
Boolean networks are discrete dynamical systems for modeling regulation and signaling in living cells. We investigate a particular class of Boolean functions with inhibiting inputs exerting a veto (forced zero) on the output. We give analytical expressions for the sensitivity of these functions and provide evidence for their role in natural systems. In an intracellular signal transduction network [Helikar et al., Proc. Natl. Acad. Sci. USA 105, 1913 (2008), 10.1073/pnas.0705088105], the functions with veto are over-represented by a factor exceeding the over-representation of threshold functions and canalyzing functions in the same system. In Boolean networks for control of the yeast cell cycle [Li et al., Proc. Natl. Acad. Sci. USA 101, 4781 (2004), 10.1073/pnas.0305937101; Davidich et al., PLoS ONE 3, e1672 (2008), 10.1371/journal.pone.0001672], no or minimal changes to the wiring diagrams are necessary to formulate their dynamics in terms of the veto functions introduced here.
Application of the Characteristic Basis Function Method Using CUDA
Directory of Open Access Journals (Sweden)
Juan Ignacio Pérez
2014-01-01
Full Text Available The characteristic basis function method (CBFM is a popular technique for efficiently solving the method of moments (MoM matrix equations. In this work, we address the adaptation of this method to a relatively new computing infrastructure provided by NVIDIA, the Compute Unified Device Architecture (CUDA, and take into account some of the limitations which appear when the geometry under analysis becomes too big to fit into the Graphics Processing Unit’s (GPU’s memory.
Institute of Scientific and Technical Information of China (English)
马伟华; 刘珑龙; 张建民
2006-01-01
将小波变换与神经网络相结合,对浮游植物活体的三维荧光光谱进行分类.首先利用小波变换对数据进行压缩,然后利用径向基函数(Radial Basis Function, RBF)神经网络对光谱曲线进行逼近,从而进行物种的识别,平均识别率高达95.8%.结果表明,该方法较传统的统计方法更方便、准确率更高.
Directory of Open Access Journals (Sweden)
Wang Pidong
2016-01-01
Full Text Available Blind source separation is a hot topic in signal processing. Most existing works focus on dealing with linear combined signals, while in practice we always encounter with nonlinear mixed signals. To address the problem of nonlinear source separation, in this paper we propose a novel algorithm using radial basis function neutral network, optimized by multi-universe parallel quantum genetic algorithm. Experiments show the efficiency of the proposed method.
Libcint: An efficient general integral library for Gaussian basis functions
Sun, Qiming
2014-01-01
An efficient integral library Libcint was designed to automatically implement general integrals for Gaussian-type scalar and spinor basis functions. The library can handle arbitrary integral expressions on top of $\\mathbf{p}$, $\\mathbf{r}$ and $\\sigma$ operators with one-electron overlap and nuclear attraction, two-electron Coulomb and Gaunt operators. Using a symbolic algebra tool, new integrals are derived and translated to C code programmatically. The generated integrals can be used in various types of molecular properties. In the present work, we computed the analytical gradients and NMR shielding constants at both non-relativistic and four-component relativistic Hartree-Fock level to demonstrate the capability of the integral library. Due to the use of kinetically balanced basis and gauge including atomic orbitals, the relativistic analytical gradients and shielding constants requires the integral library to handle the fifth-order electron repulsion integral derivatives. The generality of the integral li...
APPROXIMATION MULTIDIMENSION FUCTION WITH FUNCTIONAL NETWORK
Institute of Scientific and Technical Information of China (English)
Li Weibin; Liu Fang; Jiao Licheng; Zhang Shuling; Li Zongling
2006-01-01
The functional network was introduced by E.Catillo, which extended the neural network. Not only can it solve the problems solved, but also it can formulate the ones that cannot be solved by traditional network.This paper applies functional network to approximate the multidimension function under the ridgelet theory.The method performs more stable and faster than the traditional neural network. The numerical examples demonstrate the performance.
Product design on the basis of fuzzy quality function deployment
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
In the implementation of quality function deployment (QFD), the determination of the target values of engineering characteristics is a complex decision process with multiple variables and multiple objectives that should trade off, and optimize all kinds of conflicts and constraints. A fuzzy linear programming model (FLP) is proposed. On the basis of the inherent fuzziness of QFD system, triangular fuzzy numbers are used to represent all the relationships and correlations, and then, the functional relationships between the customer needs and engineering characteristics and the functional correlations among the engineering characteristics are determined with the information in the house of quality (HoQ) fully used. The fuzzy linear programming (FLP) model aims to find the optimal target values of the engineering characteristics to maximize the customer satisfaction. Finally, the proposed method is illustrated by a numerical example.
Wave forecasting in near real time basis by neural network
Digital Repository Service at National Institute of Oceanography (India)
Rao, S.; Mandal, S.; Prabaharan, N.
., forecasting of waves become an important aspect of marine environment. This paper presents application of the neural network (NN) with better update algorithms, namely rprop, quickprop and superSAB for wave forecasting. Measured waves off Marmagoa, Goa, India...
Institute of Scientific and Technical Information of China (English)
李洁; 彭昱忠; 吴建生
2014-01-01
构造了一种带多个核函数的结构自适应径向基函数神经网络（RBF NN）降水预报集成模型。该网络结构同时使用6个核函数，采用基于输入输出全部信息的模糊相似矩阵的平均矩阵元法自动确定隐节点数，自适应地生成RBF神经网络集成个体，最后建立多元回归模型集成。对日本的细网格资料数据建立平均日降水量预报模型，利用MATLAB进行仿真实验，结果表明，该模型预报性能明显优于同期中国气象局的T213（中国气象局的全球中期天气数值预报产品预报值）降水预报，可为气象预报研究提供新思路，为降水预报决策的制定提供重要的参考。%This paper constructs an integrated model of radial basis function neural network (RBF NN)with self-a-daptive structure of multi-kernel function on precipitation forecast. The network structure using six nuclear functions at the same time adapts the method of mean matrix element based on the fuzzy similarity matrix of the input and output information and automatically ensures the number of hidden nodes. The generated RBF neural network ensembles indi-vidual and finally establishes integrated model of multiple regression. It also establishes the mean daily precipitation forecast model on Japanese fine grid data and does simulation experiment by using the method of MATLAB. The result shows that the predictability of this model is obviously superior to the T213 (the forecast value tested by the global me-dium-range numerical forecasting product of China Meteorological Bureau),which can provide new ideas for the study of weather forecast and important references for the decision making of the precipitation forecast.
The Gaussian Radial Basis Function Method for Plasma Kinetic Theory
Hirvijoki, Eero; Belli, Emily; Embréus, Ola
2015-01-01
A fundamental macroscopic description of a magnetized plasma is the Vlasov equation supplemented by the nonlinear inverse-square force Fokker-Planck collision operator [Rosenbluth et al., Phys. Rev., 107, 1957]. The Vlasov part describes advection in a six-dimensional phase space whereas the collision operator involves friction and diffusion coefficients that are weighted velocity-space integrals of the particle distribution function. The Fokker-Planck collision operator is an integro-differential, bilinear operator, and numerical discretization of the operator is far from trivial. In this letter, we describe a new approach to discretize the entire kinetic system based on an expansion in Gaussian Radial Basis functions (RBFs). This approach is particularly well-suited to treat the collision operator because the friction and diffusion coefficients can be analytically calculated. Although the RBF method is known to be a powerful scheme for the interpolation of scattered multidimensional data, Gaussian RBFs also...
Computational optical distortion correction using a radial basis function-based mapping method.
Bauer, Aaron; Vo, Sophie; Parkins, Keith; Rodriguez, Francisco; Cakmakci, Ozan; Rolland, Jannick P
2012-07-01
A distortion mapping and computational image unwarping method based on a network interpolation that uses radial basis functions is presented. The method is applied to correct distortion in an off-axis head-worn display (HWD) presenting up to 23% highly asymmetric distortion over a 27°x21° field of view. A 10(-5) mm absolute error of the mapping function over the field of view was achieved. The unwarping efficacy was assessed using the image-rendering feature of optical design software. Correlation coefficients between unwarped images seen through the HWD and the original images, as well as edge superimposition results, are presented. In an experiment, images are prewarped using radial basis functions for a recently built, off-axis HWD with a 20° diagonal field of view in a 4:3 ratio. Real-time video is generated by a custom application with 2 ms added latency and is demonstrated.
Nikolaev, A. V.; Lamoen, D.; Partoens, B.
2016-07-01
In order to increase the accuracy of the linearized augmented plane wave (LAPW) method, we present a new approach where the plane wave basis function is augmented by two different atomic radial components constructed at two different linearization energies corresponding to two different electron bands (or energy windows). We demonstrate that this case can be reduced to the standard treatment within the LAPW paradigm where the usual basis set is enriched by the basis functions of the tight binding type, which go to zero with zero derivative at the sphere boundary. We show that the task is closely related with the problem of extended core states which is currently solved by applying the LAPW method with local orbitals (LAPW+LO). In comparison with LAPW+LO, the number of supplemented basis functions in our approach is doubled, which opens up a new channel for the extension of the LAPW and LAPW+LO basis sets. The appearance of new supplemented basis functions absent in the LAPW+LO treatment is closely related with the existence of the u ˙ l -component in the canonical LAPW method. We discuss properties of additional tight binding basis functions and apply the extended basis set for computation of electron energy bands of lanthanum (face and body centered structures) and hexagonal close packed lattice of cadmium. We demonstrate that the new treatment gives lower total energies in comparison with both canonical LAPW and LAPW+LO, with the energy difference more pronounced for intermediate and poor LAPW basis sets.
Institute of Scientific and Technical Information of China (English)
陈正洪; 王勇; 李艳
2008-01-01
A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical use. This paper presents an adaptive proportional integral differential (PID) control algorithm based on radial basis function (RBF) neural network for trajectory tracking of a two-degree-of-freedom (2-DOF) closed-chain robot. In this scheme, an RBF neural network is used to approximate the unknown nonlinear dynamics of the robot, at the same time, the PID parameters can be adjusted online and the high precision can be obtained. Simulation results show that the control algorithm accurately tracks a 2-DOF closed-chain robot trajectories. The results also indicate that the system robustness and tracking performance are superior to the classic PID method.
Quantifying and Analyzing the Network Basis of Genetic Complexity
Thompson, Ethan G.; Galitski, Timothy
2012-01-01
Genotype-to-phenotype maps exhibit complexity. This genetic complexity is mentioned frequently in the literature, but a consistent and quantitative definition is lacking. Here, we derive such a definition and investigate its consequences for model genetic systems. The definition equates genetic complexity with a surplus of genotypic diversity over phenotypic diversity. Applying this definition to ensembles of Boolean network models, we found that the in-degree distribution and the number of p...
Relativistic Kinetic-Balance Condition for Explicitly Correlated Basis Functions
Simmen, Benjamin; Reiher, Markus
2015-01-01
This paper presents the derivation of a kinetic-balance condition for explicitly correlated basis functions employed in semi-classical relativistic calculations. Such a condition is important to ensure variational stability in algorithms based on the first-quantized Dirac theory of 1/2-fermions. We demonstrate that the kinetic-balance condition can be obtained from the row reduction process commonly applied to solve systems of linear equations. The resulting form of kinetic balance establishes a relation for the $4^N$ components of the spinor of an $N$-fermion system to the non-relativistic limit, which is in accordance with recent developments in the field of exact decoupling in relativistic orbital-based many-electron theory.
Optimized Radial Basis Function Classifier for Multi Modal Biometrics
Directory of Open Access Journals (Sweden)
Anand Viswanathan
2014-07-01
Full Text Available Biometric systems can be used for the identification or verification of humans based on their physiological or behavioral features. In these systems the biometric characteristics such as fingerprints, palm-print, iris or speech can be recorded and are compared with the samples for the identification or verification. Multimodal biometrics is more accurate and solves spoof attacks than the single modal bio metrics systems. In this study, a multimodal biometric system using fingerprint images and finger-vein patterns is proposed and also an optimized Radial Basis Function (RBF kernel classifier is proposed to identify the authorized users. The extracted features from these modalities are selected by PCA and kernel PCA and combined to classify by RBF classifier. The parameters of RBF classifier is optimized by using BAT algorithm with local search. The performance of the proposed classifier is compared with the KNN classifier, Naïve Bayesian classifier and non-optimized RBF classifier.
Recent advances in radial basis function collocation methods
Chen, Wen; Chen, C S
2014-01-01
This book surveys the latest advances in radial basis function (RBF) meshless collocation methods which emphasis on recent novel kernel RBFs and new numerical schemes for solving partial differential equations. The RBF collocation methods are inherently free of integration and mesh, and avoid tedious mesh generation involved in standard finite element and boundary element methods. This book focuses primarily on the numerical algorithms, engineering applications, and highlights a large class of novel boundary-type RBF meshless collocation methods. These methods have shown a clear edge over the traditional numerical techniques especially for problems involving infinite domain, moving boundary, thin-walled structures, and inverse problems. Due to the rapid development in RBF meshless collocation methods, there is a need to summarize all these new materials so that they are available to scientists, engineers, and graduate students who are interest to apply these newly developed methods for solving real world’s ...
Institute of Scientific and Technical Information of China (English)
杨晓丽; 何琼
2016-01-01
Consensus strategy was applied to radial basis function neural network and used in NIRS determination of total flavonoids in panax notoginseng.Firstly,the spectra were preprocessed using discrete wavelet transform for noise filtering and data compression.Then,consensus radial basis function neural network was used for establishing the calibration model.It was shown by the results that the consensus models were more stable and accurate than the conventional regression models.%将共识策略结合径向基神经网络用于近红外光谱法测定三七中总黄酮的含量中。首先采用离散小波变换对近红外光谱进行预处理，去除噪声并压缩数据。继而采用共识径向基神经网络建立校正模型。结果表明：共识策略可以使模型更稳定、更准确。
Scale-Free Brain Functional Networks
Eguíluz, Victor M.; Chialvo, Dante R.; Cecchi, Guillermo A.; Baliki, Marwan; Apkarian, A. Vania
2005-01-01
Functional magnetic resonance imaging is used to extract functional networks connecting correlated human brain sites. Analysis of the resulting networks in different tasks shows that (a)the distribution of functional connections, and the probability of finding a link versus distance are both scale-free, (b)the characteristic path length is small and comparable with those of equivalent random networks, and (c)the clustering coefficient is orders of magnitude larger than those of equivalent random networks. All these properties, typical of scale-free small-world networks, reflect important functional information about brain states.
Adaptive radial basis function mesh deformation using data reduction
Gillebaart, T.; Blom, D. S.; van Zuijlen, A. H.; Bijl, H.
2016-09-01
Radial Basis Function (RBF) mesh deformation is one of the most robust mesh deformation methods available. Using the greedy (data reduction) method in combination with an explicit boundary correction, results in an efficient method as shown in literature. However, to ensure the method remains robust, two issues are addressed: 1) how to ensure that the set of control points remains an accurate representation of the geometry in time and 2) how to use/automate the explicit boundary correction, while ensuring a high mesh quality. In this paper, we propose an adaptive RBF mesh deformation method, which ensures the set of control points always represents the geometry/displacement up to a certain (user-specified) criteria, by keeping track of the boundary error throughout the simulation and re-selecting when needed. Opposed to the unit displacement and prescribed displacement selection methods, the adaptive method is more robust, user-independent and efficient, for the cases considered. Secondly, the analysis of a single high aspect ratio cell is used to formulate an equation for the correction radius needed, depending on the characteristics of the correction function used, maximum aspect ratio, minimum first cell height and boundary error. Based on the analysis two new radial basis correction functions are derived and proposed. This proposed automated procedure is verified while varying the correction function, Reynolds number (and thus first cell height and aspect ratio) and boundary error. Finally, the parallel efficiency is studied for the two adaptive methods, unit displacement and prescribed displacement for both the CPU as well as the memory formulation with a 2D oscillating and translating airfoil with oscillating flap, a 3D flexible locally deforming tube and deforming wind turbine blade. Generally, the memory formulation requires less work (due to the large amount of work required for evaluating RBF's), but the parallel efficiency reduces due to the limited
Network stratification analysis for identifying function-specific network layers.
Zhang, Chuanchao; Wang, Jiguang; Zhang, Chao; Liu, Juan; Xu, Dong; Chen, Luonan
2016-04-22
A major challenge of systems biology is to capture the rewiring of biological functions (e.g. signaling pathways) in a molecular network. To address this problem, we proposed a novel computational framework, namely network stratification analysis (NetSA), to stratify the whole biological network into various function-specific network layers corresponding to particular functions (e.g. KEGG pathways), which transform the network analysis from the gene level to the functional level by integrating expression data, the gene/protein network and gene ontology information altogether. The application of NetSA in yeast and its comparison with a traditional network-partition both suggest that NetSA can more effectively reveal functional implications of network rewiring and extract significant phenotype-related biological processes. Furthermore, for time-series or stage-wise data, the function-specific network layer obtained by NetSA is also shown to be able to characterize the disease progression in a dynamic manner. In particular, when applying NetSA to hepatocellular carcinoma and type 1 diabetes, we can derive functional spectra regarding the progression of the disease, and capture active biological functions (i.e. active pathways) in different disease stages. The additional comparison between NetSA and SPIA illustrates again that NetSA could discover more complete biological functions during disease progression. Overall, NetSA provides a general framework to stratify a network into various layers of function-specific sub-networks, which can not only analyze a biological network on the functional level but also investigate gene rewiring patterns in biological processes. PMID:26879865
Computational network design from functional specifications
Peng, Chi Han
2016-07-11
Connectivity and layout of underlying networks largely determine agent behavior and usage in many environments. For example, transportation networks determine the flow of traffic in a neighborhood, whereas building floorplans determine the flow of people in a workspace. Designing such networks from scratch is challenging as even local network changes can have large global effects. We investigate how to computationally create networks starting from only high-level functional specifications. Such specifications can be in the form of network density, travel time versus network length, traffic type, destination location, etc. We propose an integer programming-based approach that guarantees that the resultant networks are valid by fulfilling all the specified hard constraints and that they score favorably in terms of the objective function. We evaluate our algorithm in two different design settings, street layout and floorplans to demonstrate that diverse networks can emerge purely from high-level functional specifications.
Institute of Scientific and Technical Information of China (English)
Ping Jiang; Hongyi Wu; Jieqing Tan
2006-01-01
The generalized Ball curves of Wang-Said type with a position parameter L not only unify the Wang-Ball curves and the Said-Ball curves, but also include several useful intermediate curves. This paper presents the dual functionals for the generalized Ball basis of Wang-Said type. The relevant basis transformation formulae are also worked out.
Circadian gating of neuronal functionality: a basis for iterative metaplasticity.
Iyer, Rajashekar; Wang, Tongfei A; Gillette, Martha U
2014-01-01
Brain plasticity, the ability of the nervous system to encode experience, is a modulatory process leading to long-lasting structural and functional changes. Salient experiences induce plastic changes in neurons of the hippocampus, the basis of memory formation and recall. In the suprachiasmatic nucleus (SCN), the central circadian (~24-h) clock, experience with light at night induces changes in neuronal state, leading to circadian plasticity. The SCN's endogenous ~24-h time-generator comprises a dynamic series of functional states, which gate plastic responses. This restricts light-induced alteration in SCN state-dynamics and outputs to the nighttime. Endogenously generated circadian oscillators coordinate the cyclic states of excitability and intracellular signaling molecules that prime SCN receptivity to plasticity signals, generating nightly windows of susceptibility. We propose that this constitutes a paradigm of ~24-h iterative metaplasticity, the repeated, patterned occurrence of susceptibility to induction of neuronal plasticity. We detail effectors permissive for the cyclic susceptibility to plasticity. We consider similarities of intracellular and membrane mechanisms underlying plasticity in SCN circadian plasticity and in hippocampal long-term potentiation (LTP). The emerging prominence of the hippocampal circadian clock points to iterative metaplasticity in that tissue as well. Exploring these links holds great promise for understanding circadian shaping of synaptic plasticity, learning, and memory. PMID:25285070
Institute of Scientific and Technical Information of China (English)
王孔森; 盛戈皞; 孙旭日; 王威; 王世强; 江秀臣
2013-01-01
在线预测输电线路的动态热容量，合理安排负荷高峰时期运行方式和调度管理，对输电线路的安全和经济运行具有重要意义，同时也对确定风电等间歇式可再生能源的接入容量提供技术支持。为此，提出了利用径向基神经网络实现线路动态容量的在线预测方法。该方法首先利用径向基神经网络进行风速和日照辐射温度的在线学习和预测，基于IEEE 738标准进行输电线路动态容量的在线短期预测。利用典型的夏季和冬季实测数据进行动态容量预测后发现，预测未来1、2、4 h的动态容量的最大相对误差分别在10%、20%、40%以内。将短期的负荷预测与该方法结合起来，可为电力紧张地区和负荷高峰时期的智能调度提供决策支持。%It is significant for secure and economic operation of transmission lines to predict dynamic line rating (DLR) in realtime mode and reasonably arrange the operation mode during peak load period and scheduling management, meanwhile it can provide technical support for determining grid-connected capacity of intermittent renewable energy resources such as wind power and so on. For this reason, using redial base function neural network (RBFNN) an online prediction method to implement the prediction of DLR is proposed. Firstly, the online learning and prediction of wind and sunshine radiation temperature is performed by RBFNN, then based on IEEE 738 standard the online short-term DLR is predicted. In the prediction of DLR by use of typical measured data in summer season and winter season, it is found that the maximum relative error in the prediction of dynamic capacity for next one, two and four hours are less than 10%, 20% and 40% respectively. Combining the short-term load forecasting method with the proposed method, the decision-making can be provided to intelligent dispatching during the peak load period in the region lacking of power supply.
Network Physiology reveals relations between network topology and physiological function
Bashan, Amir; Kantelhardt, Jan W; Havlin, Shlomo; Ivanov, Plamen Ch; 10.1038/ncomms1705
2012-01-01
The human organism is an integrated network where complex physiologic systems, each with its own regulatory mechanisms, continuously interact, and where failure of one system can trigger a breakdown of the entire network. Identifying and quantifying dynamical networks of diverse systems with different types of interactions is a challenge. Here, we develop a framework to probe interactions among diverse systems, and we identify a physiologic network. We find that each physiologic state is characterized by a specific network structure, demonstrating a robust interplay between network topology and function. Across physiologic states the network undergoes topological transitions associated with fast reorganization of physiologic interactions on time scales of a few minutes, indicating high network flexibility in response to perturbations. The proposed system-wide integrative approach may facilitate the development of a new field, Network Physiology.
Collision avoidance for a mobile robot based on radial basis function hybrid force control technique
Institute of Scientific and Technical Information of China (English)
Wen Shu-Huan
2009-01-01
Collision avoidance is always difficult in the planning path for a mobile robot. In this paper, the virtual force field between a mobile robot and an obstacle is formed and regulated to maintain a desired distance by hybrid force control algorithm. Since uncertainties from robot dynamics and obstacle degrade the performance of a collision avoidance task, intelligent control is used to compensate for the uncertainties. A radial basis function (RBF) neural network is used to regulate the force field of an accurate distance between a robot and an obstacle in this paper and then simulation studies are conducted to confirm that the proposed algorithm is effective.
Kaye, Jason; Yang, Chao
2014-01-01
Kohn-Sham density functional theory is one of the most widely used electronic structure theories. The recently developed adaptive local basis functions form an accurate and systematically improvable basis set for solving Kohn-Sham density functional theory using discontinuous Galerkin methods, requiring a small number of basis functions per atom. In this paper we develop residual-based a posteriori error estimates for the adaptive local basis approach, which can be used to guide non-uniform basis refinement for highly inhomogeneous systems such as surfaces and large molecules. The adaptive local basis functions are non-polynomial basis functions, and standard a posteriori error estimates for $hp$-refinement using polynomial basis functions do not directly apply. We generalize the error estimates for $hp$-refinement to non-polynomial basis functions. We demonstrate the practical use of the a posteriori error estimator in performing three-dimensional Kohn-Sham density functional theory calculations for quasi-2D...
Piecewise nonlinear image registration using DCT basis functions
Gan, Lin; Agam, Gady
2015-03-01
The deformation field in nonlinear image registration is usually modeled by a global model. Such models are often faced with the problem that a locally complex deformation cannot be accurately modeled by simply increasing degrees of freedom (DOF). In addition, highly complex models require additional regularization which is usually ineffective when applied globally. Registering locally corresponding regions addresses this problem in a divide and conquer strategy. In this paper we propose a piecewise image registration approach using Discrete Cosine Transform (DCT) basis functions for a nonlinear model. The contributions of this paper are three-folds. First, we develop a multi-level piecewise registration framework that extends the concept of piecewise linear registration and works with any nonlinear deformation model. This framework is then applied to nonlinear DCT registration. Second, we show how adaptive model complexity and regularization could be applied for local piece registration, thus accounting for higher variability. Third, we show how the proposed piecewise DCT can overcome the fundamental problem of a large curvature matrix inversion in global DCT when using high degrees of freedoms. The proposed approach can be viewed as an extension of global DCT registration where the overall model complexity is increased while achieving effective local regularization. Experimental evaluation results provide comparison of the proposed approach to piecewise linear registration using an affine transformation model and a global nonlinear registration using DCT model. Preliminary results show that the proposed approach achieves improved performance.
Nested Canalizing Functions and Their Networks
Kadelka, Claus; Adeyeye, John O; Laubenbacher, Reinhard
2014-01-01
The concept of a nested canalizing Boolean function has been studied over the last decade in the context of understanding the regulatory logic of molecular interaction networks, such as gene regulatory networks. Such networks are predominantly governed by nested canalizing functions. Derrida values are frequently used to analyze the robustness of a Boolean network to perturbations. This paper introduces closed formulas for the calculation of Derrida values of networks governed by Boolean nested canalizing functions, which previously required extensive simulations. Recently, the concept of nested canalizing functions has been generalized to include multistate functions, and a recursive formula has been derived for their number, as a function of the number of variables. This paper contains a detailed analysis of the class of nested canalizing functions over an arbitrary finite field. In addition, the concept of nested canalization is further generalized and closed formulas for the number of such generalized fun...
Understanding biological functions through molecular networks
Institute of Scientific and Technical Information of China (English)
Jing-Dong Jackie Han
2008-01-01
The completion of genome sequences and subsequent high-throughput mapping of molecular networks have allowed us to study biology from the network perspective. Experimental, statistical and mathematical modeling approaches have been employed to study the structure, function and dynamics of molecular networks, and begin to reveal important links of various network properties to the functions of the biological systems. In agreement with these functional links, evolutionary selection of a network is apparently based on the function, rather than directly on the structure of the network. Dynamic modularity is one of the prominent features of molecular networks. Taking advantage of such a feature may simplify network-based biological studies through construction of process-specific modular networks and provide functional and mechanistic insights linking genotypic variations to complex traits or diseases, which is likely to be a key approach in the next wave of understanding complex human diseases. With the development of ready-to-use network analysis and modeling tools the networks approaches will be infused into everyday biological research in the near future.
Institute of Scientific and Technical Information of China (English)
顾成奎; 王正欧; 孙雅明
2003-01-01
A new method for identifying nonlinear time-varying systems with unknown structure is presented. The method extends the application area of basis sequence identification. The essential idea is to utilize the learning and nonlinear approximating ability of neural networks to model the non-linearity of the system, characterize time-varying dynamics of the system by the time-varying parametric vector of the network, then the parametric vector of the network is approximated by a weighted sum of known basis sequences. Because of black-box modeling ability of neural networks, the presented method can identify nonlinear time-varying systems with unknown structure. In order to improve the real-time capability of the algorithm, the neural network is trained by a simple fast learning algorithm based on local least squares presented by the authors. The effectiveness and the performance of the method are demonstrated by some simulation results.
Framework for Ethernet Network Functionality Testing
Directory of Open Access Journals (Sweden)
Mirza Aamir Mehmood
2011-11-01
Full Text Available Computer networks and telecommunication systems use a wide range of applications. Therefore, the power and complexity of computer networks are increasing every day which enhances the possibilities of the end user, but also makes harder the work of those who have to design, maintain and make a network efficient, optimized and secure. Ethernet functionality testing as a generic term used for checking connectivity, throughput and capability to transfer packets over the network. Especially in the packet-switch environment, Ethernet testing has become an essential part for deploying a reliable network. A platform and vendor independent framework is required to verify and test the functionality of the Ethernet network and to verify the functionality and performance of the TCP/IP stack. NetBurst is developed for Ethernet functionality testing
The functional consequences of mutualistic network architecture.
Directory of Open Access Journals (Sweden)
José M Gómez
Full Text Available The architecture and properties of many complex networks play a significant role in the functioning of the systems they describe. Recently, complex network theory has been applied to ecological entities, like food webs or mutualistic plant-animal interactions. Unfortunately, we still lack an accurate view of the relationship between the architecture and functioning of ecological networks. In this study we explore this link by building individual-based pollination networks from eight Erysimum mediohispanicum (Brassicaceae populations. In these individual-based networks, each individual plant in a population was considered a node, and was connected by means of undirected links to conspecifics sharing pollinators. The architecture of these unipartite networks was described by means of nestedness, connectivity and transitivity. Network functioning was estimated by quantifying the performance of the population described by each network as the number of per-capita juvenile plants produced per population. We found a consistent relationship between the topology of the networks and their functioning, since variation across populations in the average per-capita production of juvenile plants was positively and significantly related with network nestedness, connectivity and clustering. Subtle changes in the composition of diverse pollinator assemblages can drive major consequences for plant population performance and local persistence through modifications in the structure of the inter-plant pollination networks.
Network architecture functional description and design
Energy Technology Data Exchange (ETDEWEB)
Stans, L.; Bencoe, M.; Brown, D.; Kelly, S.; Pierson, L.; Schaldach, C.
1989-05-25
This report provides a top level functional description and design for the development and implementation of the central network to support the next generation of SNL, Albuquerque supercomputer in a UNIX{reg sign} environment. It describes the network functions and provides an architecture and topology.
Estimation of State of Charge of Lead Acid Battery using Radial Basis Function
Sauradip, M; Sinha, SK; K Muthukumar
2001-01-01
A Radial Basis Function based learning system method has been proposed for estimation of State of Charge (SOC) of Lead Acid Battery. Coulomb metric method is used for SOC estimation with correction factor computed by Radial Basis Function Method. Radial basis function based technique is used for learning battery performance variation with time and other parameters. Experimental results are included.
Directory of Open Access Journals (Sweden)
Barbara Martini
2016-06-01
Full Text Available Emerging technologies such as Software-Defined Networks (SDN and Network Function Virtualization (NFV promise to address cost reduction and flexibility in network operation while enabling innovative network service delivery models. However, operational network service delivery solutions still need to be developed that actually exploit these technologies, especially at the multi-provider level. Indeed, the implementation of network functions as software running over a virtualized infrastructure and provisioned on a service basis let one envisage an ecosystem of network services that are dynamically and flexibly assembled by orchestrating Virtual Network Functions even across different provider domains, thereby coping with changeable user and service requirements and context conditions. In this paper we propose an approach that adopts Service-Oriented Architecture (SOA technology-agnostic architectural guidelines in the design of a solution for orchestrating and dynamically chaining Virtual Network Functions. We discuss how SOA, NFV, and SDN may complement each other in realizing dynamic network function chaining through service composition specification, service selection, service delivery, and placement tasks. Then, we describe the architecture of a SOA-inspired NFV orchestrator, which leverages SDN-based network control capabilities to address an effective delivery of elastic chains of Virtual Network Functions. Preliminary results of prototype implementation and testing activities are also presented. The benefits for Network Service Providers are also described that derive from the adaptive network service provisioning in a multi-provider environment through the orchestration of computing and networking services to provide end users with an enhanced service experience.
Bruni, S.; Llombart Juan, N.; Neto, A.; Gerini, G.; Maci, S.
2004-01-01
A general algorithm for the analysis of microstrip coupled leaky wave slot antennas was discussed. The method was based on the construction of physically appealing entire domain Methods of Moments (MoM) basis function that allowed a consistent reduction of the number of unknowns and of total computa
Hierarchical modularity in human brain functional networks
Meunier, D; Fornito, A; Ersche, K D; Bullmore, E T; 10.3389/neuro.11.037.2009
2010-01-01
The idea that complex systems have a hierarchical modular organization originates in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or "modules-within-modules") decomposition of human brain functional networks, measured using functional magnetic resonance imaging (fMRI) in 18 healthy volunteers under no-task or resting conditions. We used a customized template to extract networks with more than 1800 regional nodes, and we applied a fast algorithm to identify nested modular structure at several hierarchical levels. We used mutual information, 0 < I < 1, to estimate the similarity of community structure of networks in different subjects, and to identify the individual network that is most representative of the group. Results show that human brain functional networks have a hierarchical modular organization with a fair degree of similarity between subjects, I=0.63. The largest 5 modules at ...
Generating fracture networks using iterated function systems
Mohrlok, U.; Liedl, R.
1996-03-01
In order to model flow and transport in fractured rocks it is important to know the geometry of the fracture network. A stochastic approach is commonly used to generate a synthetic fracture network from the statistics measured at a natural fracture network. The approach presented herein is able to incorporate the structures found in a natural fracture network into the synthetic fracture network. These synthetic fracture networks are the images generated by Iterated Function Systems (IFS) as introduced by Barnsley (1988). The conditions these IFS have to fulfil to determine images resembling fracture networks and the effects of their parameters on the images are discussed. It is possible to define the parameters of the IFS in order to generate some properties of a fracture network. The image of an IFS consists of many single points and has to be suitably processed for further use.
Controlled stochastic networks in heavy traffic: Convergence of value functions
Budhiraja, Amarjit; 10.1214/11-AAP784
2012-01-01
Scheduling control problems for a family of unitary networks under heavy traffic with general interarrival and service times, probabilistic routing and an infinite horizon discounted linear holding cost are studied. Diffusion control problems, that have been proposed as approximate models for the study of these critically loaded controlled stochastic networks, can be regarded as formal scaling limits of such stochastic systems. However, to date, a rigorous limit theory that justifies the use of such approximations for a general family of controlled networks has been lacking. It is shown that, under broad conditions, the value function of the suitably scaled network control problem converges to that of the associated diffusion control problem. This scaling limit result, in addition to giving a precise mathematical basis for the above approximation approach, suggests a general strategy for constructing near optimal controls for the physical stochastic networks by solving the associated diffusion control problem...
Multiprotocol label-switching network functional description
Owens, Kenneth R.; Kroculick, Joseph
1999-11-01
This paper integrates a functional transport and control layer network architecture for MPLS emphasizing Traffic Engineering concepts such as the specification and provisioning of end-to-end QoS service layer agreements. MPLS transport networks are provisioned considering administrator-defined policies on bandwidth allocation, security, and accounting techniques. The MPLS architecture consists of the transport and control layer networks. The transport layer network is concerned with configuration, packet forwarding, signaling, adaptation to higher layers, and support of higher layers. The control layer network is concerned with policy configuration, management, distribution, definitions, schemas, elements, settings, and enforcement.
An Adaptive Complex Network Model for Brain Functional Networks
Gomez Portillo, Ignacio J.; Gleiser, Pablo M.
2009-01-01
Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show diffe...
Simple models of human brain functional networks.
Vértes, Petra E; Alexander-Bloch, Aaron F; Gogtay, Nitin; Giedd, Jay N; Rapoport, Judith L; Bullmore, Edward T
2012-04-10
Human brain functional networks are embedded in anatomical space and have topological properties--small-worldness, modularity, fat-tailed degree distributions--that are comparable to many other complex networks. Although a sophisticated set of measures is available to describe the topology of brain networks, the selection pressures that drive their formation remain largely unknown. Here we consider generative models for the probability of a functional connection (an edge) between two cortical regions (nodes) separated by some Euclidean distance in anatomical space. In particular, we propose a model in which the embedded topology of brain networks emerges from two competing factors: a distance penalty based on the cost of maintaining long-range connections; and a topological term that favors links between regions sharing similar input. We show that, together, these two biologically plausible factors are sufficient to capture an impressive range of topological properties of functional brain networks. Model parameters estimated in one set of functional MRI (fMRI) data on normal volunteers provided a good fit to networks estimated in a second independent sample of fMRI data. Furthermore, slightly detuned model parameters also generated a reasonable simulation of the abnormal properties of brain functional networks in people with schizophrenia. We therefore anticipate that many aspects of brain network organization, in health and disease, may be parsimoniously explained by an economical clustering rule for the probability of functional connectivity between different brain areas.
Network Assemblies in the Functional Brain
Sepulcre, Jorge; Sabuncu, Mert R.; Johnson, Keith A.
2012-01-01
Purpose of review This review focuses on recent advances in functional connectivity MRI and renewed interest in knowing the large-scale functional network assemblies in the brain. We also consider some methodological aspects of graph theoretical analysis. Recent findings Network science applied to neuroscience is quickly growing in recent years. The characterization of the functional connectomes in normal and pathological brain conditions is now a priority for researchers in the neuropsychiatric field and current findings have provided new insights regarding the pivotal role of network epicenters and specific configurations of the functional networks in the brain. Summary Functional connectivity and its analytical tools are providing organization of the functional brain that will be key for the understanding of pathologies in neurology. PMID:22766721
Optimized basis functions for slot antennas excited by coplanar waveguides
Neto, A.; Maagt, P.de; Maci, S.
2003-01-01
A method is proposed for the analysis of slot antennas excited by coplanar waveguides. First, a standard integral equation for the continuity of the magnetic field is formulated. Then the appropriate equivalent magnetic currents of the method of moments are represented in terms of entire-domain basi
Kanungo, Bikash
2016-01-01
We present a computationally efficient approach to perform large-scale all-electron density functional theory calculations by enriching the classical finite element basis with compactly supported atom-centered numerical basis functions that are constructed from the solution of the Kohn-Sham (KS) problem for single atoms. We term these numerical basis functions as enrichment functions, and the resultant basis as the enriched finite element basis. The enrichment functions are compactly supported through the use of smooth cutoff functions, which enhances the conditioning and maintains the locality of the basis. The integrals involved in the evaluation of the discrete KS Hamiltonian and overlap matrix in the enriched finite element basis are computed using an adaptive quadrature grid based on the characteristics of enrichment functions. Further, we propose an efficient scheme to invert the overlap matrix by using a block-wise matrix inversion in conjunction with special reduced-order quadrature rules to transform...
Main concept of local area network protection on the basis of the SAAM 'TRAFFIC'
International Nuclear Information System (INIS)
In our previous paper we developed a system for acquisition, analysis and management of the network traffic (SAAM 'Traffic') for a segment of the JINR local area computer network (JINR LAN). In our present work we consider well-known scenarios of attacks on local area networks and propose protection methods based on the SAAM 'Traffic'. Although the system for LAN protection is installed on a router computer, it is not analogous to the firewall scheme and, thus, it does not hinder the performance of distributed network applications. This provides a possibility to apply such an approach to GRID-technologies, where network protection on the firewall basis can not be basically used. (author)
Directory of Open Access Journals (Sweden)
Lixia Pei
Full Text Available The clinical application of Traditional Chinese medicine (TCM, using several herbs in combination (called formulas, has a history of more than one thousand years. However, the bioactive compounds that account for their therapeutic effects remain unclear. We hypothesized that the material basis of a formula are those compounds with a high content in the decoction that are maintained at a certain level in the system circulation. Network pharmacology provides new methodological insights for complicated system studies. In this study, we propose combining pharmacokinetic (PK analysis with network pharmacology to explore the material basis of TCM formulas as exemplified by the Bushen Zhuanggu formula (BZ composed of Psoralea corylifolia L., Aconitum carmichaeli Debx., and Cnidium monnieri (L. Cuss. A sensitive and credible liquid chromatography tandem mass spectrometry (LC-MS/MS method was established for the simultaneous determination of 15 compounds present in the three herbs. The concentrations of these compounds in the BZ decoction and in rat plasma after oral BZ administration were determined. Up to 12 compounds were detected in the BZ decoction, but only 5 could be analyzed using PK parameters. Combined PK results, network pharmacology analysis revealed that 4 compounds might serve as the material basis for BZ. We concluded that a sensitive, reliable, and suitable LC-MS/MS method for both the composition and pharmacokinetic study of BZ has been established. The combination of PK with network pharmacology might be a potent method for exploring the material basis of TCM formulas.
A Quasi-Interpolation Satisfying Quadratic Polynomial Reproduction with Radial Basis Functions
Institute of Scientific and Technical Information of China (English)
Li Zha; Renzhong Feng
2007-01-01
In this paper, a new quasi-interpolation with radial basis functions which satisfies quadratic polynomial reproduction is constructed on the infinite set of equally spaced data. A new basis function is constructed by making convolution integral with a constructed spline and a given radial basis function. In particular, for twicely differentiable function the proposed method provides better approximation and also takes care of derivatives approximation.
A universal formula for network functions
DEFF Research Database (Denmark)
Skelboe, Stig
1975-01-01
A linear electrical network can be described in a convenient way by means of the node equations. This letter presents a universal formula which expresses any network function as the quotient of two determinants. The determinants belong to matrices derived from the indefinite nodal admittance...
Directory of Open Access Journals (Sweden)
M. Safish Mary
2012-04-01
Full Text Available Classification of large amount of data is a time consuming process but crucial for analysis and decision making. Radial Basis Function networks are widely used for classification and regression analysis. In this paper, we have studied the performance of RBF neural networks to classify the sales of cars based on the demand, using kernel density estimation algorithm which produces classification accuracy comparable to data classification accuracy provided by support vector machines. In this paper, we have proposed a new instance based data selection method where redundant instances are removed with help of a threshold thus improving the time complexity with improved classification accuracy. The instance based selection of the data set will help reduce the number of clusters formed thereby reduces the number of centers considered for building the RBF network. Further the efficiency of the training is improved by applying a hierarchical clustering technique to reduce the number of clusters formed at every step. The paper explains the algorithm used for classification and for conditioning the data. It also explains the complexities involved in classification of sales data for analysis and decision-making.
Directory of Open Access Journals (Sweden)
Bhuvana R.
2015-02-01
Full Text Available Depression is a severe and well-known public health challenge. Depression is one of the most common psychological problems affecting nearly everyone either personally or through a family member. This paper proposes neural network algorithm for faster learning of depression data and classifying the depression. Implementation of neural networks methods for depression data mining using Back Propagation Algorithm (BPA and Radial Basis Function (RBF are presented. Experimental data were collected with 21 depression variables used as inputs for artificial neural network (ANN and one desired category of depression as the output variable for training and testing proposed BPA/RBF algorithms. Using the data collected, the training patterns, and test patterns are obtained. The input patterns are pre-processed and presented to the input layer of BPA/RBF. The optimum number of nodes required in the hidden layer of BPA/RBF is obtained, based on the change in the mean squared error dynamically, during the successive sets of iterations. The output of BPA is given as input to RBF. Through the combined topology, the work proves to be an efficient system for diagnosis of depression.
From networks of protein interactions to networks of functional dependencies
Directory of Open Access Journals (Sweden)
Luciani Davide
2012-05-01
Full Text Available Abstract Background As protein-protein interactions connect proteins that participate in either the same or different functions, networks of interacting and functionally annotated proteins can be converted into process graphs of inter-dependent function nodes (each node corresponding to interacting proteins with the same functional annotation. However, as proteins have multiple annotations, the process graph is non-redundant, if only proteins participating directly in a given function are included in the related function node. Results Reasoning that topological features (e.g., clusters of highly inter-connected proteins might help approaching structured and non-redundant understanding of molecular function, an algorithm was developed that prioritizes inclusion of proteins into the function nodes that best overlap protein clusters. Specifically, the algorithm identifies function nodes (and their mutual relations, based on the topological analysis of a protein interaction network, which can be related to various biological domains, such as cellular components (e.g., peroxisome and cellular bud or biological processes (e.g., cell budding of the model organism S. cerevisiae. Conclusions The method we have described allows converting a protein interaction network into a non-redundant process graph of inter-dependent function nodes. The examples we have described show that the resulting graph allows researchers to formulate testable hypotheses about dependencies among functions and the underlying mechanisms.
Physiological basis, use and abuse of functional imaging
International Nuclear Information System (INIS)
Functional imaging in the contrast to conventional methods of nuclear medicine is defined. The importance of an isomorphic physiological model as a link between the physics of the test and the clinical problem is discussed. Clinical, physiological and mathematical criteria for use of computer assisted functional images are developed. (WU)
Fuzzy Functional Dependencies and Bayesian Networks
Institute of Scientific and Technical Information of China (English)
LIU WeiYi(刘惟一); SONG Ning(宋宁)
2003-01-01
Bayesian networks have become a popular technique for representing and reasoning with probabilistic information. The fuzzy functional dependency is an important kind of data dependencies in relational databases with fuzzy values. The purpose of this paper is to set up a connection between these data dependencies and Bayesian networks. The connection is done through a set of methods that enable people to obtain the most information of independent conditions from fuzzy functional dependencies.
Constructing social networks on the basis of task and knowledge networks using WESTT
Stanton, Neville A.; Baber, Chris; Houghton, Robert J.
2007-01-01
In this paper, we propose that it is possible to combine conventional Human Factors approaches to task analysis and crew models to the study of covert networks. The intention is to add to the debate and methods that surround the study of covert networks. We suggest that it is possible to use Human Factors methods to both inform Social Network Analysis approaches, and to provide representations by which intelligence can be shared and explored. The paper is couched in terms of the WESTT approac...
Institute of Scientific and Technical Information of China (English)
朱国俊; 冯建军; 郭鹏程; 罗兴锜
2014-01-01
, the Bezier curve was used to parameterize the hydrofoil. The Latin Hypercube experiment design method was used to select the sample points in the design space which were used for training the Radial Basis Function neural network. The hydrodynamic performance for each sample was calculated by the computational fluid dynamic method, and then the Radial Basis Function neural network would be trained by these sample points. After the neural network had been trained, the multi-point optimization method of hydrofoil was solved by combining the NSGA-II method and the Radial Basis Function neural network. The method mentioned above was applied to the optimal design of NACA63-815 hydrofoil, and the optimization problems of the hydrofoil in three typical conditions in which the attack angle is 0, 6º and 12º were mainly studied in this paper. After optimization, two optimized hydrofoils were selected in the Pareto solution to compare with initial, which were named Optimal A and Optimal B. According to the CFD simulation, the optimized hydrofoil’s performance was gotten and compared with the initial hydrofoil. By comparison, it was found that the drag coefficient of the optimized hydrofoil in the three conditions are less than or equal to the initial. Moreover, the lift-drag ratios of the Optimal A hydrofoil in which the attack angles is 0, 6° and 12° have been improve by 4.6%, 4.4%and 22.8%respectively. And the lift-drag ratios of the Optimal B hydrofoils have also been improved by 6.6%, 3.8%and 16.6%respectively. In addition, according to the comparison of the optimized and initial hydrofoil’s pressure coefficient at the 12°attack angle, it can be found that the optimized hydrofoil can effectively suppress the stall phenomenon. Finally, two conclusions can be drawn from the optimal results. Firstly, use the Radial Basis Function neural network to replace the CFD simulation can effectively decrease the time that the optimization cycle spent. Secondly, the hydrofoil
Prediction of Reagents Needs Using Radial Basis Function in Teaching Hospital
Directory of Open Access Journals (Sweden)
Indrabayu
2015-08-01
Full Text Available A robust reagents prediction is able to support the service improvement in laboratories. In this paper, Radial Basis Function Networks (RBFN method with (3, Q, 1 architecture is used to predict two types of reagents needs, i.e. SD Bioline HBsAg and SD Bioline Anti HCV. Data of reagents from 2012 - 2013 are used as training data, whereas 2014 data are used as comparative data for the prediction result. In RBFN training, the best condition obtained when the spread value is 4 with RMSE 1.63E-06 for both types of reagents. The prediction results with RBFN methods reached 99% with correlation value of 0.99 for each reagents. RBFN method shows better prediction result compared to BPNN method with prediction of 92%.
DEFF Research Database (Denmark)
Darden, Safi-Kirstine; Steffensen, Lise K.; Dabelsteen, Torben
2008-01-01
In species where individuals are widely spaced instantaneous signals cannot readily form the basis of communication networks, that is several individuals within signalling range of each other. However, markings, signals that remain in the environment after the signaller has left, may fulfil this...... role. In this study, we have investigated the possible function of swift fox, Vulpes velox, latrines, collections of scat, urine and possibly other secretions, in a communication network context. We found that latrines had higher frequencies of occurrence inside the core (defined as the 50% kernel....... That is, they function in territory defence in the exclusive areas of a pair's core and as centres for information exchange in the outer areas of a pair's home-range that overlap with neighbouring foxes. We discuss the possible information content of latrines and the possibility of latrines forming the...
OPTIMIZATION OF CUTTING PARAMETERS ON THE BASIS OF SEMANTIC NETWORK USAGE
Directory of Open Access Journals (Sweden)
V. M. Pashkevich
2011-01-01
Full Text Available The paper considers problems on accuracy assurance of machine component cutting while using edge tools. An approach based on artificial intelligence technologies in particular technologies of functional semantic networks. The paper analyzes a possibility to apply functional semantic networks for optimization of cutting parameters. An intellectual system intended for solution of applied problems is described in the paper. The paper reveals a system structure and an example for setting optimal cutting speed is cited in the paper.
Using symmetry-adapted optimized sum-of-products basis functions to calculate vibrational spectra
Leclerc, Arnaud
2016-01-01
Vibrational spectra can be computed without storing full-dimensional vectors by using low-rank sum-of-products (SOP) basis functions. We introduce symmetry constraints in the SOP basis functions to make it possible to separately calculate states in different symmetry subgroups. This is done using a power method to compute eigenvalues and an alternating least squares method to optimize basis functions. Owing to the fact that the power method favours the convergence of the lowest states, one must be careful not to exclude basis functions of some symmetries. Exploiting symmetry facilitates making assignments and improves the accuracy. The method is applied to the acetonitrile molecule.
Gerini, G.; Maci, S.; Bruni, S.; Llombart, N.; Neto, A.
2005-01-01
Problem matched basis functions are proposed for the method of moments analysis of printed slot coupled microstrips. The appropriate equivalent currents of the integral equation kernel are represented in terms of two sets of entire domain basis functions. These functions synthesize on one hand the r
Physiologic Basis for Improved Pulmonary Function after Lung Volume Reduction
Fessler, Henry E.; Scharf, Steven M; Ingenito, Edward P.; McKenna, Robert J.; Sharafkhaneh, Amir
2008-01-01
It is not readily apparent how pulmonary function could be improved by resecting portions of the lung in patients with emphysema. In emphysema, elevation in residual volume relative to total lung capacity reduces forced expiratory volumes, increases inspiratory effort, and impairs inspiratory muscle mechanics. Lung volume reduction surgery (LVRS) better matches the size of the lungs to the size of the thorax containing them. This restores forced expiratory volumes and the mechanical advantage...
Design Optimization of Centrifugal Pump Using Radial Basis Function Metamodels
Yu Zhang; Jinglai Wu; Yunqing Zhang; Liping Chen
2014-01-01
Optimization design of centrifugal pump is a typical multiobjective optimization (MOO) problem. This paper presents an MOO design of centrifugal pump with five decision variables and three objective functions, and a set of centrifugal pumps with various impeller shroud shapes are studied by CFD numerical simulations. The important performance indexes for centrifugal pump such as head, efficiency, and required net positive suction head (NPSHr) are investigated, and the results indicate that th...
Functional brain networks in schizophrenia: a review
Directory of Open Access Journals (Sweden)
Vince D Calhoun
2009-08-01
Full Text Available Functional magnetic resonance imaging (fMRI has become a major technique for studying cognitive function and its disruption in mental illness, including schizophrenia. The major proportion of imaging studies focused primarily upon identifying regions which hemodynamic response amplitudes covary with particular stimuli and differentiate between patient and control groups. In addition to such amplitude based comparisons, one can estimate temporal correlations and compute maps of functional connectivity between regions which include the variance associated with event related responses as well as intrinsic fluctuations of hemodynamic activity. Functional connectivity maps can be computed by correlating all voxels with a seed region when a spatial prior is available. An alternative are multivariate decompositions such as independent component analysis (ICA which extract multiple components, each of which is a spatially distinct map of voxels with a common time course. Recent work has shown that these networks are pervasive in relaxed resting and during task performance and hence provide robust measures of intact and disturbed brain activity. This in turn bears the prospect of yielding biomarkers for schizophrenia, which can be described both in terms of disrupted local processing as well as altered global connectivity between large scale networks. In this review we will summarize functional connectivity measures with a focus upon work with ICA and discuss the meaning of intrinsic fluctuations. In addition, examples of how brain networks have been used for classification of disease will be shown. We present work with functional network connectivity, an approach that enables the evaluation of the interplay between multiple networks and how they are affected in disease. We conclude by discussing new variants of ICA for extracting maximally group discriminative networks from data. In summary, it is clear that identification of brain networks and their
Avian magnetic compass: Its functional properties and physical basis
Directory of Open Access Journals (Sweden)
Roswitha WILTSCHKO, Wolfgang WILTSCHKO
2010-06-01
Full Text Available The avian magnetic compass was analyzed in bird species of three different orders – Passeriforms, Columbiforms and Galliforms – and in three different behavioral contexts, namely migratory orientation, homing and directional conditioning. The respective findings indicate similar functional properties: it is an inclination compass that works only within a functional window around the ambient magnetic field intensity; it tends to be lateralized in favor of the right eye, and it is wavelength-dependent, requiring light from the short-wavelength range of the spectrum. The underlying physical mechanisms have been identified as radical pair processes, spin-chemical reactions in specialized photopigments. The iron-based receptors in the upper beak do not seem to be involved. The existence of the same type of magnetic compass in only very distantly related bird species suggests that it may have been present already in the common ancestors of all modern birds, where it evolved as an all-purpose compass mechanism for orientation within the home range [Current Zoology 56 (3: 265–276, 2010].
Brown, James; Carrington, Tucker
2016-06-01
In this paper we show that it is possible to use an iterative eigensolver in conjunction with Halverson and Poirier's symmetrized Gaussian (SG) basis [T. Halverson and B. Poirier, J. Chem. Phys. 137, 224101 (2012)] to compute accurate vibrational energy levels of molecules with as many as five atoms. This is done, without storing and manipulating large matrices, by solving a regular eigenvalue problem that makes it possible to exploit direct-product structure. These ideas are combined with a new procedure for selecting which basis functions to use. The SG basis we work with is orders of magnitude smaller than the basis made by using a classical energy criterion. We find significant convergence errors in previous calculations with SG bases. For sum-of-product Hamiltonians, SG bases large enough to compute accurate levels are orders of magnitude larger than even simple pruned bases composed of products of harmonic oscillator functions.
Basis Function Sampling: A New Paradigm for Material Property Computation
Whitmer, Jonathan K.; Chiu, Chi-cheng; Joshi, Abhijeet A.; de Pablo, Juan J.
2014-11-01
Wang-Landau sampling, and the associated class of flat histogram simulation methods have been remarkably helpful for calculations of the free energy in a wide variety of physical systems. Practically, convergence of these calculations to a target free energy surface is hampered by reliance on parameters which are unknown a priori. Here, we derive and implement a method built upon orthogonal functions which is fast, parameter-free, and (importantly) geometrically robust. The method is shown to be highly effective in achieving convergence. An important feature of this method is its ability to attain arbitrary levels of description for the free energy. It is thus ideally suited to in silico measurement of elastic moduli and other material properties related to free energy perturbations. We demonstrate the utility of such applications by applying our method to calculate the Frank elastic constants of the Lebwohl-Lasher model of liquid crystals.
An efficient method for ectopic beats cancellation based on radial basis function.
Mateo, Jorge; Torres, Ana; Rieta, José J
2011-01-01
The analysis of the surface Electrocardiogram (ECG) is the most extended noninvasive technique in cardiological diagnosis. In order to properly use the ECG, we need to cancel out ectopic beats. These beats may occur in both normal subjects and patients with heart disease, and their presence represents an important source of error which must be handled before any other analysis. This paper presents a method for electrocardiogram ectopic beat cancellation based on Radial Basis Function Neural Network (RBFNN). A train-able neural network ensemble approach to develop customized electrocardiogram beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care is presented. Six types of beats including: Normal Beats (NB); Premature Ventricular Contractions (PVC); Left Bundle Branch Blocks (LBBB); Right Bundle Branch Blocks (RBBB); Paced Beats (PB) and Ectopic Beats (EB) are obtained from the MIT-BIH arrhythmia database. Four morphological features are extracted from each beat after the preprocessing of the selected records. Average Results for the RBFNN based method provided an ectopic beat reduction (EBR) of (mean ± std) EBR = 7, 23 ± 2.18 in contrast to traditional compared methods that, for the best case, yielded EBR = 4.05 ± 2.13. The results prove that RBFNN based methods are able to obtain a very accurate reduction of ectopic beats together with low distortion of the QRST complex.
Functional network inference of the suprachiasmatic nucleus.
Abel, John H; Meeker, Kirsten; Granados-Fuentes, Daniel; St John, Peter C; Wang, Thomas J; Bales, Benjamin B; Doyle, Francis J; Herzog, Erik D; Petzold, Linda R
2016-04-19
In the mammalian suprachiasmatic nucleus (SCN), noisy cellular oscillators communicate within a neuronal network to generate precise system-wide circadian rhythms. Although the intracellular genetic oscillator and intercellular biochemical coupling mechanisms have been examined previously, the network topology driving synchronization of the SCN has not been elucidated. This network has been particularly challenging to probe, due to its oscillatory components and slow coupling timescale. In this work, we investigated the SCN network at a single-cell resolution through a chemically induced desynchronization. We then inferred functional connections in the SCN by applying the maximal information coefficient statistic to bioluminescence reporter data from individual neurons while they resynchronized their circadian cycling. Our results demonstrate that the functional network of circadian cells associated with resynchronization has small-world characteristics, with a node degree distribution that is exponential. We show that hubs of this small-world network are preferentially located in the central SCN, with sparsely connected shells surrounding these cores. Finally, we used two computational models of circadian neurons to validate our predictions of network structure.
Functional network inference of the suprachiasmatic nucleus
Energy Technology Data Exchange (ETDEWEB)
Abel, John H.; Meeker, Kirsten; Granados-Fuentes, Daniel; St. John, Peter C.; Wang, Thomas J.; Bales, Benjamin B.; Doyle, Francis J.; Herzog, Erik D.; Petzold, Linda R.
2016-04-04
In the mammalian suprachiasmatic nucleus (SCN), noisy cellular oscillators communicate within a neuronal network to generate precise system-wide circadian rhythms. Although the intracellular genetic oscillator and intercellular biochemical coupling mechanisms have been examined previously, the network topology driving synchronization of the SCN has not been elucidated. This network has been particularly challenging to probe, due to its oscillatory components and slow coupling timescale. In this work, we investigated the SCN network at a single-cell resolution through a chemically induced desynchronization. We then inferred functional connections in the SCN by applying the maximal information coefficient statistic to bioluminescence reporter data from individual neurons while they resynchronized their circadian cycling. Our results demonstrate that the functional network of circadian cells associated with resynchronization has small-world characteristics, with a node degree distribution that is exponential. We show that hubs of this small-world network are preferentially located in the central SCN, with sparsely connected shells surrounding these cores. Finally, we used two computational models of circadian neurons to validate our predictions of network structure.
Functional network inference of the suprachiasmatic nucleus.
Abel, John H; Meeker, Kirsten; Granados-Fuentes, Daniel; St John, Peter C; Wang, Thomas J; Bales, Benjamin B; Doyle, Francis J; Herzog, Erik D; Petzold, Linda R
2016-04-19
In the mammalian suprachiasmatic nucleus (SCN), noisy cellular oscillators communicate within a neuronal network to generate precise system-wide circadian rhythms. Although the intracellular genetic oscillator and intercellular biochemical coupling mechanisms have been examined previously, the network topology driving synchronization of the SCN has not been elucidated. This network has been particularly challenging to probe, due to its oscillatory components and slow coupling timescale. In this work, we investigated the SCN network at a single-cell resolution through a chemically induced desynchronization. We then inferred functional connections in the SCN by applying the maximal information coefficient statistic to bioluminescence reporter data from individual neurons while they resynchronized their circadian cycling. Our results demonstrate that the functional network of circadian cells associated with resynchronization has small-world characteristics, with a node degree distribution that is exponential. We show that hubs of this small-world network are preferentially located in the central SCN, with sparsely connected shells surrounding these cores. Finally, we used two computational models of circadian neurons to validate our predictions of network structure. PMID:27044085
Schwenke, David W.; Truhlar, Donald G.
1990-01-01
The Generalized Newton Variational Principle for 3D quantum mechanical reactive scattering is briefly reviewed. Then three techniques are described which improve the efficiency of the computations. First, the fact that the Hamiltonian is Hermitian is used to reduce the number of integrals computed, and then the properties of localized basis functions are exploited in order to eliminate redundant work in the integral evaluation. A new type of localized basis function with desirable properties is suggested. It is shown how partitioned matrices can be used with localized basis functions to reduce the amount of work required to handle the complex boundary conditions. The new techniques do not introduce any approximations into the calculations, so they may be used to obtain converged solutions of the Schroedinger equation.
Institute of Scientific and Technical Information of China (English)
于旭东; 魏学通; 李莹; 龙兴武
2012-01-01
在激光陀螺单轴旋转惯性导航系统中,单轴旋转可以自动补偿垂直于旋转轴上的惯性器件误差,却不能消除旋转轴方向上惯性器件的误差,因此单轴旋转惯性导航系统的导航精度主要由轴向陀螺漂移决定.提出了一种基于径向基函数神经网络的轴向陀螺漂移辨识方法,利用系统纬度误差和温度变化量作为训练集,针对系统热态、冷态两种情况对RBF神经网络进行训练,对轴向陀螺漂移的辨识精度达到0.0003°/h.试验结果表明:该方法能够有效地辨识轴向陀螺漂移,使系统达到较高的导航精度,满足实际应用的需要.%In the single-axis rotation inertial navigation system with ring laser gyroscope ( RLG), the single-axis rotation can compensate the vertical errors of the inertial apparatus automatically, but cannot compensate the axial vertical errors, so the precision of the system is determined by the drift of the axial RLG. A novel identification method based on radial basis function network is proposed for the axial RLG drift. The inputs of the network are the latitude error and change of the temperature, and the network is trained for steady and non-steady state, in which the identification capability is less than 0. 0003°/h. The experiments show that this method can estimate the axial RLG drift efficaciously, and the result of the navigation is excellent and can meet the practical demand.
Schizophrenia classification using functional network features
Rish, Irina; Cecchi, Guillermo A.; Heuton, Kyle
2012-03-01
This paper focuses on discovering statistical biomarkers (features) that are predictive of schizophrenia, with a particular focus on topological properties of fMRI functional networks. We consider several network properties, such as node (voxel) strength, clustering coefficients, local efficiency, as well as just a subset of pairwise correlations. While all types of features demonstrate highly significant statistical differences in several brain areas, and close to 80% classification accuracy, the most remarkable results of 93% accuracy are achieved by using a small subset of only a dozen of most-informative (lowest p-value) correlation features. Our results suggest that voxel-level correlations and functional network features derived from them are highly informative about schizophrenia and can be used as statistical biomarkers for the disease.
Distributed Function Calculation over Noisy Networks
Directory of Open Access Journals (Sweden)
Zhidun Zeng
2016-01-01
Full Text Available Considering any connected network with unknown initial states for all nodes, the nearest-neighbor rule is utilized for each node to update its own state at every discrete-time step. Distributed function calculation problem is defined for one node to compute some function of the initial values of all the nodes based on its own observations. In this paper, taking into account uncertainties in the network and observations, an algorithm is proposed to compute and explicitly characterize the value of the function in question when the number of successive observations is large enough. While the number of successive observations is not large enough, we provide an approach to obtain the tightest possible bounds on such function by using linear programing optimization techniques. Simulations are provided to demonstrate the theoretical results.
Free vibrations and buckling analysis of laminated plates by oscillatory radial basis functions
Neves, A. M. A.; Ferreira, A. J. M.
2015-12-01
In this paper the free vibrations and buckling analysis of laminated plates is performed using a global meshless method. A refined version of Kant's theorie which accounts for transverse normal stress and through-the-thickness deformation is used. The innovation is the use of oscillatory radial basis functions. Numerical examples are performed and results are presented and compared to available references. Such functions proved to be an alternative to the tradicional nonoscillatory radial basis functions.
Computational Exploration of the Biological Basis of Black-Scholes Expected Utility Function
Sukanto Bhattacharya; Kuldeep Kumar
2007-01-01
It has often been argued that there exists an underlying biological basis of utility functions. Taking this line of argument a step further in this paper, we have aimed to computationally demonstrate the biological basis of the Black-Scholes functional form as applied to classical option pricing and hedging theory. The evolutionary optimality of the classical Black-Scholes function has been computationally established by means of a haploid genetic algorithm model. The objective was to minimiz...
Computational Exploration of the Biological Basis of Black-Scholes Expected Utility Function
Kuldeep Kumar; Sukanto Bhattacharya
2007-01-01
It has often been argued that there exists an underlying biological basis of utility functions. Taking this line of argument a step further in this paper, we have aimed to computationally demonstrate the biological basis of the Black-Scholes functional form as applied to classical option pricing and hedging theory. The evolutionary optimality of the classical Black-Scholes function has been computationally established by means of a haploid genetic algorithm model. The objective was to mi...
Deep networks for motor control functions
Directory of Open Access Journals (Sweden)
Max eBerniker
2015-03-01
Full Text Available The motor system generates time-varying commands to move our limbs and body. Conventional descriptions of motor control and learning rely on dynamical representations of our body’s state (forward and inverse models, and control policies that must be integrated forward to generate feedforward time-varying commands; thus these are representations across space, but not time. Here we examine a new approach that directly represents both time-varying commands and the resulting state trajectories with a function; a representation across space and time. Since the output of this function includes time, it necessarily requires more parameters than a typical dynamical model. To avoid the problems of local minima these extra parameters introduce, we exploit recent advances in machine learning to build our function using a stacked autoencoder, or deep network. With initial and target states as inputs, this deep network can be trained to output an accurate temporal profile of the optimal command and state trajectory for a point-to-point reach of a nonlinear limb model, even when influenced by varying force fields. In a manner that mirrors motor babble, the network can also teach itself to learn through trial and error. Lastly, we demonstrate how this network can learn to optimize a cost objective. This functional approach to motor control is a sharp departure from the standard dynamical approach, and may offer new insights into the neural implementation of motor control.
Deep networks for motor control functions.
Berniker, Max; Kording, Konrad P
2015-01-01
The motor system generates time-varying commands to move our limbs and body. Conventional descriptions of motor control and learning rely on dynamical representations of our body's state (forward and inverse models), and control policies that must be integrated forward to generate feedforward time-varying commands; thus these are representations across space, but not time. Here we examine a new approach that directly represents both time-varying commands and the resulting state trajectories with a function; a representation across space and time. Since the output of this function includes time, it necessarily requires more parameters than a typical dynamical model. To avoid the problems of local minima these extra parameters introduce, we exploit recent advances in machine learning to build our function using a stacked autoencoder, or deep network. With initial and target states as inputs, this deep network can be trained to output an accurate temporal profile of the optimal command and state trajectory for a point-to-point reach of a non-linear limb model, even when influenced by varying force fields. In a manner that mirrors motor babble, the network can also teach itself to learn through trial and error. Lastly, we demonstrate how this network can learn to optimize a cost objective. This functional approach to motor control is a sharp departure from the standard dynamical approach, and may offer new insights into the neural implementation of motor control. PMID:25852530
Design of chemical space networks on the basis of Tversky similarity.
Wu, Mengjun; Vogt, Martin; Maggiora, Gerald M; Bajorath, Jürgen
2016-01-01
Chemical space networks (CSNs) have been introduced as a coordinate-free representation of chemical space. In CSNs, nodes represent compounds and edges pairwise similarity relationships. These network representations are mostly used to navigate sections of biologically relevant chemical space. Different types of CSNs have been designed on the basis of alternative similarity measures including continuous numerical similarity values or substructure-based similarity criteria. CSNs can be characterized and compared on the basis of statistical concepts from network science. Herein, a new CSN design is introduced that is based upon asymmetric similarity assessment using the Tversky coefficient and termed TV-CSN. Compared to other CSNs, TV-CSNs have unique features. While CSNs typically contain separate compound communities and exhibit small world character, many TV-CSNs are also scale-free in nature and contain hubs, i.e., extensively connected central compounds. Compared to other CSNs, these hubs are a characteristic of TV-CSN topology. Hub-containing compound communities are of particular interest for the exploration of structure-activity relationships.
Chain functions and scoring functions in genetic networks.
Gat-Viks, I; Shamir, R
2003-01-01
One of the grand challenges of system biology is to reconstruct the network of regulatory control among genes and proteins. High throughput data, particularly from expression experiments, may gradually make this possible in the future. Here we address two key ingredients in any such 'reverse engineering' effort: The choice of a biologically relevant, yet restricted, set of potential regulation functions, and the appropriate score to evaluate candidate regulatory relations. We propose a set of regulation functions which we call chain functions, and argue for their ubiquity in biological networks. We analyze their complexity and show that their number is exponentially smaller than all boolean functions of the same dimension. We define two new scores: one evaluating the fitness of a candidate set of regulators of a particular gene, and the other evaluating a candidate function. Both scores use established statistical methods. Finally, we test our methods on experimental gene expression data from the yeast galactose pathway. We show the utility of using chain functions and the improved inference using our scores in comparison to several extant scores. We demonstrate that the combined use of the two scores gives an extra advantage. We expect both chain functions and the new scores to be helpful in future attempts to infer regulatory networks. PMID:12855446
Functional optimization of the arterial network
Mauroy, Benjamin
2014-01-01
We build an evolutionary scenario that explains how some crucial physiological constraints in the arterial network of mammals - i.e. hematocrit, vessels diameters and arterial pressure drops - could have been selected by evolution. We propose that the arterial network evolved while being constrained by its function as an organ. To support this hypothesis, we focus our study on one of the main function of blood network: oxygen supply to the organs. We consider an idealized organ with a given oxygen need and we optimize blood network geometry and hematocrit with the constraint that it must fulfill the organ oxygen need. Our model accounts for the non-Newtonian behavior of blood, its maintenance cost and F\\aa hr\\ae us effects (decrease in average concentration of red blood cells as the vessel diameters decrease). We show that the mean shear rates (relative velocities of fluid layers) in the tree vessels follow a scaling law related to the multi-scale property of the tree network, and we show that this scaling la...
Neural network model for the efficient calculation of Green's functions in layered media
Soliman, E A; El-Gamal, M A; 10.1002/mmce.10066
2003-01-01
In this paper, neural networks are employed for fast and efficient calculation of Green's functions in a layered medium. Radial basis function networks (RBFNs) are effectively trained to estimate the coefficients and the exponents that represent a Green's function in the discrete complex image method (DCIM). Results show very good agreement with the DCIM, and the trained RBFNs are very fast compared with the corresponding DCIM. (23 refs).
Institute of Scientific and Technical Information of China (English)
张超; 彭道黎
2012-01-01
Aiming at the problem of multicollinearity and low precision predictions by the regression prediction model of carbon storage, this study used forest resource inventory data and SPOT5 image to retrieve the aboveground forest carbon storage of Populus forests in Yanqing County. Firstly, 10 factors were analyzed by principal components analysis. Then this paper introduced a method based on PCA and radial basis function （RBF） neural network for predicting forest carbon storage. The research results show that forest resource inventory data combined SPOT5 image is very useful for retrieving study of carbon storage of Populus forests~ the fitting precision of the PCA-RBF neural network model was 99.90% ,and the average prediction reached 96.71%. The model has a good retrieval accuracy, which can be well used for retrieval of regional aboveground forest carbon storage.%针对碳储量回归预测模型存在共线性和精度较低的问题,利用森林资源二类调查数据和SPOT5影像数据对北京市延庆县的杨树林进行碳储量反演研究。先对选取的10个指标进行主成分分析,在此基础上采用径向基函数（RBF）神经网络方法构建碳储量反演模型,用预留测试样本验证,并与实测值进行比较。研究结果表明：SPOT5数据和二类数据可以很好地结合起来用于森林地上碳储量反演研究;PCA-RBF神经网络森林碳储量遥感反演模型拟合精度为99.90%,平均预测精度达到96.71%,预估效果较理想;模型训练完成后,可以应用于延庆县森林地上碳储量反演。
Multiscale finite element methods for high-contrast problems using local spectral basis functions
Efendiev, Yalchin
2011-02-01
In this paper we study multiscale finite element methods (MsFEMs) using spectral multiscale basis functions that are designed for high-contrast problems. Multiscale basis functions are constructed using eigenvectors of a carefully selected local spectral problem. This local spectral problem strongly depends on the choice of initial partition of unity functions. The resulting space enriches the initial multiscale space using eigenvectors of local spectral problem. The eigenvectors corresponding to small, asymptotically vanishing, eigenvalues detect important features of the solutions that are not captured by initial multiscale basis functions. Multiscale basis functions are constructed such that they span these eigenfunctions that correspond to small, asymptotically vanishing, eigenvalues. We present a convergence study that shows that the convergence rate (in energy norm) is proportional to (H/Λ*)1/2, where Λ* is proportional to the minimum of the eigenvalues that the corresponding eigenvectors are not included in the coarse space. Thus, we would like to reach to a larger eigenvalue with a smaller coarse space. This is accomplished with a careful choice of initial multiscale basis functions and the setup of the eigenvalue problems. Numerical results are presented to back-up our theoretical results and to show higher accuracy of MsFEMs with spectral multiscale basis functions. We also present a hierarchical construction of the eigenvectors that provides CPU savings. © 2010.
Gaussian continuum basis functions for calculating high-harmonic generation spectra
Coccia, Emanuele; Labeye, Marie; Caillat, Jérémie; Taieb, Richard; Toulouse, Julien; Luppi, Eleonora
2016-01-01
We explore the computation of high-harmonic generation spectra by means of Gaussian basis sets in approaches propagating the time-dependent Schr{\\"o}dinger equation. We investigate the efficiency of Gaussian functions specifically designed for the description of the continuum proposed by Kaufmann et al. [ J. Phys. B 22 , 2223 (1989) ]. We assess the range of applicability of this approach by studying the hydrogen atom , i. e. the simplest atom for which "exact" calculations on a grid can be performed. We notably study the effect of increasing the basis set cardinal number , the number of diffuse basis functions , and the number of Gaussian pseudo-continuum basis functions for various laser parameters. Our results show that the latter significantly improve the description of the low-lying continuum states , and provide a satisfactory agreement with grid calculations for laser wavelengths $\\lambda$0 = 800 and 1064 nm. The Kaufmann continuum functions therefore appear as a promising way of constructing Gaussian ...
Fire Risk Assessment of Some Indian Coals Using Radial Basis Function (RBF) Technique
Nimaje, Devidas; Tripathy, Debi Prasad
2016-03-01
Fires, whether surface or underground, pose serious and environmental problems in the global coal mining industry. It is causing huge loss of coal due to burning and loss of lives, sterilization of coal reserves and environmental pollution. Most of the instances of coal mine fires happening worldwide are mainly due to the spontaneous combustion. Hence, attention must be paid to take appropriate measures to prevent occurrence and spread of fire. In this paper, to evaluate the different properties of coals for fire risk assessment, forty-nine in situ coal samples were collected from major coalfields of India. Intrinsic properties viz. proximate and ultimate analysis; and susceptibility indices like crossing point temperature, flammability temperature, Olpinski index and wet oxidation potential method of Indian coals were carried out to ascertain the liability of coal to spontaneous combustion. Statistical regression analysis showed that the parameters of ultimate analysis provide significant correlation with all investigated susceptibility indices as compared to the parameters of proximate analysis. Best correlated parameters (ultimate analysis) were used as inputs to the radial basis function network model. The model revealed that Olpinski index can be used as a reliable method to assess the liability of Indian coals to spontaneous combustion.
Off-Line Hand Written Character Recognition Using Radial Basis Function
Directory of Open Access Journals (Sweden)
J.ASHOK
2011-01-01
Full Text Available Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a personal Digital Assistant (PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields, in forms etc. To solve the problem of writer identification with intermediate classes (writers and objects (characters , it is a good way to extract the features with clear physical meanings. The extracted features are in variant under translation scaling and stroke width.The off-line (which pertains to scanned images is considered. Algorithms of preprocessing, character and word recognition, and performance with practical system are indicated. The recognition rate of Radial Basis Function (RBF is found to be better compared to that of Back Propagation Network (BPN. The recognition rate in the proposed system lies between 90% to 100%.
Network Coding Capacity Regions via Entropy Functions
Chan, Terence H
2012-01-01
In this paper, we use entropy functions to characterise the set of rate-capacity tuples achievable with either zero decoding error, or vanishing decoding error, for general network coding problems. We show that when sources are colocated, the outer bound obtained by Yeung, A First Course in Information Theory, Section 15.5 (2002) is tight and the sets of zero-error achievable and vanishing-error achievable rate-capacity tuples are the same. We also characterise the set of zero-error and vanishing-error achievable rate capacity tuples for network coding problems subject to linear encoding constraints, routing constraints (where some or all nodes can only perform routing) and secrecy constraints. Finally, we show that even for apparently simple networks, design of optimal codes may be difficult. In particular, we prove that for the incremental multicast problem and for the single-source secure network coding problem, characterisation of the achievable set is very hard and linear network codes may not be optimal...
Arithmetic functions in torus and tree networks
Bhanot, Gyan; Blumrich, Matthias A.; Chen, Dong; Gara, Alan G.; Giampapa, Mark E.; Heidelberger, Philip; Steinmacher-Burow, Burkhard D.; Vranas, Pavlos M.
2007-12-25
Methods and systems for performing arithmetic functions. In accordance with a first aspect of the invention, methods and apparatus are provided, working in conjunction of software algorithms and hardware implementation of class network routing, to achieve a very significant reduction in the time required for global arithmetic operation on the torus. Therefore, it leads to greater scalability of applications running on large parallel machines. The invention involves three steps in improving the efficiency and accuracy of global operations: (1) Ensuring, when necessary, that all the nodes do the global operation on the data in the same order and so obtain a unique answer, independent of roundoff error; (2) Using the topology of the torus to minimize the number of hops and the bidirectional capabilities of the network to reduce the number of time steps in the data transfer operation to an absolute minimum; and (3) Using class function routing to reduce latency in the data transfer. With the method of this invention, every single element is injected into the network only once and it will be stored and forwarded without any further software overhead. In accordance with a second aspect of the invention, methods and systems are provided to efficiently implement global arithmetic operations on a network that supports the global combining operations. The latency of doing such global operations are greatly reduced by using these methods.
Function Analyses of Geographic Information System on Rural Distribution Network
Institute of Scientific and Technical Information of China (English)
FANG Junlong; FAN Yongcun; ZHANG Chunmei; GU Shumin
2006-01-01
With the actuality and characteristic and requirement of rural power enterprise distribution network management, this article introduced the function of geographic information system on the framework of distribution network, in order to develop rural distribution network.
International Nuclear Information System (INIS)
Full text: We show that a suitable set of coherent basis states placed on a discrete hexagonal grid can be used to numerically very accurately represent general quantum states in a memory efficient way. Adding an algorithm for dynamic basis adaptation allows highly accurate Quantum Monte Carlo wave function simulations with small basis sets. At the example of the intricate nonlinear dynamics of an optical parametric oscillator around threshold, we demonstrate that this approach yields accurate time dependent solutions with a substantially smaller basis sets than required for a photon number basis. Above threshold the adaptive basis splits into localized subsets allowing efficient representation of bimodal or even more complex phase space distributions and directly yields an intuitive physical picture of the ongoing dynamics. (author)
Combustion monitoring of a water tube boiler using a discriminant radial basis network.
Sujatha, K; Pappa, N
2011-01-01
This research work includes a combination of Fisher's linear discriminant (FLD) analysis and a radial basis network (RBN) for monitoring the combustion conditions for a coal fired boiler so as to allow control of the air/fuel ratio. For this, two-dimensional flame images are required, which were captured with a CCD camera; the features of the images-average intensity, area, brightness and orientation etc of the flame-are extracted after preprocessing the images. The FLD is applied to reduce the n-dimensional feature size to a two-dimensional feature size for faster learning of the RBN. Also, three classes of images corresponding to different burning conditions of the flames have been extracted from continuous video processing. In this, the corresponding temperatures, and the carbon monoxide (CO) emissions and those of other flue gases have been obtained through measurement. Further, the training and testing of Fisher's linear discriminant radial basis network (FLDRBN), with the data collected, have been carried out and the performance of the algorithms is presented.
Hong, X; Harris, C J
2000-01-01
This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bézier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bézier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bézier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bézier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.
Haghdani, Shokouh; Åstrand, Per-Olof; Koch, Henrik
2016-02-01
We have calculated the electronic optical rotation of seven molecules using coupled cluster singles-doubles (CCSD) and the second-order approximation (CC2) employing the aug-cc-pVXZ (X = D, T, or Q) basis sets. We have also compared to time-dependent density functional theory (TDDFT) by utilizing two functionals B3LYP and CAM-B3LYP and the same basis sets. Using relative and absolute error schemes, our calculations demonstrate that the CAM-B3LYP functional predicts optical rotation with the minimum deviations compared to CCSD at λ = 355 and 589.3 nm. Furthermore, our results illustrate that the aug-cc-pVDZ basis set provides the optical rotation in good agreement with the larger basis sets for molecules not possessing small-angle optical rotation at λ = 589.3 nm. We have also performed several two-point inverse power extrapolations for the basis set convergence, i.e., OR(∞) + AX(-n), using the CC2 model at λ = 355 and 589.3 nm. Our results reveal that a two-point inverse power extrapolation with the aug-cc-pVTZ and aug-cc-pVQZ basis sets at n = 5 provides optical rotation deviations similar to those of aug-cc-pV5Z with respect to the basis limit.
On Functional Module Detection in Metabolic Networks
Directory of Open Access Journals (Sweden)
Ina Koch
2013-08-01
Full Text Available Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models.
On functional module detection in metabolic networks.
Koch, Ina; Ackermann, Jörg
2013-01-01
Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes) and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum) as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models. PMID:24958145
MESHLESS METHOD BASED ON COLLOCATION WITH CONSISTENT COMPACTLY SUPPORTED RADIAL BASIS FUNCTIONS
Institute of Scientific and Technical Information of China (English)
SONG Kangzu; ZHANG Xiong; LU Mingwan
2004-01-01
Based on our previous study, the accuracy of derivatives of interpolating functions are usually very poor near the boundary of domain when Compactly Supported Radial Basis Functions (CSRBFs) are used, so that it could result in significant error in solving partial differential equations with Neumann boundary conditions. To overcome this drawback, the Consistent Compactly Supported Radial Basis Functions (CCSRBFs) are developed, which satisfy the predetermined consistency conditions. Meshless method based on point collocation with CCSRBFs is developed for solving partial differential equations. Numerical studies show that the proposed method improves the accuracy of approximation significantly.
Nonlinear System Identification via Basis Functions Based Time Domain Volterra Model
Directory of Open Access Journals (Sweden)
Yazid Edwar
2014-07-01
Full Text Available This paper proposes basis functions based time domain Volterra model for nonlinear system identification. The Volterra kernels are expanded by using complex exponential basis functions and estimated via genetic algorithm (GA. The accuracy and practicability of the proposed method are then assessed experimentally from a scaled 1:100 model of a prototype truss spar platform. Identification results in time and frequency domain are presented and coherent functions are performed to check the quality of the identification results. It is shown that results between experimental data and proposed method are in good agreement.
DEFF Research Database (Denmark)
Kim, Oleksiy S.; Meincke, Peter; Breinbjerg, Olav;
2007-01-01
applied to transform the VSIE into a system of linear equations. The higher-order MoM provides significant reduction in the number of unknowns in comparison with standard MoM formulations using low-order basis functions, such as RWG functions. Due to the orthogonal nature of the higher-order Legendre......The problem of electromagnetic scattering by composite metallic and dielectric objects is solved using the coupled volume-surface integral equation (VSIE). The method of moments (MoM) based on higher-order hierarchical Legendre basis functions and higher-order curvilinear geometrical elements is...
Complex network perspective on structure and function of Staphylococcus aureus metabolic network
Indian Academy of Sciences (India)
L Ying; D W Ding
2013-02-01
With remarkable advances in reconstruction of genome-scale metabolic networks, uncovering complex network structure and function from these networks is becoming one of the most important topics in system biology. This work aims at studying the structure and function of Staphylococcus aureus (S. aureus) metabolic network by complex network methods. We first generated a metabolite graph from the recently reconstructed high-quality S. aureus metabolic network model. Then, based on `bow tie' structure character, we explain and discuss the global structure of S. aureus metabolic network. The functional significance, global structural properties, modularity and centrality analysis of giant strong component in S. aureus metabolic networks are studied.
International Nuclear Information System (INIS)
Although phase-space localized Gaussians are themselves poor basis functions, they can be used to effectively contract a discrete variable representation basis [A. Shimshovitz and D. J. Tannor, Phys. Rev. Lett. 109, 070402 (2012)]. This works despite the fact that elements of the Hamiltonian and overlap matrices labelled by discarded Gaussians are not small. By formulating the matrix problem as a regular (i.e., not a generalized) matrix eigenvalue problem, we show that it is possible to use an iterative eigensolver to compute vibrational energy levels in the Gaussian basis
Institute of Scientific and Technical Information of China (English)
叶林; 陈政; 赵永宁; 朱倩雯
2015-01-01
To deal with the problem of fluctuating photovoltaic power,a photovoltaic power forecasting model based on genetic algorithm (GA) and fuzzy radial basis function (RBF) neural network is proposed and the output power is applied to the battery energy storage system to mitigate the electric shock on the power system.A historical day of identical weather type,a close date and minimum temperature Euclidean distance is chosen as the similar day.The solar radiation intensity and temperature closely correlated with photovoltaic (PV) power are chosen as input variables of the model,a GA and fuzzy RBF neural network is built as the final prediction model based on the parameter optimization method of K-means clustering and genetic algorithm.Furthermore,a smooth control strategy considering PV power forecasting is used to control the grid-connected PV power,so as to smooth the PV power fluctuation.The experimental results show that the proposed forecasting model has high accuracy and the smooth control strategy for power fluctuation based on photovoltaic power forecasting is effective.%针对光伏发电系统出力波动问题，提出遗传算法(GA)—模糊径向基(RBF)神经网络的光伏发电功率预测模型，将功率预测值应用于光伏发电的蓄电池储能功率调节系统，以降低对电网的冲击。选择与待预测日天气类型相同、日期相近、温度欧氏距离最小的历史日作为相似日，把与光伏发电功率相关性大的太阳辐射强度和温度作为模型输入变量，提出 K 均值聚类和遗传算法的参数优化方法，建立基于 GA—模糊 RBF 神经网络的最终预测模型。在光伏功率预测的基础上，提出一种平滑控制策略，对光伏并网功率进行有效调节，从而达到平滑光伏功率波动的目的。实例证明，所述预测模型具有较高精度，并验证了平滑功率波动控制策略的有效性。
Unconditioned responses and functional fear networks in human classical conditioning.
Linnman, Clas; Rougemont-Bücking, Ansgar; Beucke, Jan Carl; Zeffiro, Thomas A; Milad, Mohammed R
2011-08-01
Human imaging studies examining fear conditioning have mainly focused on the neural responses to conditioned cues. In contrast, the neural basis of the unconditioned response and the mechanisms by which fear modulates inter-regional functional coupling have received limited attention. We examined the neural responses to an unconditioned stimulus using a partial-reinforcement fear conditioning paradigm and functional MRI. The analysis focused on: (1) the effects of an unconditioned stimulus (an electric shock) that was either expected and actually delivered, or expected but not delivered, and (2) on how related brain activity changed across conditioning trials, and (3) how shock expectation influenced inter-regional coupling within the fear network. We found that: (1) the delivery of the shock engaged the red nucleus, amygdale, dorsal striatum, insula, somatosensory and cingulate cortices, (2) when the shock was expected but not delivered, only the red nucleus, the anterior insular and dorsal anterior cingulate cortices showed activity increases that were sustained across trials, and (3) psycho-physiological interaction analysis demonstrated that fear led to increased red nucleus coupling to insula but decreased hippocampus coupling to the red nucleus, thalamus and cerebellum. The hippocampus and the anterior insula may serve as hubs facilitating the switch between engagement of a defensive immediate fear network and a resting network. PMID:21377494
Graph theoretical analysis of resting magnetoencephalographic functional connectivity networks
Directory of Open Access Journals (Sweden)
Lindsay eRutter
2013-07-01
Full Text Available Complex networks have been observed to comprise small-world properties, believed to represent an optimal organization of local specialization and global integration of information processing at reduced wiring cost. Here, we applied magnitude squared coherence to resting magnetoencephalographic time series in reconstructed source space, acquired from controls and patients with schizophrenia, and generated frequency-dependent adjacency matrices modeling functional connectivity between virtual channels. After configuring undirected binary and weighted graphs, we found that all human networks demonstrated highly localized clustering and short characteristic path lengths. The most conservatively thresholded networks showed efficient wiring, with topographical distance between connected vertices amounting to one-third as observed in surrogate randomized topologies. Nodal degrees of the human networks conformed to a heavy-tailed exponentially truncated power-law, compatible with the existence of hubs, which included theta and alpha bilateral cerebellar tonsil, beta and gamma bilateral posterior cingulate, and bilateral thalamus across all frequencies. We conclude that all networks showed small-worldness, minimal physical connection distance, and skewed degree distributions characteristic of physically-embedded networks, and that these calculations derived from graph theoretical mathematics did not quantifiably distinguish between subject populations, independent of bandwidth. However, post-hoc measurements of edge computations at the scale of the individual vertex revealed trends of reduced gamma connectivity across the posterior medial parietal cortex in patients, an observation consistent with our prior resting activation study that found significant reduction of synthetic aperture magnetometry gamma power across similar regions. The basis of these small differences remains unclear.
Allocating resources between network nodes for providing a network node function
Strijkers, R.J.; Meulenhoff, P.J.
2014-01-01
The invention provides a method wherein a first network node advertises available resources that a second network node may use to offload network node functions transparently to the first network node. Examples of the first network node are a client device (e.g. PC, notebook, tablet, smart phone), a
Automatic target tracking in forward-looking infrared video sequences using tuned basis functions
Bal, Abdullah; Alam, Mohammad S.
2016-07-01
Tuned basis function (TBF) is a powerful technique for classification of two classes by transforming them into a new space, where both classes will have complementary eigenvectors. A target discrimination technique can be described based on these complementary eigenvector analyses under two classes: (1) target and (2) background clutter, where basis functions that best represent the desired targets form one class while the complementary basis functions form the second class. Since the TBF does not require pixel-based preprocessing, it provides significant advantages for target tracking applications. Furthermore, efficient eigenvector selection and subframe segmentation significantly reduce the computation burden of the target tracking algorithm. The performance of the proposed TBF-based target tracking algorithm has been tested using real-world forward looking infrared video sequences.
Symmetric multivariate polynomials as a basis for three-boson light-front wave functions.
Chabysheva, Sophia S; Elliott, Blair; Hiller, John R
2013-12-01
We develop a polynomial basis to be used in numerical calculations of light-front Fock-space wave functions. Such wave functions typically depend on longitudinal momentum fractions that sum to unity. For three particles, this constraint limits the two remaining independent momentum fractions to a triangle, for which the three momentum fractions act as barycentric coordinates. For three identical bosons, the wave function must be symmetric with respect to all three momentum fractions. Therefore, as a basis, we construct polynomials in two variables on a triangle that are symmetric with respect to the interchange of any two barycentric coordinates. We find that, through the fifth order, the polynomial is unique at each order, and, in general, these polynomials can be constructed from products of powers of the second- and third-order polynomials. The use of such a basis is illustrated in a calculation of a light-front wave function in two-dimensional ϕ(4) theory; the polynomial basis performs much better than the plane-wave basis used in discrete light-cone quantization.
Institute of Scientific and Technical Information of China (English)
LI An-yong
2004-01-01
A new method based on angular momentum theory was proposed to construct the basis functions of the irreducible representations(IRs) of point groups. The transformation coefficients, i. e. , coefficients S, are the components of the eigenvectors of some Hermitian matrices, and can be made as real numbers for all pure rotation point groups. The general formula for coefficient S was deduced, and applied to constructing the basis functions of single-valued irreducible representations of icosahedral group from the spherical harmonics with angular momentum j≤7.
Plant geography upon the basis of functional traits: an example from eastern North American trees.
Swenson, Nathan G; Weiser, Michael D
2010-08-01
Plant geographers have sought for decades to describe and predict the geographic distribution of vegetation types on the basis of plant function and its relationship with the abiotic environment. Traditionally this has been accomplished using categorical representations such as plant functional types. Increasingly, plant functional ecologists have sought to refine categorical functional types via quantitative functional traits in order to understand the ecological implications of trade-offs in plant form and function. Fewer works have focused upon testing whether commonly measured functional traits enhance our understanding of plant biogeography broadly and the geographic distribution of vegetation types in particular. Here we combine a continental-scale forest inventory data set containing 18 111 plots with a plant functional trait data set to ask: (1) Is there a strong relationship between the abiotic environment and the distribution of functional trait values in forest inventory plots? And (2) can different Holdridge life zones be distinguished upon the basis of their functional trait distributions? The results show geographic patterns of functional trait distributions that are often strongly correlated with climate and also show that the Holdridge life zones in the study area can be differentiated using a combination of functional traits. PMID:20836445
Deterministic Function Computation with Chemical Reaction Networks
Chen, Ho-Lin; Soloveichik, David
2012-01-01
We study the deterministic computation of functions on tuples of natural numbers by chemical reaction networks (CRNs). CRNs have been shown to be efficiently Turing-universal when allowing for a small probability of error. CRNs that are guaranteed to converge on a correct answer, on the other hand, have been shown to decide only the semilinear predicates. We introduce the notion of function, rather than predicate, computation by representing the output of a function f:N^k --> N^l by a count of some molecular species, i.e., if the CRN starts with n_1,...,n_k molecules of some "input" species X_1,...,X_k, the CRN is guaranteed to converge to having f(n_1,...,n_k) molecules of the "output" species Y_1,...,Y_l. We show that a function f:N^k --> N^l is deterministically computed by a CRN if and only if its graph {(x,y) \\in N^k x N^l | f(x) = y} is a semilinear set. Finally, we show that each semilinear function f can be computed on input x in expected time O(polylog |x|).
Vestibular and Attractor Network Basis of the Head Direction Cell Signal in Subcortical Circuits
Directory of Open Access Journals (Sweden)
Benjamin J Clark
2012-03-01
Full Text Available Accurate navigation depends on a network of neural systems that encode the moment-to-moment changes in an animal’s directional orientation and location in space. Within this navigation system are head direction (HD cells, which fire persistently when an animal’s head is pointed in a particular direction (Sharp et al., 2001a; Taube, 2007. HD cells are widely thought to underlie an animal’s sense of spatial orientation, and research over the last 25+ years has revealed that this robust spatial signal is widely distributed across subcortical and cortical limbic areas. Much of this work has been directed at understanding the functional organization of the HD cell circuitry, and precisely how this signal is generated from sensory and motor systems. The purpose of the present review is to summarize some of the recent studies arguing that the HD cell circuit is largely processed in a hierarchical fashion, following a pathway involving the dorsal tegmental nuclei → lateral mammillary nuclei → anterior thalamus → parahippocampal and retrosplenial cortical regions. We also review recent work identifying bursting cellular activity in the HD cell circuit after lesions of the vestibular system, and relate these observations to the long held view that attractor network mechanisms underlie HD signal generation. Finally, we summarize the work to date suggesting that this network architecture may reside within the tegmento-mammillary circuit.
Barca, Giuseppe M J; Gill, Peter M W
2016-01-01
Explicitly-correlated F12 methods are becoming the first choice for high-accuracy molecular orbital calculations, and can often achieve chemical accuracy with relatively small gaussian basis sets. In most calculations, the many three- and four-electron integrals that formally appear in the theory are avoided through judicious use of resolutions of the identity (RI). However, in order not to jeopardize the intrinsic accuracy of the F12 wave function, the associated RI auxiliary basis set must be large. Here, inspired by the Head-Gordon-Pople (HGP) and PRISM algorithms for two-electron integrals, we present an algorithm to compute directly three-electron integrals over gaussian basis functions and a very general class of three-electron operators, without invoking RI approximations. A general methodology to derive vertical, transfer and horizontal recurrence relations is also presented.
Institute of Scientific and Technical Information of China (English)
ZHENG Guangguo; ZHOU Dongsheng; WEI Xiaopeng; ZHANG Qiang
2012-01-01
Compactly supported radial basis function can enable the coefficient matrix of solving weigh linear system to have a sparse banded structure, thereby reducing the complexity of the algorithm. Firstly, based on the compactly supported radial basis function, the paper makes the complex quadratic function （Multiquadric, MQ for short） to be transformed and proposes a class of compactly supported MQ function. Secondly, the paper describes a method that interpolates discrete motion capture data to solve the motion vectors of the interpolation points and they are used in facial expression reconstruction. Finally, according to this characteris- tic of the uneven distribution of the face markers, the markers are numbered and grouped in accordance with the density level, and then be interpolated in line with each group. The approach not only ensures the accuracy of the deformation of face local area and smoothness, but also reduces the time complexity of computing.
Quantifying the connectivity of a network: The network correlation function method
Barzel, Baruch; 10.1103/PhysRevE.80.046104
2009-01-01
Networks are useful for describing systems of interacting objects, where the nodes represent the objects and the edges represent the interactions between them. The applications include chemical and metabolic systems, food webs as well as social networks. Lately, it was found that many of these networks display some common topological features, such as high clustering, small average path length (small world networks) and a power-law degree distribution (scale free networks). The topological features of a network are commonly related to the network's functionality. However, the topology alone does not account for the nature of the interactions in the network and their strength. Here we introduce a method for evaluating the correlations between pairs of nodes in the network. These correlations depend both on the topology and on the functionality of the network. A network with high connectivity displays strong correlations between its interacting nodes and thus features small-world functionality. We quantify the ...
A data-driven approach to local gravity field modelling using spherical radial basis functions
Klees, R.; Tenzer, R.; Prutkin, I.; Wittwer, T.
2008-01-01
We propose a methodology for local gravity field modelling from gravity data using spherical radial basis functions. The methodology comprises two steps: in step 1, gravity data (gravity anomalies and/or gravity disturbances) are used to estimate the disturbing potential using least-squares techniqu
Sparse Linear Parametric Modeling of Room Acoustics with Orthonormal Basis Functions
DEFF Research Database (Denmark)
Vairetti, G.; von Waterschoot, T.; Moonen, M.;
2014-01-01
Orthonormal Basis Function (OBF) models provide a stable and well-conditioned representation of a linear system. When used for the modeling of room acoustics, useful information about the true dynamics of the system can be introduced by a proper selection of a set of poles, which however appear non...
IMAGE COMPRESSION USING DISCRETE ORTHOGONAL TRANSFORMS WITH THE «NOISE-LIKE» BASIS FUNCTIONS
Chernov, V.; Dmitriyev, A.
1999-01-01
The generalization of the discrete orthogonal transforms with the basis functions generated in a pseudorandom way is the subject of the article. The examples of such transforms application in the field of videoinformation coding in the channels with the high level of «seldom» noise are also given.
High Performance 3D PET Reconstruction Using Spherical Basis Functions on a Polar Grid
Directory of Open Access Journals (Sweden)
J. Cabello
2012-01-01
Full Text Available Statistical iterative methods are a widely used method of image reconstruction in emission tomography. Traditionally, the image space is modelled as a combination of cubic voxels as a matter of simplicity. After reconstruction, images are routinely filtered to reduce statistical noise at the cost of spatial resolution degradation. An alternative to produce lower noise during reconstruction is to model the image space with spherical basis functions. These basis functions overlap in space producing a significantly large number of non-zero elements in the system response matrix (SRM to store, which additionally leads to long reconstruction times. These two problems are partly overcome by exploiting spherical symmetries, although computation time is still slower compared to non-overlapping basis functions. In this work, we have implemented the reconstruction algorithm using Graphical Processing Unit (GPU technology for speed and a precomputed Monte-Carlo-calculated SRM for accuracy. The reconstruction time achieved using spherical basis functions on a GPU was 4.3 times faster than the Central Processing Unit (CPU and 2.5 times faster than a CPU-multi-core parallel implementation using eight cores. Overwriting hazards are minimized by combining a random line of response ordering and constrained atomic writing. Small differences in image quality were observed between implementations.
International Nuclear Information System (INIS)
The US Department of Energy has selected the Savannah River Site (SRS) as the location to consolidate and store aluminum-based spent nuclear fuel (Al-SNF) from domestic and foreign research reactors. This report presents the technical basis for the functional performance requirements
MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions.
Novosad, Philip; Reader, Andrew J
2016-06-21
Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the reconstruction algorithm. In this work we have developed a highly constrained, fully dynamic PET reconstruction algorithm incorporating both spectral analysis temporal basis functions and spatial basis functions derived from the kernel method applied to a co-registered T1-weighted magnetic resonance (MR) image. The dynamic PET image is modelled as a linear combination of spatial and temporal basis functions, and a maximum likelihood estimate for the coefficients can be found using the expectation-maximization (EM) algorithm. Following reconstruction, kinetic fitting using any temporal model of interest can be applied. Based on a BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [(18)F]FDG simulation study with two noise levels, and investigated the quantitative performance of the proposed reconstruction algorithm, comparing it with reconstructions incorporating either spectral analysis temporal basis functions alone or kernel spatial basis functions alone, as well as with conventional frame-independent reconstruction. Compared to the other reconstruction algorithms, the proposed algorithm achieved superior performance, offering a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in the tumours when they were present on the co-registered MR image. When the tumours were not visible in the MR image, reconstruction with the proposed algorithm performed similarly to reconstruction with spectral temporal basis functions and was superior to both conventional frame-independent reconstruction and frame-independent reconstruction with kernel spatial basis functions. Furthermore, we demonstrate that a joint spectral
A mixed basis density functional approach for one-dimensional systems with B-splines
Ren, Chung-Yuan; Chang, Yia-Chung; Hsue, Chen-Shiung
2016-05-01
A mixed basis approach based on density functional theory is extended to one-dimensional (1D) systems. The basis functions here are taken to be the localized B-splines for the two finite non-periodic dimensions and the plane waves for the third periodic direction. This approach will significantly reduce the number of the basis and therefore is computationally efficient for the diagonalization of the Kohn-Sham Hamiltonian. For 1D systems, B-spline polynomials are particularly useful and efficient in two-dimensional spatial integrations involved in the calculations because of their absolute localization. Moreover, B-splines are not associated with atomic positions when the geometry structure is optimized, making the geometry optimization easy to implement. With such a basis set we can directly calculate the total energy of the isolated system instead of using the conventional supercell model with artificial vacuum regions among the replicas along the two non-periodic directions. The spurious Coulomb interaction between the charged defect and its repeated images by the supercell approach for charged systems can also be avoided. A rigorous formalism for the long-range Coulomb potential of both neutral and charged 1D systems under the mixed basis scheme will be derived. To test the present method, we apply it to study the infinite carbon-dimer chain, graphene nanoribbon, carbon nanotube and positively-charged carbon-dimer chain. The resulting electronic structures are presented and discussed in detail.
Institute of Scientific and Technical Information of China (English)
陈红梅; 刘兴高
2015-01-01
In view of the problems that not highlighted predominant data and variables affect the prediction accuracy and not enough data smoothness influences the generalization performance in the prediction of PolyPropylene ( PP ) Melt Index ( MI) , this paper proposed a forecasting model of MI based on Radial Basis Function Neural Network ( RBFNN) with weighting and smoothing of multi-technology integration. The proposed model integratedly applied two data weighting schemes: weighting based on space Euclidean distance in the time scale, weighting based on grey correlation and linear autoregression error in the variable dimension, and also applied two data smoothing methods: smoothing based upon the Euclidean distance of process variable differential sequence and partial linear smoothing, to solve the problems of low prediction precision and generalization ability. To further improve the forecasting capability of the model, based on the Nonlinear Autoregressive Moving Average ( NARMA) model framework with error compensation and the RBFNN, the paper used the self-tuning predictive control algorithm and the piecewise-linear alterable learning rate algorithm to optimize this model. The presented model is validated by the real data from a plant and the prediction results on the generalization database are as follows: Mean Relative Error ( MRE) is 1. 32%, Root Mean Square Error ( RMSE) is 0. 045 9. Compared and analysed detailedly with the report in the literature, the results show that the proposed model in the paper has an excellent forecasting accuracy and generalization ability, and has a certain application value in the industrial process with large time delay.%针对在对聚丙烯熔融指数进行预测时优势数据和优势变量不突出影响预测精度、数据平滑度不够影响泛化性能的问题，提出了基于多技术融合加权平滑的径向基函数神经网络预报模型。综合运用了在时间尺度基于空间欧氏距离加权、在变量
RCS Computation by Parallel MoM Using Higher-Order Basis Functions
Directory of Open Access Journals (Sweden)
Ying Yan
2012-01-01
Full Text Available A Message-Passing Interface (MPI parallel implementation of an integral equation solver that uses the Method of Moments (MoM with higher-order basis functions has been proposed to compute the Radar Cross-Section (RCS of various targets. The block-partitioned scheme for the large dense MoM matrix is designed to achieve excellent load balance and high parallel efficiency. Some numerical results demonstrate that higher-order basis in this parallelized scheme is more efficient than the conventional RWG method and able to efficiently analyze RCS of various electrically large platforms.
RCS Computation by Parallel MoM Using Higher-Order Basis Functions
Ying Yan; Yu Zhang; Chang-Hong Liang; Hui Zhao; D. García-Doñoro
2012-01-01
A Message-Passing Interface (MPI) parallel implementation of an integral equation solver that uses the Method of Moments (MoM) with higher-order basis functions has been proposed to compute the Radar Cross-Section (RCS) of various targets. The block-partitioned scheme for the large dense MoM matrix is designed to achieve excellent load balance and high parallel efficiency. Some numerical results demonstrate that higher-order basis in this parallelized scheme is more efficient than the convent...
Constructive feedforward neural networks using hermite polynomial activation functions.
Ma, Liying; Khorasani, K
2005-07-01
In this paper, a constructive one-hidden-layer network is introduced where each hidden unit employs a polynomial function for its activation function that is different from other units. Specifically, both a structure level as well as a function level adaptation methodologies are utilized in constructing the network. The functional level adaptation scheme ensures that the "growing" or constructive network has different activation functions for each neuron such that the network may be able to capture the underlying input-output map more effectively. The activation functions considered consist of orthonormal Hermite polynomials. It is shown through extensive simulations that the proposed network yields improved performance when compared to networks having identical sigmoidal activation functions.
An efficient ensemble of radial basis functions method based on quadratic programming
Shi, Renhe; Liu, Li; Long, Teng; Liu, Jian
2016-07-01
Radial basis function (RBF) surrogate models have been widely applied in engineering design optimization problems to approximate computationally expensive simulations. Ensemble of radial basis functions (ERBF) using the weighted sum of stand-alone RBFs improves the approximation performance. To achieve a good trade-off between the accuracy and efficiency of the modelling process, this article presents a novel efficient ERBF method to determine the weights through solving a quadratic programming subproblem, denoted ERBF-QP. Several numerical benchmark functions are utilized to test the performance of the proposed ERBF-QP method. The results show that ERBF-QP can significantly improve the modelling efficiency compared with several existing ERBF methods. Moreover, ERBF-QP also provides satisfactory performance in terms of approximation accuracy. Finally, the ERBF-QP method is applied to a satellite multidisciplinary design optimization problem to illustrate its practicality and effectiveness for real-world engineering applications.
Improving the Network Structure can lead to Functional Failures
Pade, Jan Philipp
2014-01-01
In many real-world networks the ability to synchronize is a key property for its performance. Examples include power-grid, sensor, and neuron networks as well as consensus formation. Recent work on undirected networks with diffusive interaction revealed that improvements in the network connectivity such as making the network more connected and homogeneous enhances synchronization. However, real-world networks have directed and weighted connections. In such directed networks, understanding the impact of structural changes on the network performance remains a major challenge. Here, we show that improving the structure of a directed network can lead to a failure in the network function. For instance, introducing new links to reduce the minimum distance between nodes can lead to instabilities in the synchronized motion. This counter-intuitive effect only occurs in directed networks. Our results allow to identify the dynamical importance of a link and thereby have a major impact on the design and control of direct...
Hoyer, Chad E; Gagliardi, Laura; Truhlar, Donald G
2015-11-01
Time-dependent Kohn-Sham density functional theory (TD-KS-DFT) is useful for calculating electronic excitation spectra of large systems, but the low-energy spectra are often complicated by artificially lowered higher-energy states. This affects even the lowest energy excited states. Here, by calculating the lowest energy spin-conserving excited state for atoms from H to K and for formaldehyde, we show that this problem does not occur in multiconfiguration pair-density functional theory (MC-PDFT). We use the tPBE on-top density functional, which is a translation of the PBE exchange-correlation functional. We compare to a robust multireference method, namely, complete active space second-order perturbation theory (CASPT2), and to TD-KS-DFT with two popular exchange-correlation functionals, PBE and PBE0. We find for atoms that the mean unsigned error (MUE) of MC-PDFT with the tPBE functional improves from 0.42 to 0.40 eV with a double set of diffuse functions, whereas the MUEs for PBE and PBE0 drastically increase from 0.74 to 2.49 eV and from 0.45 to 1.47 eV, respectively.
Identification of Resting State Networks Involved in Executive Function.
Connolly, Joanna; McNulty, Jonathan P; Boran, Lorraine; Roche, Richard A P; Delany, David; Bokde, Arun L W
2016-06-01
The structural networks in the human brain are consistent across subjects, and this is reflected also in that functional networks across subjects are relatively consistent. These findings are not only present during performance of a goal oriented task but there are also consistent functional networks during resting state. It suggests that goal oriented activation patterns may be a function of component networks identified using resting state. The current study examines the relationship between resting state networks measured and patterns of neural activation elicited during a Stroop task. The association between the Stroop-activated networks and the resting state networks was quantified using spatial linear regression. In addition, we investigated if the degree of spatial association of resting state networks with the Stroop task may predict performance on the Stroop task. The results of this investigation demonstrated that the Stroop activated network can be decomposed into a number of resting state networks, which were primarily associated with attention, executive function, visual perception, and the default mode network. The close spatial correspondence between the functional organization of the resting brain and task-evoked patterns supports the relevance of resting state networks in cognitive function. PMID:26935902
Advanced functional network analysis in the geosciences: The pyunicorn package
Donges, Jonathan F.; Heitzig, Jobst; Runge, Jakob; Schultz, Hanna C. H.; Wiedermann, Marc; Zech, Alraune; Feldhoff, Jan; Rheinwalt, Aljoscha; Kutza, Hannes; Radebach, Alexander; Marwan, Norbert; Kurths, Jürgen
2013-04-01
Functional networks are a powerful tool for analyzing large geoscientific datasets such as global fields of climate time series originating from observations or model simulations. pyunicorn (pythonic unified complex network and recurrence analysis toolbox) is an open-source, fully object-oriented and easily parallelizable package written in the language Python. It allows for constructing functional networks (aka climate networks) representing the structure of statistical interrelationships in large datasets and, subsequently, investigating this structure using advanced methods of complex network theory such as measures for networks of interacting networks, node-weighted statistics or network surrogates. Additionally, pyunicorn allows to study the complex dynamics of geoscientific systems as recorded by time series by means of recurrence networks and visibility graphs. The range of possible applications of the package is outlined drawing on several examples from climatology.
Functional alignment of regulatory networks: a study of temperate phages.
Directory of Open Access Journals (Sweden)
Ala Trusina
2005-12-01
Full Text Available The relationship between the design and functionality of molecular networks is now a key issue in biology. Comparison of regulatory networks performing similar tasks can provide insights into how network architecture is constrained by the functions it directs. Here, we discuss methods of network comparison based on network architecture and signaling logic. Introducing local and global signaling scores for the difference between two networks, we quantify similarities between evolutionarily closely and distantly related bacteriophages. Despite the large evolutionary separation between phage lambda and 186, their networks are found to be similar when difference is measured in terms of global signaling. We finally discuss how network alignment can be used to pinpoint protein similarities viewed from the network perspective.
Nonequilibrium functional bosonization of quantum wire networks
Energy Technology Data Exchange (ETDEWEB)
Ngo Dinh, Stephane, E-mail: stephane.ngodinh@kit.edu [Institut fuer Theorie der Kondensierten Materie, Karlsruhe Institute of Technology, 76128 Karlsruhe (Germany); DFG Center for Functional Nanostructures, Karlsruhe Institute of Technology, 76128 Karlsruhe (Germany); Bagrets, Dmitry A. [Institut fuer Theoretische Physik, Universitaet zu Koeln, Zuelpicher Str. 77, 50937 Koeln (Germany); Mirlin, Alexander D. [Institut fuer Theorie der Kondensierten Materie, Karlsruhe Institute of Technology, 76128 Karlsruhe (Germany); Institut fuer Nanotechnologie, Karlsruhe Institute of Technology, 76021 Karlsruhe (Germany); DFG Center for Functional Nanostructures, Karlsruhe Institute of Technology, 76128 Karlsruhe (Germany); Petersburg Nuclear Physics Institute, 188300 St. Petersburg (Russian Federation)
2012-11-15
We develop a general approach to nonequilibrium nanostructures formed by one-dimensional channels coupled by tunnel junctions and/or by impurity scattering. The formalism is based on nonequilibrium version of functional bosonization. A central role in this approach is played by the Keldysh action that has a form reminiscent of the theory of full counting statistics. To proceed with evaluation of physical observables, we assume the weak-tunneling regime and develop a real-time instanton method. A detailed exposition of the formalism is supplemented by two important applications: (i) tunneling into a biased Luttinger liquid with an impurity, and (ii) quantum Hall Fabry-Perot interferometry. - Highlights: Black-Right-Pointing-Pointer A nonequilibrium functional bosonization framework for quantum wire networks is developed Black-Right-Pointing-Pointer For the study of observables in the weak tunneling regime a real-time instanton method is elaborated. Black-Right-Pointing-Pointer We consider tunneling into a biased Luttinger liquid with an impurity. Black-Right-Pointing-Pointer We analyze electronic Fabry-Perot interferometers in the integer quantum Hall regime.
Analysis of Neural Networks through Base Functions
Zwaag, van der B.J.; Slump, C.H.; Spaanenburg, L.
2002-01-01
Problem statement. Despite their success-story, neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more
Energy Technology Data Exchange (ETDEWEB)
Gu, Renliang, E-mail: Venliang@iastate.edu, E-mail: ald@iastate.edu; Dogandžić, Aleksandar, E-mail: Venliang@iastate.edu, E-mail: ald@iastate.edu [Iowa State University, Center for Nondestructive Evaluation, 1915 Scholl Road, Ames, IA 50011 (United States)
2015-03-31
We develop a sparse image reconstruction method for polychromatic computed tomography (CT) measurements under the blind scenario where the material of the inspected object and the incident energy spectrum are unknown. To obtain a parsimonious measurement model parameterization, we first rewrite the measurement equation using our mass-attenuation parameterization, which has the Laplace integral form. The unknown mass-attenuation spectrum is expanded into basis functions using a B-spline basis of order one. We develop a block coordinate-descent algorithm for constrained minimization of a penalized negative log-likelihood function, where constraints and penalty terms ensure nonnegativity of the spline coefficients and sparsity of the density map image in the wavelet domain. This algorithm alternates between a Nesterov’s proximal-gradient step for estimating the density map image and an active-set step for estimating the incident spectrum parameters. Numerical simulations demonstrate the performance of the proposed scheme.
A Modified Beam Propagation Method Based on the Galerkin Method with Hermite-Gauss Basis Functions
Institute of Scientific and Technical Information of China (English)
Xiao Jinbiao; Liu Xu; Cai Chun; Fan Hehong; Sun Xiaohan
2006-01-01
A beam propagation method based on the Galerkin method with Hermite-Gauss basis functions for studying optical field propagation in weakly guiding dielectric structures is described. The selected basis functions naturally satisfy the required boundary conditions at infinity so that the boundary truncation is avoided. The paraxial propagation equation is converted into a set of first-order ordinary differential equations,which are solved by means of standard numerical library routines. Besides, the calculation is efficient due to its small resulted matrix. The evolution of the injected field and its normalized power along the propagation distance in an asymmetric slab waveguide and directional coupler are presented, and the solutions are good agreement with those obtained by finite difference BPM, which tests the validity of the present approach.
Phase estimation from noisy phase fringe patterns using linearly independent basis functions
International Nuclear Information System (INIS)
A novel technique is proposed for obtaining unwrapped phase estimation from a highly noisy exponential phase field. In this technique, the interference phase is represented as a linear combination of linearly independent and pre-defined basis functions along each row/column of the phase field at a time. Consequently, the problem of phase estimation is converted into the problem of the estimation of the weights of the basis functions. The extended Kalman filter formulation allows for the accurate estimation of these weights. The simulation results indicate that the formulation offers a strong noise robustness in the phase estimation. Experimental results obtained using digital holographic interferometry and digital speckle pattern interferometry set-ups are provided to demonstrate the practical applicability of the proposed method. (paper)
Multi-dimensional option pricing using radial basis functions and the generalized Fourier transform
Larsson, Elisabeth; Ahlander, Krister; Hall, Andreas
2008-12-01
We show that the generalized Fourier transform can be used for reducing the computational cost and memory requirements of radial basis function methods for multi-dimensional option pricing. We derive a general algorithm, including a transformation of the Black-Scholes equation into the heat equation, that can be used in any number of dimensions. Numerical experiments in two and three dimensions show that the gain is substantial even for small problem sizes. Furthermore, the gain increases with the number of dimensions.
Fukushima, Kimichika
2015-01-01
This paper presents analytical eigenenergies for a pair of confined fundamental fermion and antifermion under a linear potential derived from the Wilson loop for the non-Abelian Yang-Mills field. We use basis functions localized in spacetime, and the Hamiltonian matrix of the Dirac equation is analytically diagonalized. The squared system eigenenergies are proportional to the string tension and the absolute value of the Dirac's relativistic quantum number related to the total angular momentum, consistent with the expectation.
Analysis of the diffracted current basis functions used in the hybrid MoM-PO method
Institute of Scientific and Technical Information of China (English)
GONG Zhuqian; XIAO Boxun; ZHU Guoqiang; GUO Jianyan
2007-01-01
The combined moment method(MoM)-physical optics (PO)approach proposed by Bilow fails in some cases.Based on the theory of diffraction and the fundamental theory of electromagnetism,Bilow's diffracted current basis function was modified both within and outside the transition regions.The improved MoM-PO technique is validated by comparison with exact solutions for a right-angled perfectly conducting wedge at normal incidence.
Application of natural basis functions to soft x-ray tomography
International Nuclear Information System (INIS)
Natural basis functions (NBFs), also known as natural pixels in the literature, have been applied in tomographic reconstructions of simulated measurements for the JET soft x-ray system, which has a total of about 200 detectors spread over 6 directions. Various types of NBFs, i.e. normal, generalized and orthonormal NBFs, are reviewed. The number of basis functions is roughly equal to the number of measurements. Therefore, little a priori information is required as regularization and truncated singular-value decomposition can be used for the tomographic inversion. The results of NBFs are compared with reconstructions by the same solution technique using local basis functions (LBFs), and with the reconstructions of a conventional constrained-optimization tomography method with many more LBFs that requires more a priori information. Although the results of the conventional method are superior due to the a priori information, the results of the NBF and other LBF methods are reasonable and show the main features. Therefore, NBFs are a promising way to assess whether features in reconstructions are real or artefacts resulting from the a priori information. Of the NBFs, regular triangular (generalized) NBFs give the most acceptable reconstructions, much better than traditional square pixels, although the reconstructions with pyramid-shaped LBFs are also reasonable and have slightly smaller reconstruction errors. A more-regular (virtual) viewing geometry improves the reconstructions. However, simulations with a viewing geometry with a total of 480 channels spread over 12 directions clearly show that a priori information still improves the reconstructions considerably. (author)
Roohani Ghehsareh, Hadi; Kamal Etesami, Seyed; Hajisadeghi Esfahani, Maryam
2016-08-01
In the current work, the electromagnetic (EM) scattering from infinite perfectly conducting cylinders with arbitrary cross sections in both transverse magnetic (TM) and transverse electric (TE) modes is numerically investigated. The problems of TE and TM EM scattering can be mathematically modelled via the magnetic field integral equation (MFIE) and the electric field integral equation (EFIE), respectively. An efficient technique is performed to approximate the solution of these surface integral equations. In the proposed numerical method, compactly supported radial basis functions (RBFs) are employed as the basis functions. The radial and compactly supported properties of these basis functions substantially reduce the computational cost and improve the efficiency of the method. To show the accuracy of the proposed technique, it has been applied to solve three interesting test problems. Moreover, the method is well used to compute the electric current density and also the radar cross section (RCS) for some practical scatterers with different cross section geometries. The reported numerical results through the tables and figures demonstrate the efficiency and accuracy of the proposed technique.
A Functional Complexity Framework for the Analysis of Telecommunication Networks
Dzaferagic, Merim; Macaluso, Irene; Marchetti, Nicola
2016-01-01
The rapid evolution of network services demands new paradigms for studying and designing networks. In order to understand the underlying mechanisms that provide network functions, we propose a framework which enables the functional analysis of telecommunication networks. This framework allows us to isolate and analyse a network function as a complex system. We propose functional topologies to visualise the relationships between system entities and enable the systematic study of interactions between them. We also define a complexity metric $C_F$ (functional complexity) which quantifies the variety of structural patterns and roles of nodes in the topology. This complexity metric provides a wholly new approach to study the operation of telecommunication networks. We study the relationship between $C_F$ and different graph structures by analysing graph theory metrics in order to recognize complex organisations. $C_F$ is equal to zero for both a full mesh topology and a disconnected topology. We show that complexi...
Institute of Scientific and Technical Information of China (English)
尤文坚; 梁兵; 李荫军
2013-01-01
In view of problem that eddy-current sensor cannot reflect measured physical quantity accurately caused by higher nonlinear of output characteristic parameter, the paper proposed a scheme of using RBF neural network to fit output characteristic parameter of eddy-current sensor. The scheme uses newrb function to create RBF neural network, and takes measured physical quantity as input matrix and output of eddy-current sensor as output matrix to train the RBF neural network, so as to obtain low root-mean-square error and smooth output characteristic fitting curve of eddy-current sensor. The simulation result showed that RBF neural network can effectively realize fitting of output characteristic of eddy-current sensor by selecting proper creating function and expanding coefficient.%针对电涡流传感器的输出特性参数非线性较大,不能精确地反映被测物理量的问题,提出了一种采用径向基神经网络对电涡流传感器的输出特性参数进行拟合的方案.该方案采用newrb函数创建一个径向基神经网络,以被测物理量作为输入矩阵、电涡流传感器输出电压作为输出矩阵,对该径向基神经网络进行训练,从而可得到均方根误差小且光滑的电涡流传感器输出特性拟合曲线.实验结果表明,只要选择合适的创建函数和扩展系数,径向基神经网络能有效地实现电涡流传感器输出特性的拟合.
Hellmann-Feynman forces within the DFT + U in Wannier functions basis.
Novoselov, D; Korotin, Dm M; Anisimov, V I
2015-08-19
The most general way to describe localized atomic-like electronic states in strongly correlated compounds is to use Wannier functions. In the present paper we continue development of widely-used DFT + U method with the Wannier function basis set and propose a technique to calculate Hubbard contribution to atomic forces. The technique was implemented as a part of plane-waves pseudopotential code Quantum-ESPRESSO and tested on two compounds: charge transfer insulator NiO with cubic crystal structure and correlated metal SrVO3 with perovskite structure.
Heidari, Mohammad; Heidari, Ali; Homaei, Hadi
2014-01-01
The static pull-in instability of beam-type microelectromechanical systems (MEMS) is theoretically investigated. Two engineering cases including cantilever and double cantilever microbeam are considered. Considering the midplane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size-dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, capable of capturing the size effect. By selecting a range of geometric parameters such as beam lengths, width, thickness, gaps, and size effect, we identify the static pull-in instability voltage. A MAPLE package is employed to solve the nonlinear differential governing equations to obtain the static pull-in instability voltage of microbeams. Radial basis function artificial neural network with two functions has been used for modeling the static pull-in instability of microcantilever beam. The network has four inputs of length, width, gap, and the ratio of height to scale parameter of beam as the independent process variables, and the output is static pull-in voltage of microbeam. Numerical data, employed for training the network, and capabilities of the model have been verified in predicting the pull-in instability behavior. The output obtained from neural network model is compared with numerical results, and the amount of relative error has been calculated. Based on this verification error, it is shown that the radial basis function of neural network has the average error of 4.55% in predicting pull-in voltage of cantilever microbeam. Further analysis of pull-in instability of beam under different input conditions has been investigated and comparison results of modeling with numerical considerations shows a good agreement, which also proves the feasibility and effectiveness of the adopted approach. The results reveal significant influences of size effect and geometric parameters on the static pull-in instability voltage of MEMS.
Basis of symmetric polynomials for many-boson light-front wave functions.
Chabysheva, Sophia S; Hiller, John R
2014-12-01
We provide an algorithm for the construction of orthonormal multivariate polynomials that are symmetric with respect to the interchange of any two coordinates on the unit hypercube and are constrained to the hyperplane where the sum of the coordinates is one. These polynomials form a basis for the expansion of bosonic light-front momentum-space wave functions, as functions of longitudinal momentum, where momentum conservation guarantees that the fractions are on the interval [0,1] and sum to one. This generalizes earlier work on three-boson wave functions to wave functions for arbitrarily many identical bosons. A simple application in two-dimensional ϕ(4) theory illustrates the use of these polynomials.
Changes in cognitive state alter human functional brain networks
Directory of Open Access Journals (Sweden)
Malaak Nasser Moussa
2011-08-01
Full Text Available The study of the brain as a whole system can be accomplished using network theory principles. Research has shown that human functional brain networks during a resting state exhibit small-world properties and high degree nodes, or hubs, localized to brain areas consistent with the default mode network (DMN. However, the study of brain networks across different tasks and or cognitive states has been inconclusive. Research in this field is important because the underpinnings of behavioral output are inherently dependent on whether or not brain networks are dynamic. This is the first comprehensive study to evaluate multiple network metrics at a voxel-wise resolution in the human brain at both the whole brain and regional level under various conditions: resting state, visual stimulation, and multisensory (auditory and visual stimulation. Our results show that despite global network stability, functional brain networks exhibit considerable task-induced changes in connectivity, efficiency, and community structure at the regional level.
Application of Radial Basis Network Model for HIV/AIDs Regimen Specifications
Balasubramanie, P
2009-01-01
HIV/AIDs Regimen specification one of many problems for which bioinformaticians have implemented and trained machine learning methods such as neural networks. Predicting HIV resistance would be much easier, but unfortunately we rarely have enough structural information available to train a neural network. To network model designed to predict how long the HIV patient can prolong his/her life time with certain regimen specification. To learn this model 300 patient's details have taken as a training set to train the network and 100 patients medical history has taken to test this model. This network model is trained using MAT lab implementation.
Novel Sequential Neural Network Learning Algorithm for Function Approximation
Institute of Scientific and Technical Information of China (English)
KANG Huai-qi; SHI Cai-cheng; HE Pei-kun; LI Xiao-qiong
2007-01-01
A novel sequential neural network learning algorithm for function approximation is presented.The multi-step-ahead output predictor of the stochastic time series is introduced to the growing and pruning network for constructing network structure.And the network parameters are adjusted by the proportional differential filter (PDF) rather than EKF when the network growing criteria are not met.Experimental results show that the proposed algorithm can obtain a more compact network along with a smaller error in mean square sense than other typical sequential learning algorithms.
A Mixed Basis Density Functional Approach for Low Dimensional Systems with B-splines
Ren, Chung-Yuan; Chang, Yia-Chung
2014-01-01
A mixed basis approach based on density functional theory is employed for low dimensional systems. The basis functions are taken to be plane waves for the periodic direction multiplied by B-spline polynomials in the non-periodic direction. B-splines have the following advantages:(1) the associated matrix elements are sparse, (2) B-splines possess a superior treatment of derivatives, (3) B-splines are not associated with atomic positions when the geometry structure is optimized, making the geometry optimization easy to implement. With this mixed basis set we can directly calculate the total energy of the system instead of using the conventional supercell model with a slab sandwiched between vacuum regions. A generalized Lanczos-Krylov iterative method is implemented for the diagonalization of the Hamiltonian matrix. To demonstrate the present approach, we apply it to study the C(001)-(2x1) surface with the norm-conserving pseudopotential, the n-type delta-doped graphene, and graphene nanoribbon with Vanderbilt...
Enhancing the functional content of eukaryotic protein interaction networks.
Directory of Open Access Journals (Sweden)
Gaurav Pandey
Full Text Available Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, these networks face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we apply a robust measure of local network structure called common neighborhood similarity (CNS to address these challenges. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of human and fly protein interactions, and a set of over 100 GO terms for both, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the HC.cont measure and other continuous CNS measures perform well this task, especially for large networks. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures to prune out noisy edges and enhance functional coherence in the transformed networks.
Cognitive fitness of cost-efficient brain functional networks
Bassett, Danielle S; Bullmore, Edward T.; Meyer-Lindenberg, Andreas; Apud, José A; Weinberger, Daniel R.; Coppola, Richard
2009-01-01
The human brain's capacity for cognitive function is thought to depend on coordinated activity in sparsely connected, complex networks organized over many scales of space and time. Recent work has demonstrated that human brain networks constructed from neuroimaging data have economical small-world properties that confer high efficiency of information processing at relatively low connection cost. However, it has been unclear how the architecture of complex brain networks functioning at differe...
Functional brain networks associated with eating behaviors in obesity
Bo-yong Park; Jongbum Seo; Hyunjin Park
2016-01-01
Obesity causes critical health problems including diabetes and hypertension that affect billions of people worldwide. Obesity and eating behaviors are believed to be closely linked but their relationship through brain networks has not been fully explored. We identified functional brain networks associated with obesity and examined how the networks were related to eating behaviors. Resting state functional magnetic resonance imaging (MRI) scans were obtained for 82 participants. Data were from...
Tuan, P H; Liang, H C; Tung, J C; Chiang, P Y; Huang, K F; Chen, Y F
2015-12-01
The coupling interaction between the driving source and the RLC network is explored and characterized as the effective impedance. The mathematical form of the derived effective impedance is verified to be identical to the meromorphic function of the singular billiards with a truncated basis. By using the derived impedance function, the resonant modes of the RLC network can be divided into the open-circuit and short-circuit states to manifest the evolution of eigenvalues and eigenstates from closed quantum billiards to the singular billiards with a truncated basis in the strongly coupled limit. The substantial differences of the wave patterns between the uncoupled and strongly coupled eigenmodes in the two-dimensional wave systems can be clearly revealed with the RLC network. Finally, the short-circuit resonant states are exploited to confirm that the experimental Chladni nodal-line patterns in the vibrating plate are the resonant modes subject to the strong coupling between the oscillation system and the driving source. PMID:26764773
Rational design of functional and tunable oscillating enzymatic networks
Semenov, Sergey N.; Wong, Albert S. Y.; van der Made, R. Martijn; Postma, Sjoerd G. J.; Groen, Joost; van Roekel, Hendrik W. H.; de Greef, Tom F. A.; Huck, Wilhelm T. S.
2015-02-01
Life is sustained by complex systems operating far from equilibrium and consisting of a multitude of enzymatic reaction networks. The operating principles of biology's regulatory networks are known, but the in vitro assembly of out-of-equilibrium enzymatic reaction networks has proved challenging, limiting the development of synthetic systems showing autonomous behaviour. Here, we present a strategy for the rational design of programmable functional reaction networks that exhibit dynamic behaviour. We demonstrate that a network built around autoactivation and delayed negative feedback of the enzyme trypsin is capable of producing sustained oscillating concentrations of active trypsin for over 65 h. Other functions, such as amplification, analog-to-digital conversion and periodic control over equilibrium systems, are obtained by linking multiple network modules in microfluidic flow reactors. The methodology developed here provides a general framework to construct dissipative, tunable and robust (bio)chemical reaction networks.
Emotion-Induced Topological Changes in Functional Brain Networks.
Park, Chang-Hyun; Lee, Hae-Kook; Kweon, Yong-Sil; Lee, Chung Tai; Kim, Ki-Tae; Kim, Young-Joo; Lee, Kyoung-Uk
2016-01-01
In facial expression perception, a distributed network is activated according to stimulus context. We proposed that an interaction between brain activation and stimulus context in response to facial expressions could signify a pattern of interactivity across the whole brain network beyond the face processing network. Functional magnetic resonance imaging data were acquired for 19 young healthy subjects who were exposed to either emotionally neutral or negative facial expressions. We constructed group-wise functional brain networks for 12 face processing areas [bilateral inferior occipital gyri (IOG), fusiform gyri (FG), superior temporal sulci (STS), amygdalae (AMG), inferior frontal gyri (IFG), and orbitofrontal cortices (OFC)] and for 73 whole brain areas, based on partial correlation of mean activation across subjects. We compared the topological properties of the networks with respect to functional distance-based measures, global and local efficiency, between the two types of face stimulus. In both face processing and whole brain networks, global efficiency was lower and local efficiency was higher for negative faces relative to neutral faces, indicating that network topology differed according to stimulus context. Particularly in the face processing network, emotion-induced changes in network topology were attributable to interactions between core (bilateral IOG, FG, and STS) and extended (bilateral AMG, IFG, and OFC) systems. These results suggest that changes in brain activation patterns in response to emotional face stimuli could be revealed as changes in the topological properties of functional brain networks for the whole brain as well as for face processing areas.
Voytek, Bradley; Robert T Knight
2015-01-01
Perception, cognition, and social interaction depend upon coordinated neural activity. This coordination operates within noisy, overlapping, and distributed neural networks operating at multiple timescales. These networks are built upon a structural scaffolding with intrinsic neuroplasticity that changes with development, aging, disease, and personal experience. In this paper we begin from the perspective that successful interregional communication relies upon the transient synchronization be...
Dimensionality reduction in conic section function neural network
Indian Academy of Sciences (India)
Tulay Yildirim; Lale Ozyilmaz
2002-12-01
This paper details how dimensionality can be reduced in conic section function neural networks (CSFNN). This is particularly important for hardware implementation of networks. One of the main problems to be solved when considering the hardware design is the high connectivity requirement. If the effect that each of the network inputs has on the network output after training a neural network is known, then some inputs can be removed from the network. Consequently, the dimensionality of the network, and hence, the connectivity and the training time can be reduced. Sensitivity analysis, which extracts the cause and effect relationship between the inputs and outputs of the network, has been proposed as a method to achieve this and is investigated for Iris plant, thyroid disease and ionosphere databases. Simulations demonstrate the validity of the method used.
Directory of Open Access Journals (Sweden)
1 Taiwo O. A
2013-01-01
Full Text Available The problem of solving special nth-order linear integro-differential equations has special importance in engineering and sciences that constitutes a good model for many systems in various fields. In this paper, we construct canonical polynomial from the differential parts of special nth-order integro-differential equations and use it as our basis function for the numerical solutions of special nth-order integro-differential equations. The results obtained by this method are compared with those obtained by Adomian Decomposition method. It is also observed that the new method is an effective method with high accuracy. Some examples are given to illustrate the method.
Vo, P. T.; Eversman, W.
1978-01-01
The method of weighted residuals (MWR) in the form of a modified Galerkin method with trigonometric basis functions is used to compute the transmission of sound in an axisymmetric duct. The method is used to generate the axial wave number for uniform ducts. These are compared with exact solutions generated by a formal eigenvalue routine in the hard-wall case and a Runge-Kutta integration eigenvalue scheme in the soft-wall case. The method is applicable to both flow and no-flow cases.
A correlated basis-function description of 16O with realistic interactions
Boscá, M. C.
1994-01-01
The correlated basis-function theory is applied at the lowest order to analyze the ground state and low-energy spectrum of the 16O nucleus. Results are quoted for both the Urbana and the Argonne υ 14 nucleon-nucleon interactions. The work includes state-dependent correlations and their radial components are determined by solving a set of Euler-Lagrange equations. The matrix elements are computed by using a cluster expansion and the sequential condition is imposed in order to insure convergence. The results clearly disagree with the experimental values.
Functional clustering in hippocampal cultures: relating network structure and dynamics
International Nuclear Information System (INIS)
In this work we investigate the relationship between gross anatomic structural network properties, neuronal dynamics and the resultant functional structure in dissociated rat hippocampal cultures. Specifically, we studied cultures as they developed under two conditions: the first supporting glial cell growth (high glial group), and the second one inhibiting it (low glial group). We then compared structural network properties and the spatio-temporal activity patterns of the neurons. Differences in dynamics between the two groups could be linked to the impact of the glial network on the neuronal network as the cultures developed. We also implemented a recently developed algorithm called the functional clustering algorithm (FCA) to obtain the resulting functional network structure. We show that this new algorithm is useful for capturing changes in functional network structure as the networks evolve over time. The FCA detects changes in functional structure that are consistent with expected dynamical differences due to the impact of the glial network. Cultures in the high glial group show an increase in global synchronization as the cultures age, while those in the low glial group remain locally synchronized. We additionally use the FCA to quantify the amount of synchronization present in the cultures and show that the total level of synchronization in the high glial group is stronger than in the low glial group. These results indicate an interdependence between the glial and neuronal networks present in dissociated cultures
A Game for Energy-Aware Allocation of Virtualized Network Functions
Directory of Open Access Journals (Sweden)
Roberto Bruschi
2016-01-01
Full Text Available Network Functions Virtualization (NFV is a network architecture concept where network functionality is virtualized and separated into multiple building blocks that may connect or be chained together to implement the required services. The main advantages consist of an increase in network flexibility and scalability. Indeed, each part of the service chain can be allocated and reallocated at runtime depending on demand. In this paper, we present and evaluate an energy-aware Game-Theory-based solution for resource allocation of Virtualized Network Functions (VNFs within NFV environments. We consider each VNF as a player of the problem that competes for the physical network node capacity pool, seeking the minimization of individual cost functions. The physical network nodes dynamically adjust their processing capacity according to the incoming workload, by means of an Adaptive Rate (AR strategy that aims at minimizing the product of energy consumption and processing delay. On the basis of the result of the nodes’ AR strategy, the VNFs’ resource sharing costs assume a polynomial form in the workflows, which admits a unique Nash Equilibrium (NE. We examine the effect of different (unconstrained and constrained forms of the nodes’ optimization problem on the equilibrium and compare the power consumption and delay achieved with energy-aware and non-energy-aware strategy profiles.
Network-Level Structure-Function Relationships in Human Neocortex.
Mišić, Bratislav; Betzel, Richard F; de Reus, Marcel A; van den Heuvel, Martijn P; Berman, Marc G; McIntosh, Anthony R; Sporns, Olaf
2016-07-01
The dynamics of spontaneous fluctuations in neural activity are shaped by underlying patterns of anatomical connectivity. While numerous studies have demonstrated edge-wise correspondence between structural and functional connections, much less is known about how large-scale coherent functional network patterns emerge from the topology of structural networks. In the present study, we deploy a multivariate statistical technique, partial least squares, to investigate the association between spatially extended structural networks and functional networks. We find multiple statistically robust patterns, reflecting reliable combinations of structural and functional subnetworks that are optimally associated with one another. Importantly, these patterns generally do not show a one-to-one correspondence between structural and functional edges, but are instead distributed and heterogeneous, with many functional relationships arising from nonoverlapping sets of anatomical connections. We also find that structural connections between high-degree hubs are disproportionately represented, suggesting that these connections are particularly important in establishing coherent functional networks. Altogether, these results demonstrate that the network organization of the cerebral cortex supports the emergence of diverse functional network configurations that often diverge from the underlying anatomical substrate. PMID:27102654
Network Function Virtualization Technology:Progress and Standardization
Institute of Scientific and Technical Information of China (English)
Huiling Zhao; Yunpeng Xie; Fan Shi
2014-01-01
Network innovation and business transformation are both necessary for telecom operators to adapt to new situations, but operators face challenges in terms of network bearer complexity, business centralization, and IT/CT integration. Network function virtualiza-tion (NFV) may inspire new development ideas, but many doubts still exist within industry, especially about how to introduce NFV into an operator ’s network. This article describes the latest progress in NFV standardization, NFV requirements and hot technology issues, and typical NFV applications in an operator networks.
Density functional and neural network analysis
DEFF Research Database (Denmark)
Jalkanen, K. J.; Suhai, S.; Bohr, Henrik
1997-01-01
dichroism (VCD) intensities. The large changes due to hydration on the structures, relative stability of conformers, and in the VA and VCD spectra observed experimentally are reproduced by the DFT calculations. Furthermore a neural network was constructed for reproducing the inverse scattering data (infer...... the structural coordinates from spectroscopic data) that the DFT method could produce. Finally the neural network performances are used to monitor a sensitivity or dependence analysis of the importance of secondary structures....
GRACE L1b inversion through a self-consistent modified radial basis function approach
Yang, Fan; Kusche, Juergen; Rietbroek, Roelof; Eicker, Annette
2016-04-01
Implementing a regional geopotential representation such as mascons or, more general, RBFs (radial basis functions) has been widely accepted as an efficient and flexible approach to recover the gravity field from GRACE (Gravity Recovery and Climate Experiment), especially at higher latitude region like Greenland. This is since RBFs allow for regionally specific regularizations over areas which have sufficient and dense GRACE observations. Although existing RBF solutions show a better resolution than classical spherical harmonic solutions, the applied regularizations cause spatial leakage which should be carefully dealt with. It has been shown that leakage is a main error source which leads to an evident underestimation of yearly trend of ice-melting over Greenland. Unlike some popular post-processing techniques to mitigate leakage signals, this study, for the first time, attempts to reduce the leakage directly in the GRACE L1b inversion by constructing an innovative modified (MRBF) basis in place of the standard RBFs to retrieve a more realistic temporal gravity signal along the coastline. Our point of departure is that the surface mass loading associated with standard RBF is smooth but disregards physical consistency between continental mass and passive ocean response. In this contribution, based on earlier work by Clarke et al.(2007), a physically self-consistent MRBF representation is constructed from standard RBFs, with the help of the sea level equation: for a given standard RBF basis, the corresponding MRBF basis is first obtained by keeping the surface load over the continent unchanged, but imposing global mass conservation and equilibrium response of the oceans. Then, the updated set of MRBFs as well as standard RBFs are individually employed as the basis function to determine the temporal gravity field from GRACE L1b data. In this way, in the MRBF GRACE solution, the passive (e.g. ice melting and land hydrology response) sea level is automatically
Systematic Functional Annotation and Visualization of Biological Networks.
Baryshnikova, Anastasia
2016-06-22
Large-scale biological networks represent relationships between genes, but our understanding of how networks are functionally organized is limited. Here, I describe spatial analysis of functional enrichment (SAFE), a systematic method for annotating biological networks and examining their functional organization. SAFE visualizes the network in 2D space and measures the continuous distribution of functional enrichment across local neighborhoods, producing a list of the associated functions and a map of their relative positioning. I applied SAFE to annotate the Saccharomyces cerevisiae genetic interaction similarity network and protein-protein interaction network with gene ontology terms. SAFE annotations of the genetic network matched manually derived annotations, while taking less than 1% of the time, and proved robust to noise and sensitive to biological signal. Integration of genetic interaction and chemical genomics data using SAFE revealed a link between vesicle-mediate transport and resistance to the anti-cancer drug bortezomib. These results demonstrate the utility of SAFE for examining biological networks and understanding their functional organization. PMID:27237738
Functional imaging in oncology. Biophysical basis and technical approaches. Vol. 1
Energy Technology Data Exchange (ETDEWEB)
Luna, Antonio [Health Time Group, Jaen (Spain); University Hospitals, Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Radiology; Vilanova, Joan C. [Clinica Girona - Hospital Sta. Caterina, Girona (Spain); Hygino da Cruz, L. Celso Jr. [CDPI and IRM, Rio de Janeiro, RJ (Brazil). Dept. of Radiology; Rossi, Santiago E. (ed.) [Centro de Diagnostico, Buenos Aires (Argentina)
2014-07-01
Easy-to-read manual on new functional imaging techniques in oncology. Explains current clinical applications and outlines future avenues. Includes numerous high-quality illustrations to highlight the major teaching points. In the new era of functional and molecular imaging, both currently available imaging biomarkers and biomarkers under development are expected to lead to major changes in the management of oncological patients. This well-illustrated two-volume book is a practical manual on the various imaging techniques capable of delivering functional information on cancer, including preclinical and clinical imaging techniques, based on US, CT, MRI, PET and hybrid modalities. This first volume explains the biophysical basis for these functional imaging techniques and describes the techniques themselves. Detailed information is provided on the imaging of cancer hallmarks, including angiogenesis, tumor metabolism, and hypoxia. The techniques and their roles are then discussed individually, covering the full range of modalities in clinical use as well as new molecular and functional techniques. The value of a multiparametric approach is also carefully considered.
Directory of Open Access Journals (Sweden)
MALLESWARAN M,
2010-12-01
Full Text Available Global positioning System (GPS and Inertial Navigation System (INS data can be integrated together to provide a reliable navigation. GPS/INS data integration provides reliable navigation solutions by overcoming each of their shortcomings, including signal blockage for GPS and increase in position errors with time for INS. This paper aims to provide GPS/INS data integration utilizing Artificial Neural Network (ANN architecture. This architecture is based on Feed Forward Neural Networks, which generally includes Radial Basis Function (RBF neural network and Back Propagation neural network (BPN. These are systematic methods for training multi-layer artificial networks. The BPN-ANN and RBF-ANN modules are trained to predict the INS position error and provide accurate positioning of the moving vehicle. This paper also compares performance of theGPS/INS data integration system by using different activation function like Bipolar Sigmoidal Function (BPSF, Binary Sigmoidal Function (BISF, Hyperbolic Tangential Function (HTF and Gaussian Function (GF in BPN-ANN and using Gaussian function in RBF-ANN.
Accelerating wavefunction in density-functional-theory embedding by truncating the active basis set.
Bennie, Simon J; Stella, Martina; Miller, Thomas F; Manby, Frederick R
2015-07-14
Methods where an accurate wavefunction is embedded in a density-functional description of the surrounding environment have recently been simplified through the use of a projection operator to ensure orthogonality of orbital subspaces. Projector embedding already offers significant performance gains over conventional post-Hartree-Fock methods by reducing the number of correlated occupied orbitals. However, in our first applications of the method, we used the atomic-orbital basis for the full system, even for the correlated wavefunction calculation in a small, active subsystem. Here, we further develop our method for truncating the atomic-orbital basis to include only functions within or close to the active subsystem. The number of atomic orbitals in a calculation on a fixed active subsystem becomes asymptotically independent of the size of the environment, producing the required O(N(0)) scaling of cost of the calculation in the active subsystem, and accuracy is controlled by a single parameter. The applicability of this approach is demonstrated for the embedded many-body expansion of binding energies of water hexamers and calculation of reaction barriers of SN2 substitution of fluorine by chlorine in α-fluoroalkanes.
Green's function multiple-scattering theory with a truncated basis set: An augmented-KKR formalism
Alam, Aftab; Khan, Suffian N.; Smirnov, A. V.; Nicholson, D. M.; Johnson, Duane D.
2014-11-01
The Korringa-Kohn-Rostoker (KKR) Green's function, multiple-scattering theory is an efficient site-centered, electronic-structure technique for addressing an assembly of N scatterers. Wave functions are expanded in a spherical-wave basis on each scattering center and indexed up to a maximum orbital and azimuthal number Lmax=(l,mmax), while scattering matrices, which determine spectral properties, are truncated at Lt r=(l,mt r) where phase shifts δl >ltr are negligible. Historically, Lmax is set equal to Lt r, which is correct for large enough Lmax but not computationally expedient; a better procedure retains higher-order (free-electron and single-site) contributions for Lmax>Lt r with δl >ltr set to zero [X.-G. Zhang and W. H. Butler, Phys. Rev. B 46, 7433 (1992), 10.1103/PhysRevB.46.7433]. We present a numerically efficient and accurate augmented-KKR Green's function formalism that solves the KKR equations by exact matrix inversion [R3 process with rank N (ltr+1 ) 2 ] and includes higher-L contributions via linear algebra [R2 process with rank N (lmax+1) 2 ]. The augmented-KKR approach yields properly normalized wave functions, numerically cheaper basis-set convergence, and a total charge density and electron count that agrees with Lloyd's formula. We apply our formalism to fcc Cu, bcc Fe, and L 1 0 CoPt and present the numerical results for accuracy and for the convergence of the total energies, Fermi energies, and magnetic moments versus Lmax for a given Lt r.
D'Ostilio, Kevin; Garraux, Gaëtan
2016-01-01
The high prevalence of major depressive disorder in people with Parkinson's disease (PD), its negative impact on health-related quality of life and the low response rate to conventional pharmacological therapies call to seek innovative treatments. Here, we review the new approaches for treating major depressive disorder in patients with PD within the framework of the network model of depression. According to this model, major depressive disorder reflects maladaptive neuronal plasticity. Non-invasive brain stimulation (NIBS) using high frequency repetitive transcranial magnetic stimulation (rTMS) over the prefrontal cortex has been proposed as a feasible and effective strategy with minimal risk. The neurobiological basis of its therapeutic effect may involve neuroplastic modifications in limbic and cognitive networks. However, the way this networks reorganize might be strongly influenced by the environment. To address this issue, we propose a combined strategy that includes NIBS together with cognitive and behavioral interventions.
Changes in brain functional network connectivity after stroke
Institute of Scientific and Technical Information of China (English)
Wei Li; Yapeng Li; Wenzhen Zhu; Xi Chen
2014-01-01
Studies have shown that functional network connection models can be used to study brain net-work changes in patients with schizophrenia. In this study, we inferred that these models could also be used to explore functional network connectivity changes in stroke patients. We used independent component analysis to find the motor areas of stroke patients, which is a novel way to determine these areas. In this study, we collected functional magnetic resonance imaging datasets from healthy controls and right-handed stroke patients following their ifrst ever stroke. Using independent component analysis, six spatially independent components highly correlat-ed to the experimental paradigm were extracted. Then, the functional network connectivity of both patients and controls was established to observe the differences between them. The results showed that there were 11 connections in the model in the stroke patients, while there were only four connections in the healthy controls. Further analysis found that some damaged connections may be compensated for by new indirect connections or circuits produced after stroke. These connections may have a direct correlation with the degree of stroke rehabilitation. Our ifndings suggest that functional network connectivity in stroke patients is more complex than that in hea-lthy controls, and that there is a compensation loop in the functional network following stroke. This implies that functional network reorganization plays a very important role in the process of rehabilitation after stroke.
Boyd, John P.
2011-02-01
Radial basis function (RBF) interpolants have become popular in computer graphics, neural networks and for solving partial differential equations in many fields of science and engineering. In this article, we compare five different species of RBFs: Gaussians, hyperbolic secant (sech's), inverse quadratics, multiquadrics and inverse multiquadrics. We show that the corresponding cardinal functions for a uniform, unbounded grid are all approximated by the same function: C(X) ∼ (1/(ρ)) sin (πX)/sinh (πX/ρ) for some constant ρ(α) which depends on the inverse width parameter (“shape parameter”) α of the RBF and also on the RBF species. The error in this approximation is exponentially small in 1/α for sech's and inverse quadratics and exponentially small in 1/α2 for Gaussians; the error is proportional to α4 for multiquadrics and inverse multiquadrics. The error in all cases is small even for α ∼ O(1). These results generalize to higher dimensions. The Gaussian RBF cardinal functions in any number of dimensions d are, without approximation, the tensor product of one dimensional Gaussian cardinal functions: Cd(x1,x2…,xd)=∏j=1dC(xj). For other RBF species, we show that the two-dimensional cardinal functions are well approximated by the products of one-dimensional cardinal functions; again the error goes to zero as α → 0. The near-identity of the cardinal functions implies that all five species of RBF interpolants are (almost) the same, despite the great differences in the RBF ϕ's themselves.
Triangles as basis to detect communities: an appication to Twitter's network
Abdelsadek, Youcef; Herrmann, Francine; Kacem, Imed; Otjacques, Benoît
2016-01-01
Nowadays, the interest given by the scientific community to the investigation of the data generated by social networks is increasing as much as the exponential increasing of social network data. The data structure complexity is one among the snags, which slowdown their understanding. On the other hand, community detection in social networks helps the analyzers to reveal the structure and the underlying semantic within communities. In this paper we propose an interactive visualization approach relying on our application NLCOMS, which uses synchronous and related views for graph and community visualization. Additionally, we present our algorithm for community detection in networks. A computation study is conducted on instances generated with the LFR [9]-[10] benchmark. Finally, in order to assess our approach on real-world data, we consider the data of the ANR-Info-RSN project. The latter addresses community detection in Twitter.
Stability condition of FAST TCP in high speed network Oil the basis of control theory
Institute of Scientific and Technical Information of China (English)
Zhao Fuzhe; Zhou Jianzhong; Luo Zhimeng; Xiao Yang
2008-01-01
Considering the instability of data transferred existing in high speed network.a near method is proposed for improving the stability using control theory.Under this method,the mathematical model of such a network is established.Stability condition is derived from the mathematical model.Several sivaulation experiments are performed.The results show that the method can increase the stability of data transferred in terms of the congestion window,queue size,and sending rate of the source.
Directory of Open Access Journals (Sweden)
Lukas Falat
2016-01-01
Full Text Available This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.
Falat, Lukas; Marcek, Dusan; Durisova, Maria
2016-01-01
This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process. PMID:26977450
Falat, Lukas; Marcek, Dusan; Durisova, Maria
2016-01-01
This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.
Emotional cuing to test attentional network functioning in trait anxiety
Gómez Íñiguez, Consolación; Luis J Fuentes; Martínez-Sánchez, Francisco; Campoy, Guillermo; Montoro, Pedro J.; Palmero Cantero, Francisco
2014-01-01
The Attention Networks Test (ANT) has been widely used to assess the three attentional networks proposed by Posner and his collaborators. Here we present a version of the ANT that uses emotionally laden words as cues to evaluate the functioning of the attention networks and their interactions. University students participated in the task and the results replicated those found in previous studies with the original version of the test. Then, those with extreme scores on a trai...
Arabidopsis gene co-expression network and its functional modules
Directory of Open Access Journals (Sweden)
Dash Sudhansu
2009-10-01
Full Text Available Abstract Background Biological networks characterize the interactions of biomolecules at a systems-level. One important property of biological networks is the modular structure, in which nodes are densely connected with each other, but between which there are only sparse connections. In this report, we attempted to find the relationship between the network topology and formation of modular structure by comparing gene co-expression networks with random networks. The organization of gene functional modules was also investigated. Results We constructed a genome-wide Arabidopsis gene co-expression network (AGCN by using 1094 microarrays. We then analyzed the topological properties of AGCN and partitioned the network into modules by using an efficient graph clustering algorithm. In the AGCN, 382 hub genes formed a clique, and they were densely connected only to a small subset of the network. At the module level, the network clustering results provide a systems-level understanding of the gene modules that coordinate multiple biological processes to carry out specific biological functions. For instance, the photosynthesis module in AGCN involves a very large number (> 1000 of genes which participate in various biological processes including photosynthesis, electron transport, pigment metabolism, chloroplast organization and biogenesis, cofactor metabolism, protein biosynthesis, and vitamin metabolism. The cell cycle module orchestrated the coordinated expression of hundreds of genes involved in cell cycle, DNA metabolism, and cytoskeleton organization and biogenesis. We also compared the AGCN constructed in this study with a graphical Gaussian model (GGM based Arabidopsis gene network. The photosynthesis, protein biosynthesis, and cell cycle modules identified from the GGM network had much smaller module sizes compared with the modules found in the AGCN, respectively. Conclusion This study reveals new insight into the topological properties of
Leménager, Tagrid; Dieter, Julia; Hill, Holger; Hoffmann, Sabine; Reinhard, Iris; Beutel, Martin; Vollstädt-Klein, Sabine; Kiefer, Falk; Mann, Karl
2016-09-01
Background and aims Internet gaming addiction appears to be related to self-concept deficits and increased angular gyrus (AG)-related identification with one's avatar. For increased social network use, a few existing studies suggest striatal-related positive social feedback as an underlying factor. However, whether an impaired self-concept and its reward-based compensation through the online presentation of an idealized version of the self are related to pathological social network use has not been investigated yet. We aimed to compare different stages of pathological Internet game and social network use to explore the neural basis of avatar and self-identification in addictive use. Methods About 19 pathological Internet gamers, 19 pathological social network users, and 19 healthy controls underwent functional magnetic resonance imaging while completing a self-retrieval paradigm, asking participants to rate the degree to which various self-concept-related characteristics described their self, ideal, and avatar. Self-concept-related characteristics were also psychometrically assessed. Results Psychometric testing indicated that pathological Internet gamers exhibited higher self-concept deficits generally, whereas pathological social network users exhibit deficits in emotion regulation only. We observed left AG hyperactivations in Internet gamers during avatar reflection and a correlation with symptom severity. Striatal hypoactivations during self-reflection (vs. ideal reflection) were observed in social network users and were correlated with symptom severity. Discussion and conclusion Internet gaming addiction appears to be linked to increased identification with one's avatar, evidenced by high left AG activations in pathological Internet gamers. Addiction to social networks seems to be characterized by emotion regulation deficits, reflected by reduced striatal activation during self-reflection compared to during ideal reflection. PMID:27415603
Leménager, Tagrid; Dieter, Julia; Hill, Holger; Hoffmann, Sabine; Reinhard, Iris; Beutel, Martin; Vollstädt-Klein, Sabine; Kiefer, Falk; Mann, Karl
2016-09-01
Background and aims Internet gaming addiction appears to be related to self-concept deficits and increased angular gyrus (AG)-related identification with one's avatar. For increased social network use, a few existing studies suggest striatal-related positive social feedback as an underlying factor. However, whether an impaired self-concept and its reward-based compensation through the online presentation of an idealized version of the self are related to pathological social network use has not been investigated yet. We aimed to compare different stages of pathological Internet game and social network use to explore the neural basis of avatar and self-identification in addictive use. Methods About 19 pathological Internet gamers, 19 pathological social network users, and 19 healthy controls underwent functional magnetic resonance imaging while completing a self-retrieval paradigm, asking participants to rate the degree to which various self-concept-related characteristics described their self, ideal, and avatar. Self-concept-related characteristics were also psychometrically assessed. Results Psychometric testing indicated that pathological Internet gamers exhibited higher self-concept deficits generally, whereas pathological social network users exhibit deficits in emotion regulation only. We observed left AG hyperactivations in Internet gamers during avatar reflection and a correlation with symptom severity. Striatal hypoactivations during self-reflection (vs. ideal reflection) were observed in social network users and were correlated with symptom severity. Discussion and conclusion Internet gaming addiction appears to be linked to increased identification with one's avatar, evidenced by high left AG activations in pathological Internet gamers. Addiction to social networks seems to be characterized by emotion regulation deficits, reflected by reduced striatal activation during self-reflection compared to during ideal reflection.
The Efficiency of a Small-World Functional Brain Network
Institute of Scientific and Technical Information of China (English)
ZHAO Qing-Bai; ZHANG Xiao-Fei; SUI Dan-Ni; ZHOU Zhi-Jin; CHEN Qi-Cai; TANG Yi-Yuan
2012-01-01
We investigate whether the small-world topology of a functional brain network means high information processing efficiency by calculating the correlation between the small-world measures of a functional brain network and behavioral reaction during an imagery task.Functional brain networks are constructed by multichannel eventrelated potential data,in which the electrodes are the nodes and the functional connectivities between them are the edges.The results show that the correlation between small-world measures and reaction time is task-specific,such that in global imagery,there is a positive correlation between the clustering coefficient and reaction time,while in local imagery the average path length is positively correlated with the reaction time.This suggests that the efficiency of a functional brain network is task-dependent.%We investigate whether the small-world topology of a functional brain network means high information processing efficiency by calculating the correlation between the small-world measures of a functional brain network and behavioral reaction during an imagery task. Functional brain networks are constructed by multichannel event-related potential data, in which the electrodes are the nodes and the functional connectivities between them are the edges. The results show that the correlation between small-world measures and reaction time is task-specific, such that in global imagery, there is a positive correlation between the clustering coefficient and reaction time, while in local imagery the average path length is positively correlated with the reaction time. This suggests that the efficiency of a functional brain network is task-dependent.
Binary higher order neural networks for realizing Boolean functions.
Zhang, Chao; Yang, Jie; Wu, Wei
2011-05-01
In order to more efficiently realize Boolean functions by using neural networks, we propose a binary product-unit neural network (BPUNN) and a binary π-ς neural network (BPSNN). The network weights can be determined by one-step training. It is shown that the addition " σ," the multiplication " π," and two kinds of special weighting operations in BPUNN and BPSNN can implement the logical operators " ∨," " ∧," and " ¬" on Boolean algebra 〈Z(2),∨,∧,¬,0,1〉 (Z(2)={0,1}), respectively. The proposed two neural networks enjoy the following advantages over the existing networks: 1) for a complete truth table of N variables with both truth and false assignments, the corresponding Boolean function can be realized by accordingly choosing a BPUNN or a BPSNN such that at most 2(N-1) hidden nodes are needed, while O(2(N)), precisely 2(N) or at most 2(N), hidden nodes are needed by existing networks; 2) a new network BPUPS based on a collaboration of BPUNN and BPSNN can be defined to deal with incomplete truth tables, while the existing networks can only deal with complete truth tables; and 3) the values of the weights are all simply -1 or 1, while the weights of all the existing networks are real numbers. Supporting numerical experiments are provided as well. Finally, we present the risk bounds of BPUNN, BPSNN, and BPUPS, and then analyze their probably approximately correct learnability. PMID:21427020
Advanced Functionalities for Highly Reliable Optical Networks
DEFF Research Database (Denmark)
An, Yi
to increase the availability of highly reliable optical networks. A cost-effective transmitter based on a directly modulated laser (DML) using a silicon micro-ring resonator (MRR) to enhance its modulation speed is proposed, analysed and experimentally demonstrated. A modulation speed enhancement from 10 Gbit...
The Union of Shortest Path Trees of Functional Brain Networks
Meier, J.; Tewarie, P.; Van Mieghem, P.
2015-01-01
Communication between brain regions is still insufficiently understood. Applying concepts from network science has shown to be successful in gaining insight in the functioning of the brain. Recent work has implicated that especially shortest paths in the structural brain network seem to play a major
A network function-based definition of communities in complex networks
Chauhan, Sanjeev; Girvan, Michelle; Ott, Edward
2012-09-01
We consider an alternate definition of community structure that is functionally motivated. We define network community structure based on the function the network system is intended to perform. In particular, as a specific example of this approach, we consider communities whose function is enhanced by the ability to synchronize and/or by resilience to node failures. Previous work has shown that, in many cases, the largest eigenvalue of the network's adjacency matrix controls the onset of both synchronization and percolation processes. Thus, for networks whose functional performance is dependent on these processes, we propose a method that divides a given network into communities based on maximizing a function of the largest eigenvalues of the adjacency matrices of the resulting communities. We also explore the differences between the partitions obtained by our method and the modularity approach (which is based solely on consideration of network structure). We do this for several different classes of networks. We find that, in many cases, modularity-based partitions do almost as well as our function-based method in finding functional communities, even though modularity does not specifically incorporate consideration of function.
Joint Modelling of Structural and Functional Brain Networks
DEFF Research Database (Denmark)
Andersen, Kasper Winther; Herlau, Tue; Mørup, Morten;
Functional and structural magnetic resonance imaging have become the most important noninvasive windows to the human brain. A major challenge in the analysis of brain networks is to establish the similarities and dissimilarities between functional and structural connectivity. We formulate a non......-parametric Bayesian network model which allows for joint modelling and integration of multiple networks. We demonstrate the model’s ability to detect vertices that share structure across networks jointly in functional MRI (fMRI) and diffusion MRI (dMRI) data. Using two fMRI and dMRI scans per subject, we establish...... significant structures that are consistently shared across subjects and data splits. This provides an unsupervised approach for modeling of structure-function relations in the brain and provides a general framework for multimodal integration....
Structural and Functional Basis for Substrate Specificity and Catalysis of Levan Fructotransferase*
Park, Jinseo; Kim, Myung-Il; Park, Young-Don; Shin, Inchul; Cha, Jaeho; Kim, Chul Ho; Rhee, Sangkee
2012-01-01
Levan is β-2,6-linked polymeric fructose and serves as reserve carbohydrate in some plants and microorganisms. Mobilization of fructose is usually mediated by enzymes such as glycoside hydrolase (GH), typically releasing a monosaccharide as a product. The enzyme levan fructotransferase (LFTase) of the GH32 family catalyzes an intramolecular fructosyl transfer reaction and results in production of cyclic difructose dianhydride, thus exhibiting a novel substrate specificity. The mechanism by which LFTase carries out these functions via the structural fold conserved in the GH32 family is unknown. Here, we report the crystal structure of LFTase from Arthrobacter ureafaciens in apo form, as well as in complexes with sucrose and levanbiose, a difructosacchride with a β-2,6-glycosidic linkage. Despite the similarity of its two-domain structure to members of the GH32 family, LFTase contains an active site that accommodates a difructosaccharide using the −1 and −2 subsites. This feature is unique among GH32 proteins and is facilitated by small side chain residues in the loop region of a catalytic β-propeller N-domain, which is conserved in the LFTase family. An additional oligosaccharide-binding site was also characterized in the β-sandwich C-domain, supporting its role in carbohydrate recognition. Together with functional analysis, our data provide a molecular basis for the catalytic mechanism of LFTase and suggest functional variations from other GH32 family proteins, notwithstanding the conserved structural elements. PMID:22810228
Structural and functional basis for substrate specificity and catalysis of levan fructotransferase.
Park, Jinseo; Kim, Myung-Il; Park, Young-Don; Shin, Inchul; Cha, Jaeho; Kim, Chul Ho; Rhee, Sangkee
2012-09-01
Levan is β-2,6-linked polymeric fructose and serves as reserve carbohydrate in some plants and microorganisms. Mobilization of fructose is usually mediated by enzymes such as glycoside hydrolase (GH), typically releasing a monosaccharide as a product. The enzyme levan fructotransferase (LFTase) of the GH32 family catalyzes an intramolecular fructosyl transfer reaction and results in production of cyclic difructose dianhydride, thus exhibiting a novel substrate specificity. The mechanism by which LFTase carries out these functions via the structural fold conserved in the GH32 family is unknown. Here, we report the crystal structure of LFTase from Arthrobacter ureafaciens in apo form, as well as in complexes with sucrose and levanbiose, a difructosacchride with a β-2,6-glycosidic linkage. Despite the similarity of its two-domain structure to members of the GH32 family, LFTase contains an active site that accommodates a difructosaccharide using the -1 and -2 subsites. This feature is unique among GH32 proteins and is facilitated by small side chain residues in the loop region of a catalytic β-propeller N-domain, which is conserved in the LFTase family. An additional oligosaccharide-binding site was also characterized in the β-sandwich C-domain, supporting its role in carbohydrate recognition. Together with functional analysis, our data provide a molecular basis for the catalytic mechanism of LFTase and suggest functional variations from other GH32 family proteins, notwithstanding the conserved structural elements. PMID:22810228
Interpolation by Hankel translates of a basis function: inversion formulas and polynomial bounds.
Arteaga, Cristian; Marrero, Isabel
2014-01-01
For μ≥-1/2, the authors have developed elsewhere a scheme for interpolation by Hankel translates of a basis function Φ in certain spaces of continuous functions Yn(n∈ℕ) depending on a weight w. The functions Φ and w are connected through the distributional identity t4n(hμ'Φ)(t)=1/w(t), where hμ' denotes the generalized Hankel transform of order μ. In this paper, we use the projection operators associated with an appropriate direct sum decomposition of the Zemanian space ℋμ in order to derive explicit representations of the derivatives SμmΦ and their Hankel transforms, the former ones being valid when m∈ℤ+ is restricted to a suitable interval for which SμmΦ is continuous. Here, Sμm denotes the mth iterate of the Bessel differential operator Sμ if m∈ℕ, while Sμ0 is the identity operator. These formulas, which can be regarded as inverses of generalizations of the equation (hμ'Φ)(t)=1/t4nw(t), will allow us to get some polynomial bounds for such derivatives. Corresponding results are obtained for the members of the interpolation space Y n .
Genetic basis of cytokinin and auxin functions during root nodule development
Directory of Open Access Journals (Sweden)
Takuya eSuzaki
2013-03-01
Full Text Available The phytohormones cytokinin and auxin are essential for the control of diverse aspects of cell proliferation and differentiation processes in plants. Although both phytohormones have been suggested to play key roles in the regulation of root nodule development, only recently, significant progress has been made in the elucidation of the molecular genetic basis of cytokinin action in the model leguminous species, Lotus japonicus and Medicago truncatula. Identification and functional analyses of the putative cytokinin receptors LOTUS HISTIDINE KINASE 1 and M. truncatula CYTOKININ RESPONSE 1 have brought a greater understanding of how activation of cytokinin signaling is crucial to the initiation of nodule primordia. Recent studies have also started to shed light on the roles of auxin in the regulation of nodule development. Here, we review the history and recent progress of research into the roles of cytokinin and auxin, and their possible interactions, in nodule development.
Improved radial basis function methods for multi-dimensional option pricing
Pettersson, Ulrika; Larsson, Elisabeth; Marcusson, Gunnar; Persson, Jonas
2008-12-01
In this paper, we have derived a radial basis function (RBF) based method for the pricing of financial contracts by solving the Black-Scholes partial differential equation. As an example of a financial contract that can be priced with this method we have chosen the multi-dimensional European basket call option. We have shown numerically that our scheme is second-order accurate in time and spectrally accurate in space for constant shape parameter. For other non-optimal choices of shape parameter values, the resulting convergence rate is algebraic. We propose an adapted node point placement that improves the accuracy compared with a uniform distribution. Compared with an adaptive finite difference method, the RBF method is 20-40 times faster in one and two space dimensions and has approximately the same memory requirements.
Improved radial basis function approach with the odd-even corrections
Niu, Z M; Liang, H Z; Niu, Y F; Guo, J Y
2016-01-01
The radial basis function (RBF) approach has been used to improve the mass predictions of nuclear models. However, systematic deviations exist between the improved masses and the experimental data for nuclei with different odd-even parities of ($Z$, $N$), i.e., the (even $Z$, even $N$), (even $Z$, odd $N$), (odd $Z$, even $N$), and (odd $Z$, odd $N$). By separately training the RBF for these four different groups, it is found that the systematic odd-even deviations can be cured in a large extend and the predictive power of nuclear mass models can thus be further improved. Moreover, this new approach can better reproduce the single-nucleon separation energies. Based on the latest version of Weizs\\"acker-Skyrme model WS4, the root-mean-square deviation of the improved masses with respect to known data falls to $135$ keV, approaching the chaos-related unpredictability limit ($\\sim 100$ keV).
Basis Function Repetitive And Feedback Control With Application To A Particle Accelerator
Akogyeram, R A
2002-01-01
The thesis addresses three problem areas within repetitive control. Firstly, it addresses issues concerning the ability of repetitive control and feedback control systems to eliminate periodic disturbances occurring above the Nyquist frequency of the hardware. Methods are developed for decomposing and unfolding notch filter or comb filter feedback control so that disturbances above Nyquist frequency can be canceled. Phenomena affecting final error levels are discussed, including error in unfolding, coarseness of zero-order hold cancellation, and waterbed effects in the feedback control system frequency response for different sample rates. Secondly, matched basis function repetitive control laws are developed for batch mode and real time implementation to converge to zero tracking error in the presence of periodic disturbances. For both control methods, conditions are given that guarantee asymptotic and monotonic convergence. Stability tests are formulated to examine stability when the period of a disturbance ...
Radial Basis Function Based Implicit Surface Reconstruction Interpolating Arbitrary Triangular Mesh
Institute of Scientific and Technical Information of China (English)
PANG Mingyong
2006-01-01
In this paper, we present an approach for smooth surface reconstructions interpolating triangular meshes with arbitrary topology and geometry. The approach is based on the well-known radial basis functions (RBFs) and the constructed surfaces are generalized thin-plate spline surfaces. Our algorithm first defines a pair of offset points for each vertex of a given mesh to enhance the controllability of local geometry and to assure stability of the construction. A linear system is then solved by LU decomposition and the implicit governing equation of interpolating surface is obtained. The constructed surfaces finally are visualized by a Marching Cubes based polygonizer. The approach provides a robust and efficient solution for smooth surface reconstruction from various 3D meshes.
Cooper, Guy A.; Peterson, Randolph S.; Gruber, Ralf; Cooper, W. Anthony; Graves, Jonathan P.
2009-11-01
An incompressible variational ideal ballooning mode equation is discretized with the COOL finite element discretization scheme using basis functions composed of variable order Legendre polynomials.footnotetextG. A. Cooper, J. P. Graves, W. A. Cooper, R. Gruber and R. S. Peterson, J. Comput. Phys. 228 (2009) 4911-4916. This reduces the second order ordinary differential equation to a special block pentadiagonal matrix equation that is solved using an inverse vector iteration method. A benchmark test of BECOOL (Ballooning Eigensolver using COOL finite elements) with second order Legendre polynomials recovers precisely the eigenvalues computed by the VVBAL shooting code.footnotetextA. Cooper, Plasma Phys. Control. Fusion 34 (1992) 1011-1036. Timing runs reveal the need to determine an optimal lower order case. Eigenvalue convergence runs show that cubic Legendre polynomials construct the optimal ballooning mode equation for intensive computations.
Analysis of cornea curvature using radial basis functions - Part II: Fitting to data-set.
Griffiths, G W; Płociniczak, Ł; Schiesser, W E
2016-10-01
In part I we discussed the solution of corneal curvature using a 2D meshless method based on radial basis functions (RBFs). In Part II we use these methods to fit a full nonlinear thin membrane model to a measured data-set in order to generate a topological mathematical description of the cornea. In addition, we show how these results can lead to estimations for corneal radius of curvature and certain physical properties of the cornea; namely, tension and elasticity coefficient. Again all calculations and graphics generation were performed using the R language programming environment. The model describes corneal topology extremely well, and the estimated properties fall well within the expected range of values. The method is straight forward to implement and offers scope for further analysis using more detailed 3D models that include corneal thickness. PMID:27570056
SOME PROBLEMS WITH THE METHOD OF FUNDAMENTAL SOLUTION USING RADIAL BASIS FUNCTIONS
Institute of Scientific and Technical Information of China (English)
Wang Hui; Qin Qinghua
2007-01-01
The present work describes the application of the method of fundamental solutions (MFS) along with the analog equation method (AEM) and radial basis function (RBF) approximation for solving the 2D isotropic and anisotropic Helmholtz problems with different wave numbers.The AEM is used to convert the original governing equation into the classical Poisson's equation,and the MFS and RBF approximations are used to derive the homogeneous and particular solutions, respectively. Finally, the satisfaction of the solution consisting of the homogeneous and particular parts to the related governing equation and boundary conditions can produce a system of linear equations, which can be solved with the singular value decomposition (SVD) technique.In the computation, such crucial factors related to the MFS-RBF as the location of the virtual boundary, the differential and integrating strategies, and the variation of shape parameters in multi-quadric (MQ) are fully analyzed to provide useful reference.
Vibration measurement based on electronic speckle pattern interferometry and radial basis function
Dai, Xiangjun; Shao, Xinxing; Geng, Zhencen; Yang, Fujun; Jiang, Yijun; He, Xiaoyuan
2015-11-01
A method incorporating amplitude-fluctuation electronic speckle pattern interferometry (AF-ESPI) with radial basis function (RBF) was proposed to investigate vibration characteristics of structures. The vibration patterns were obtained by AF-ESPI. A novel pre-filtering RBF method was presented to improve the quality of patterns. The out-of-plane vibration amplitude was rebuilt after fringe analysis. Ideal pre-filtering widow sizes for the presented RBF were given based on numerical experiments. For validation, an aluminum circular plate with fixed boundary was determined and compared with FEM, confirming the effectiveness of the proposed method. Finally, vibration characteristics of sandwich panels with honeycomb core were measured. The influence of presence of a pre-notch at different location was also investigated.
The Function Biomedical Informatics Research Network Data Repository
Keator, DB; van Erp, TGM; Turner, JA; Glover, GH; Mueller, BA; Liu, TT; Voyvodic, JT; Rasmussen, J.; Calhoun, VD; Lee, HJ.; Toga, AW; McEwen, S.; Ford, JM; Mathalon, DH; Diaz, M
2016-01-01
© 2015 Elsevier Inc. The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associa...
Nodal centrality of functional network in the differentiation of schizophrenia.
Cheng, Hu; Newman, Sharlene; Goñi, Joaquín; Kent, Jerillyn S; Howell, Josselyn; Bolbecker, Amanda; Puce, Aina; O'Donnell, Brian F; Hetrick, William P
2015-10-01
A disturbance in the integration of information during mental processing has been implicated in schizophrenia, possibly due to faulty communication within and between brain regions. Graph theoretic measures allow quantification of functional brain networks. Functional networks are derived from correlations between time courses of brain regions. Group differences between SZ and control groups have been reported for functional network properties, but the potential of such measures to classify individual cases has been little explored. We tested whether the network measure of betweenness centrality could classify persons with schizophrenia and normal controls. Functional networks were constructed for 19 schizophrenic patients and 29 non-psychiatric controls based on resting state functional MRI scans. The betweenness centrality of each node, or fraction of shortest-paths that pass through it, was calculated in order to characterize the centrality of the different regions. The nodes with high betweenness centrality agreed well with hub nodes reported in previous studies of structural and functional networks. Using a linear support vector machine algorithm, the schizophrenia group was differentiated from non-psychiatric controls using the ten nodes with the highest betweenness centrality. The classification accuracy was around 80%, and stable against connectivity thresholding. Better performance was achieved when using the ranks as feature space as opposed to the actual values of betweenness centrality. Overall, our findings suggest that changes in functional hubs are associated with schizophrenia, reflecting a variation of the underlying functional network and neuronal communications. In addition, a specific network property, betweenness centrality, can classify persons with SZ with a high level of accuracy. PMID:26299706
Grimme, Stefan; Brandenburg, Jan Gerit; Bannwarth, Christoph; Hansen, Andreas
2015-08-01
A density functional theory (DFT) based composite electronic structure approach is proposed to efficiently compute structures and interaction energies in large chemical systems. It is based on the well-known and numerically robust Perdew-Burke-Ernzerhoff (PBE) generalized-gradient-approximation in a modified global hybrid functional with a relatively large amount of non-local Fock-exchange. The orbitals are expanded in Ahlrichs-type valence-double zeta atomic orbital (AO) Gaussian basis sets, which are available for many elements. In order to correct for the basis set superposition error (BSSE) and to account for the important long-range London dispersion effects, our well-established atom-pairwise potentials are used. In the design of the new method, particular attention has been paid to an accurate description of structural parameters in various covalent and non-covalent bonding situations as well as in periodic systems. Together with the recently proposed three-fold corrected (3c) Hartree-Fock method, the new composite scheme (termed PBEh-3c) represents the next member in a hierarchy of "low-cost" electronic structure approaches. They are mainly free of BSSE and account for most interactions in a physically sound and asymptotically correct manner. PBEh-3c yields good results for thermochemical properties in the huge GMTKN30 energy database. Furthermore, the method shows excellent performance for non-covalent interaction energies in small and large complexes. For evaluating its performance on equilibrium structures, a new compilation of standard test sets is suggested. These consist of small (light) molecules, partially flexible, medium-sized organic molecules, molecules comprising heavy main group elements, larger systems with long bonds, 3d-transition metal systems, non-covalently bound complexes (S22 and S66×8 sets), and peptide conformations. For these sets, overall deviations from accurate reference data are smaller than for various other tested DFT methods
Energy Technology Data Exchange (ETDEWEB)
Grimme, Stefan, E-mail: grimme@thch.uni-bonn.de; Brandenburg, Jan Gerit; Bannwarth, Christoph; Hansen, Andreas [Mulliken Center for Theoretical Chemistry, Institut für Physikalische und Theoretische Chemie, Rheinische Friedrich-Wilhelms Universität Bonn, Beringstraße 4, 53115 Bonn (Germany)
2015-08-07
A density functional theory (DFT) based composite electronic structure approach is proposed to efficiently compute structures and interaction energies in large chemical systems. It is based on the well-known and numerically robust Perdew-Burke-Ernzerhoff (PBE) generalized-gradient-approximation in a modified global hybrid functional with a relatively large amount of non-local Fock-exchange. The orbitals are expanded in Ahlrichs-type valence-double zeta atomic orbital (AO) Gaussian basis sets, which are available for many elements. In order to correct for the basis set superposition error (BSSE) and to account for the important long-range London dispersion effects, our well-established atom-pairwise potentials are used. In the design of the new method, particular attention has been paid to an accurate description of structural parameters in various covalent and non-covalent bonding situations as well as in periodic systems. Together with the recently proposed three-fold corrected (3c) Hartree-Fock method, the new composite scheme (termed PBEh-3c) represents the next member in a hierarchy of “low-cost” electronic structure approaches. They are mainly free of BSSE and account for most interactions in a physically sound and asymptotically correct manner. PBEh-3c yields good results for thermochemical properties in the huge GMTKN30 energy database. Furthermore, the method shows excellent performance for non-covalent interaction energies in small and large complexes. For evaluating its performance on equilibrium structures, a new compilation of standard test sets is suggested. These consist of small (light) molecules, partially flexible, medium-sized organic molecules, molecules comprising heavy main group elements, larger systems with long bonds, 3d-transition metal systems, non-covalently bound complexes (S22 and S66×8 sets), and peptide conformations. For these sets, overall deviations from accurate reference data are smaller than for various other tested DFT
Selectively disrupted functional connectivity networks in type 2 diabetes mellitus
Directory of Open Access Journals (Sweden)
Yaojing eChen
2015-12-01
Full Text Available Background: The high prevalence of type 2 diabetes mellitus (T2DM in individuals over 65 years old and cognitive deficits caused by T2DM have attracted broad attention. The pathophysiological mechanism of T2DM induced cognitive impairments, however, remains poorly understood. Previous studies have suggested that the cognitive impairments can be attributed not merely to local functional and structural abnormalities but also to specific brain networks. Thus, we aimed to investigate the changes of global networks selectively affected by T2DM. Methods: A resting state functional network analysis was conducted to investigate the intrinsic functional connectivity in 37 patients with diabetes and 40 healthy controls which were recruited from local communities in Beijing, China. Results: We found that patients with T2DM exhibited cognitive function declines and functional connectivity disruptions within the default mode network, left frontal parietal network, and sensorimotor network. More importantly, the fasting glucose level was correlated with abnormal functional connectivity.Conclusions: These findings could help to understand the neural mechanisms of cognitive impairments in T2DM and provide potential neuroimaging biomarkers that may be used for early diagnosis and intervention in cognitive decline.
Functional brain networks associated with eating behaviors in obesity.
Park, Bo-Yong; Seo, Jongbum; Park, Hyunjin
2016-01-01
Obesity causes critical health problems including diabetes and hypertension that affect billions of people worldwide. Obesity and eating behaviors are believed to be closely linked but their relationship through brain networks has not been fully explored. We identified functional brain networks associated with obesity and examined how the networks were related to eating behaviors. Resting state functional magnetic resonance imaging (MRI) scans were obtained for 82 participants. Data were from an equal number of people of healthy weight (HW) and non-healthy weight (non-HW). Connectivity matrices were computed with spatial maps derived using a group independent component analysis approach. Brain networks and associated connectivity parameters with significant group-wise differences were identified and correlated with scores on a three-factor eating questionnaire (TFEQ) describing restraint, disinhibition, and hunger eating behaviors. Frontoparietal and cerebellum networks showed group-wise differences between HW and non-HW groups. Frontoparietal network showed a high correlation with TFEQ disinhibition scores. Both frontoparietal and cerebellum networks showed a high correlation with body mass index (BMI) scores. Brain networks with significant group-wise differences between HW and non-HW groups were identified. Parts of the identified networks showed a high correlation with eating behavior scores. PMID:27030024
Visualizing and Clustering Protein Similarity Networks: Sequences, Structures, and Functions.
Mai, Te-Lun; Hu, Geng-Ming; Chen, Chi-Ming
2016-07-01
Research in the recent decade has demonstrated the usefulness of protein network knowledge in furthering the study of molecular evolution of proteins, understanding the robustness of cells to perturbation, and annotating new protein functions. In this study, we aimed to provide a general clustering approach to visualize the sequence-structure-function relationship of protein networks, and investigate possible causes for inconsistency in the protein classifications based on sequences, structures, and functions. Such visualization of protein networks could facilitate our understanding of the overall relationship among proteins and help researchers comprehend various protein databases. As a demonstration, we clustered 1437 enzymes by their sequences and structures using the minimum span clustering (MSC) method. The general structure of this protein network was delineated at two clustering resolutions, and the second level MSC clustering was found to be highly similar to existing enzyme classifications. The clustering of these enzymes based on sequence, structure, and function information is consistent with each other. For proteases, the Jaccard's similarity coefficient is 0.86 between sequence and function classifications, 0.82 between sequence and structure classifications, and 0.78 between structure and function classifications. From our clustering results, we discussed possible examples of divergent evolution and convergent evolution of enzymes. Our clustering approach provides a panoramic view of the sequence-structure-function network of proteins, helps visualize the relation between related proteins intuitively, and is useful in predicting the structure and function of newly determined protein sequences. PMID:27267620
Cornew, R W; Morse, P M
1975-08-15
After 4 years of operation the NERComP network is now a self-supporting success. Some of the reasons for its success are that (i) the network started small and built up utilization; (ii) the members, through monthly trustee meetings, practiced "participatory management" from the outset; (iii) unlike some networks, NERComP appealed to individual academic and research users who were terminal-oriented and who controlled their own budgets; (iv) the compactness of the New England region made it an ideal laboratory for testing networking concepts; and (v) a dedicated staff was willing to work hard in the face of considerable uncertainty. While the major problems were "political, organizational and economic" (1) we have found that they can be solved if the network meets real needs. We have also found that it is difficult to proceed beyond a certain point without investing responsibility and authority in the networking organization. Conversely, there is a need to distribute some responsibilities such as marketing and user services back to the member institutions. By adopting a modest starting point and achieving limited goals the necessary trust and working relationships between institutions can be built. In our case the necessary planning has been facilitated by recognizing three distinct network functions: governance, user services, and technical operations. Separating out the three essential networing tasks and dealing with each individually through advisory committees, each with its own staff coordinator, has overcome a distracting tendency to address all issues at once. It has also provided an element of feedback between the end user and the supplier not usually present in networking activity. The success of NERComP demonstrates that a distributive-type network can work. Our experiences in New England-which, because of its numerous colleges and universities free from domination by any single institution, is a microcosm for academic computing in the United States
Density-dependence of functional spiking networks in vitro
Energy Technology Data Exchange (ETDEWEB)
Ham, Michael I [Los Alamos National Laboratory; Gintautuas, Vadas [Los Alamos National Laboratory; Rodriguez, Marko A [Los Alamos National Laboratory; Bettencourt, Luis M A [Los Alamos National Laboratory; Bennett, Ryan [UNIV OF NORTH TEXAS; Santa Maria, Cara L [UNIV OF NORTH TEXAS
2008-01-01
During development, the mammalian brain differentiates into specialized regions with unique functional abilities. While many factors contribute to this functional specialization, we explore the effect neuronal density can have on neuronal interactions. Two types of networks, dense (50,000 neurons and glia support cells) and sparse (12,000 neurons and glia support cells), are studied. A competitive first response model is applied to construct activation graphs that represent pairwise neuronal interactions. By observing the evolution of these graphs during development in vitro we observe that dense networks form activation connections earlier than sparse networks, and that link-!llltropy analysis of the resulting dense activation graphs reveals that balanced directional connections dominate. Information theoretic measures reveal in addition that early functional information interactions (of order 3) are synergetic in both dense and sparse networks. However, during development in vitro, such interactions become redundant in dense, but not sparse networks. Large values of activation graph link-entropy correlate strongly with redundant ensembles observed in the dense networks. Results demonstrate differences between dense and sparse networks in terms of informational groups, pairwise relationships, and activation graphs. These differences suggest that variations in cell density may result in different functional specialization of nervous system tissue also in vivo.
A framework for interpreting functional networks in schizophrenia
Directory of Open Access Journals (Sweden)
Peter eWilliamson
2012-06-01
Full Text Available Some promising genetic correlates of schizophrenia have emerged in recent years but none explain more than a small fraction of cases. The challenge of our time is to characterize the neuronal networks underlying schizophrenia and other neuropsychiatric illnesses. It has been proposed that schizophrenia arises from a uniquely human brain network associated with directed effort including the dorsal anterior and posterior cingulate cortex, auditory cortex, and hippocampus and while mood disorders arise from a different brain network associated with emotional encoding including the ventral anterior cingulate cortex, orbital frontal cortex, and amygdala. Both interact with a representation network including the frontal and temporal poles and the fronto-insular cortex, allowing the representation of the thoughts, feelings and actions of self and others. This paper reviews recent morphological and functional literature in light of the proposed networks underlying these disorders. It is suggested that there is considerable support for the involvement of the directed effort network in schizophrenia from studies of brain structure with voxel-based morphometry (VBM and diffusion tensor imaging (DTI. While early studies of resting brain networks are inconclusive, functional magnetic resonance imaging imaging (fMRI studies of task-related networks clearly implicate these regions. In keeping with the model, functional deficits in regions associated with directed effort and self-monitoring are associated with structural anomalies in action-related regions in schizophrenic patients. VBM, DTI, fMRI studies of mood disordered patients support the involvement of a different network associated with emotional encoding. The distinction between disorders is enhanced by combining structural and functional data. It is concluded that brain networks associated with directed effort are particularly vulnerable to failure in the human brain leading to the symptoms of
SYNERGY IN DEMAND MANAGEMENT ON THE EDUCATIONAL SERVICES MARKET ON THE BASIS OF EDUCATION NETWORKS
Korenkova Natalia Anatolyevna
2012-01-01
Purpose: to review the approach to social technology of demand management on educational services on the basis of synergetic approach allowing due to selforganizing processes to provide balance of actors interests in the market of educational services. Methodology: For the analysis of the basic synergetic approach properties applicable to management of social structures, the system approach within the paradigm of social behavior, as well as methods of abstraction, analysis, synthesis and mode...
Assortative mixing in functional brain networks during epileptic seizures
Bialonski, Stephan
2013-01-01
We investigate assortativity of functional brain networks before, during, and after one-hundred epileptic seizures with different anatomical onset locations. We construct binary functional networks from multi-channel electroencephalographic data recorded from 60 epilepsy patients, and from time-resolved estimates of the assortativity coefficient we conclude that positive degree-degree correlations are inherent to seizure dynamics. While seizures evolve, an increasing assortativity indicates a segregation of the underlying functional network into groups of brain regions that are only sparsely interconnected, if at all. Interestingly, assortativity decreases already prior to seizure end. Together with previous observations of characteristic temporal evolutions of global statistical properties and synchronizability of epileptic brain networks, our findings may help to gain deeper insights into the complicated dynamics underlying generation, propagation, and termination of seizures.
INFRAFRONTIER: a European resource for studying the functional basis of human disease.
Raess, Michael; de Castro, Ana Ambrosio; Gailus-Durner, Valérie; Fessele, Sabine; Hrabě de Angelis, Martin
2016-08-01
Ageing research and more generally the study of the functional basis of human diseases profit enormously from the large-scale approaches and resources in mouse functional genomics: systematic targeted mutation of the mouse genome, systemic phenotyping in mouse clinics, and the archiving and distribution of the mouse resources in public repositories. INFRAFRONTIER, the European research infrastructure for the development, systemic phenotyping, archiving and distribution of mammalian models, offers access to sustainable mouse resources for biomedical research. INFRAFRONTIER promotes the global sharing of high-quality resources and data and thus contributes to data reproducibility and animal welfare. INFRAFRONTIER puts great effort into international standardisation and quality control and into technology development to improve and expand experimental protocols, reduce the use of animals in research and increase the reproducibility of results. In concert with the research community and the International Mouse Phenotyping Consortium (IMPC), INFRAFRONTIER is currently developing new pilot platforms and services for the research on ageing and age-related diseases. PMID:27262858
Barka, André; Picard, Clément
2008-03-01
In this paper, we discuss several improvements of a substructuring Domain Decomposition Method (DDM) devoted to Electromagnetic computations, based on the Boundary Element Method (BEM) and the Finite Element Method (FEM). This computation procedure is applied to the analysis of antenna performance on board vehicles as well as Radar Cross Section (RCS). The benefits of the subdomain Computational Electromagnetic Method are mainly the ability to deal with collaborative studies involving several companies, and the reduction of the computation costs by one or more orders of magnitude, especially in the context of parametric studies. Furthermore, this paper proposes a Spectral Basis Function (SBF) defined on fictitious surfaces surrounding equipment, to deal with both the computation of antenna far field patterns and RCS in a multi-domain mode. By masking the complexity of the equipment (wires, thin surfaces, materials, supply network, weapons) the external domain of the vehicle can be closed so that the Combined Field Integral Equation (CFIE) can be used, which is better conditioned than the Electric Field Integral Equation (EFIE). This calculation procedure leads to a faster convergence when using iterative Multi Level Fast Multiple Algorithms (MLFMA). The accuracy and efficiency of this technique is assessed by performing the computation of the diffraction and radiation of several test-objects in a multi-domain way cross compared with reference integral equation results.
Uncovering Biological Network Function via Graphlet Degree Signatures
Directory of Open Access Journals (Sweden)
Nataša Pržulj
2008-01-01
Full Text Available Motivation: Proteins are essential macromolecules of life and thus understanding their function is of great importance. The number of functionally unclassiﬁed proteins is large even for simple and well studied organisms such as baker’s yeast. Methods for determining protein function have shifted their focus from targeting speciﬁc proteins based solely on sequence homology to analyses of the entire proteome based on protein-protein interaction (PPI networks. Since proteins interact to perform a certain function, analyzing structural properties of PPI networks may provide useful clues about the biological function of individual proteins, protein complexes they participate in, and even larger subcellular machines.Results: We design a sensitive graph theoretic method for comparing local structures of node neighborhoods that demonstrates that in PPI networks, biological function of a node and its local network structure are closely related. The method summarizes a protein’s local topology in a PPI network into the vector of graphlet degrees called the signature of the protein and computes the signature similarities between all protein pairs. We group topologically similar proteins under this measure in a PPI network and show that these protein groups belong to the same protein complexes, perform the same biological functions, are localized in the same subcellular compartments, and have the same tissue expressions. Moreover, we apply our technique on a proteome-scale network data and infer biological function of yet unclassiﬁed proteins demonstrating that our method can provide valuable guidelines for future experimental research such as disease protein prediction.Availability: Data is available upon request.
Directory of Open Access Journals (Sweden)
Delong Zhang
Full Text Available Many studies have demonstrated that the pathophysiology and clinical symptoms of Parkinson's disease (PD are inhomogeneous. However, the symptom-specific intrinsic neural activities underlying the PD subtypes are still not well understood. Here, 15 tremor-dominant PD patients, 10 non-tremor-dominant PD patients, and 20 matched normal controls (NCs were recruited and underwent resting-state functional magnetic resonance imaging (fMRI. Functional brain networks were constructed based on randomly generated anatomical templates with and without the cerebellum. The regional network efficiencies (i.e., the local and global efficiencies were further measured and used to distinguish subgroups of PD patients (i.e., with tremor-dominant PD and non-tremor-dominant PD from the NCs using linear discriminant analysis. The results demonstrate that the subtype-specific functional networks were small-world-organized and that the network regional efficiency could discriminate among the individual PD subgroups and the NCs. Brain regions involved in distinguishing between the study groups included the basal ganglia (i.e., the caudate and putamen, limbic regions (i.e., the hippocampus and thalamus, the cerebellum, and other cerebral regions (e.g., the insula, cingulum, and calcarine sulcus. In particular, the performances of the regional local efficiency in the functional network were better than those of the global efficiency, and the performances of global efficiency were dependent on the inclusion of the cerebellum in the analysis. These findings provide new evidence for the neurological basis of differences between PD subtypes and suggest that the cerebellum may play different roles in the pathologies of different PD subtypes. The present study demonstrated the power of the combination of graph-based network analysis and discrimination analysis in elucidating the neural basis of different PD subtypes.
Mapping Multiplex Hubs in Human Functional Brain Networks.
De Domenico, Manlio; Sasai, Shuntaro; Arenas, Alex
2016-01-01
Typical brain networks consist of many peripheral regions and a few highly central ones, i.e., hubs, playing key functional roles in cerebral inter-regional interactions. Studies have shown that networks, obtained from the analysis of specific frequency components of brain activity, present peculiar architectures with unique profiles of region centrality. However, the identification of hubs in networks built from different frequency bands simultaneously is still a challenging problem, remaining largely unexplored. Here we identify each frequency component with one layer of a multiplex network and face this challenge by exploiting the recent advances in the analysis of multiplex topologies. First, we show that each frequency band carries unique topological information, fundamental to accurately model brain functional networks. We then demonstrate that hubs in the multiplex network, in general different from those ones obtained after discarding or aggregating the measured signals as usual, provide a more accurate map of brain's most important functional regions, allowing to distinguish between healthy and schizophrenic populations better than conventional network approaches. PMID:27471443
EEG-based research on brain functional networks in cognition.
Wang, Niannian; Zhang, Li; Liu, Guozhong
2015-01-01
Recently, exploring the cognitive functions of the brain by establishing a network model to understand the working mechanism of the brain has become a popular research topic in the field of neuroscience. In this study, electroencephalography (EEG) was used to collect data from subjects given four different mathematical cognitive tasks: recite numbers clockwise and counter-clockwise, and letters clockwise and counter-clockwise to build a complex brain function network (BFN). By studying the connectivity features and parameters of those brain functional networks, it was found that the average clustering coefficient is much larger than its corresponding random network and the average shortest path length is similar to the corresponding random networks, which clearly shows the characteristics of the small-world network. The brain regions stimulated during the experiment are consistent with traditional cognitive science regarding learning, memory, comprehension, and other rational judgment results. The new method of complex networking involves studying the mathematical cognitive process of reciting, providing an effective research foundation for exploring the relationship between brain cognition and human learning skills and memory. This could help detect memory deficits early in young and mentally handicapped children, and help scientists understand the causes of cognitive brain disorders. PMID:26405867
Development of large-scale functional brain networks in children.
Directory of Open Access Journals (Sweden)
Kaustubh Supekar
2009-07-01
Full Text Available The ontogeny of large-scale functional organization of the human brain is not well understood. Here we use network analysis of intrinsic functional connectivity to characterize the organization of brain networks in 23 children (ages 7-9 y and 22 young-adults (ages 19-22 y. Comparison of network properties, including path-length, clustering-coefficient, hierarchy, and regional connectivity, revealed that although children and young-adults' brains have similar "small-world" organization at the global level, they differ significantly in hierarchical organization and interregional connectivity. We found that subcortical areas were more strongly connected with primary sensory, association, and paralimbic areas in children, whereas young-adults showed stronger cortico-cortical connectivity between paralimbic, limbic, and association areas. Further, combined analysis of functional connectivity with wiring distance measures derived from white-matter fiber tracking revealed that the development of large-scale brain networks is characterized by weakening of short-range functional connectivity and strengthening of long-range functional connectivity. Importantly, our findings show that the dynamic process of over-connectivity followed by pruning, which rewires connectivity at the neuronal level, also operates at the systems level, helping to reconfigure and rebalance subcortical and paralimbic connectivity in the developing brain. Our study demonstrates the usefulness of network analysis of brain connectivity to elucidate key principles underlying functional brain maturation, paving the way for novel studies of disrupted brain connectivity in neurodevelopmental disorders such as autism.
Development of large-scale functional brain networks in children.
Supekar, Kaustubh; Musen, Mark; Menon, Vinod
2009-07-01
The ontogeny of large-scale functional organization of the human brain is not well understood. Here we use network analysis of intrinsic functional connectivity to characterize the organization of brain networks in 23 children (ages 7-9 y) and 22 young-adults (ages 19-22 y). Comparison of network properties, including path-length, clustering-coefficient, hierarchy, and regional connectivity, revealed that although children and young-adults' brains have similar "small-world" organization at the global level, they differ significantly in hierarchical organization and interregional connectivity. We found that subcortical areas were more strongly connected with primary sensory, association, and paralimbic areas in children, whereas young-adults showed stronger cortico-cortical connectivity between paralimbic, limbic, and association areas. Further, combined analysis of functional connectivity with wiring distance measures derived from white-matter fiber tracking revealed that the development of large-scale brain networks is characterized by weakening of short-range functional connectivity and strengthening of long-range functional connectivity. Importantly, our findings show that the dynamic process of over-connectivity followed by pruning, which rewires connectivity at the neuronal level, also operates at the systems level, helping to reconfigure and rebalance subcortical and paralimbic connectivity in the developing brain. Our study demonstrates the usefulness of network analysis of brain connectivity to elucidate key principles underlying functional brain maturation, paving the way for novel studies of disrupted brain connectivity in neurodevelopmental disorders such as autism. PMID:19621066
Reorganization of functional networks in mild cognitive impairment.
Directory of Open Access Journals (Sweden)
Javier M Buldú
Full Text Available Whether the balance between integration and segregation of information in the brain is damaged in Mild Cognitive Impairment (MCI subjects is still a matter of debate. Here we characterize the functional network architecture of MCI subjects by means of complex networks analysis. Magnetoencephalograms (MEG time series obtained during a memory task were evaluated by synchronization likelihood (SL, to quantify the statistical dependence between MEG signals and to obtain the functional networks. Graphs from MCI subjects show an enhancement of the strength of connections, together with an increase in the outreach parameter, suggesting that memory processing in MCI subjects is associated with higher energy expenditure and a tendency toward random structure, which breaks the balance between integration and segregation. All features are reproduced by an evolutionary network model that simulates the degenerative process of a healthy functional network to that associated with MCI. Due to the high rate of conversion from MCI to Alzheimer Disease (AD, these results show that the analysis of functional networks could be an appropriate tool for the early detection of both MCI and AD.
Functional-Friction Networks: New Insights on the Laboratory Earthquakes
Ghaffari, H O
2013-01-01
We formulate the universality of regular precursor rupture fronts in functional network parameter space, in light of recent analysis of acoustics emissions-coupled friction experimental results. Furthermore, using a phenomenological approach based on friction networks, we propose that the energy of the ruptures can be extended in terms of networks motifs and the transition from regular rupture to slow deformation can have a third production from the critical rupture class, comparable with the direct observations of this phenomena in the transparent samples . Based on this model, the transition from slow ruptures (i.e., creep pulse) to the critical speeds of ruptures is possible. In addition, the evolution of arrested rupture fronts is inspected through a statistical-network modelling which sheds light on the communities evolution. We propose a phase diagram for the friction networks which depends on the scaling coefficients of scalar parameters and can show a transition towards the capturing of the links by a...
Mizera, Mikołaj; Lewadowska, Kornelia; Talaczyńska, Alicja; Cielecka-Piontek, Judyta
2015-02-01
The work was aimed at investigating the influence of diffusion of basis functions on the geometry optimization of molecule of losartan in acidic and salt form. Spectroscopic properties of losartan potassium were also calculated and compared with experiment. Density functional theory method with various basis sets: 6-31G(d,p) and its diffused variations 6-31G(d,p)+ and 6-31G(d,p)++ was used. Application of diffuse basis functions in geometry optimization resulted in significant change of total molecule energy. Total molecule energy of losartan potassium decreased by 112.91 kJ/mol and 114.32 kJ/mol for 6-31G(d,p)+ and 6-31G(d,p)++ basis sets, respectively. Almost the same decrease was observed for losartan: 114.99 kJ/mol and 117.08 kJ/mol respectively for 6-31G(d,p)+ and 6-31G(d,p)++ basis sets. Further investigation showed significant difference within geometries of losartan potassium optimized with investigated basis sets. Application of diffused basis functions resulted in average 1.29 Å difference in relative position between corresponding atoms of three obtained geometries. Similar study taken on losartan resulted in only average 0.22 Å of dislocation. Extensive analysis of geometry changes in molecules obtained with diffused and non-diffuse basis functions was carried out in order to elucidate observed changes. The analysis was supported by electrostatic potential maps and calculation of natural atomic charges. UV, FT-IR and Raman spectra of losartan potassium were calculated and compared with experimental results. No crucial differences between Raman spectra obtained with different basis sets were observed. However, FT-IR spectra of geometry of losartan potassium optimized with 6-31G(d,p)++ basis set resulted in 40% better correlation with experimental FT-IR spectra than FT-IR calculated with geometry optimized with 6-31G(d,p) basis set. Therefore, it is highly advisable to optimize geometry of molecules with ionic interactions using diffuse basis functions
McClements, David Julian; Gumus, Cansu Ekin
2016-08-01
There is increasing consumer pressure for commercial products that are more natural, sustainable, and environmentally friendly, including foods, cosmetics, detergents, and personal care products. Industry has responded by trying to identify natural alternatives to synthetic functional ingredients within these products. The focus of this review article is on the replacement of synthetic surfactants with natural emulsifiers, such as amphiphilic proteins, polysaccharides, biosurfactants, phospholipids, and bioparticles. In particular, the physicochemical basis of emulsion formation and stabilization by natural emulsifiers is discussed, and the benefits and limitations of different natural emulsifiers are compared. Surface-active polysaccharides typically have to be used at relatively high levels to produce small droplets, but the droplets formed are highly resistant to environmental changes. Conversely, surface-active proteins are typically utilized at low levels, but the droplets formed are highly sensitive to changes in pH, ionic strength, and temperature. Certain phospholipids are capable of producing small oil droplets during homogenization, but again the droplets formed are highly sensitive to changes in environmental conditions. Biosurfactants (saponins) can be utilized at low levels to form fine oil droplets that remain stable over a range of environmental conditions. Some nature-derived nanoparticles (e.g., cellulose, chitosan, and starch) are effective at stabilizing emulsions containing relatively large oil droplets. Future research is encouraged to identify, isolate, purify, and characterize new types of natural emulsifier, and to test their efficacy in food, cosmetic, detergent, personal care, and other products. PMID:27181392
Mass predictions of the relativistic mean-field model with the radial basis function approach
Zheng, J. S.; Wang, N. Y.; Wang, Z. Y.; Niu, Z. M.; Niu, Y. F.; Sun, B.
2014-07-01
The radial basis function (RBF) is a powerful tool to improve mass predictions of nuclear models. By combining the RBF approach with the relativistic mean-field (RMF) model, the systematic deviations between mass predictions of the RMF model and the experimental data are eliminated to a large extent and the resulting rms deviation is reduced from 2.217 to 0.488 MeV. Furthermore, it is found that the RBF approach has a relatively reliable extrapolative power along the distance from the β-stability line except for a large uncertainty around the region at magic number. From the deduced neutron separation energies, we found that the description of the nuclear shell structure and shape transition is also significantly improved by the RBF approach, thus improving agreement with the solar r-process abundances before A =130 and speeding up the r-matter flow. Therefore, a shorter irradiation time is enough to reproduce the solar r-process abundance distribution for the improved RMF mass model, which is closer to the irradiation time for those sophisticated mass models.
Radial Basis Function-Sparse Partial Least Squares for Application to Brain Imaging Data
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Hisako Yoshida
2013-01-01
Full Text Available Magnetic resonance imaging (MRI data is an invaluable tool in brain morphology research. Here, we propose a novel statistical method for investigating the relationship between clinical characteristics and brain morphology based on three-dimensional MRI data via radial basis function-sparse partial least squares (RBF-sPLS. Our data consisted of MRI image intensities for multimillion voxels in a 3D array along with 73 clinical variables. This dataset represents a suitable application of RBF-sPLS because of a potential correlation among voxels as well as among clinical characteristics. Additionally, this method can simultaneously select both effective brain regions and clinical characteristics based on sparse modeling. This is in contrast to existing methods, which consider prespecified brain regions because of the computational difficulties involved in processing high-dimensional data. RBF-sPLS employs dimensionality reduction in order to overcome this obstacle. We have applied RBF-sPLS to a real dataset composed of 102 chronic kidney disease patients, while a comparison study used a simulated dataset. RBF-sPLS identified two brain regions of interest from our patient data: the temporal lobe and the occipital lobe, which are associated with aging and anemia, respectively. Our simulation study suggested that such brain regions are extracted with excellent accuracy using our method.
An, Yu; Liu, Jie; Zhang, Guanglei; Ye, Jinzuo; Mao, Yamin; Jiang, Shixin; Shang, Wenting; Du, Yang; Chi, Chongwei; Tian, Jie
2015-10-01
Fluorescence molecular tomography (FMT) is a promising tool in the study of cancer, drug discovery, and disease diagnosis, enabling noninvasive and quantitative imaging of the biodistribution of fluorophores in deep tissues via image reconstruction techniques. Conventional reconstruction methods based on the finite-element method (FEM) have achieved acceptable stability and efficiency. However, some inherent shortcomings in FEM meshes, such as time consumption in mesh generation and a large discretization error, limit further biomedical application. In this paper, we propose a meshless method for reconstruction of FMT (MM-FMT) using compactly supported radial basis functions (CSRBFs). With CSRBFs, the image domain can be accurately expressed by continuous CSRBFs, avoiding the discretization error to a certain degree. After direct collocation with CSRBFs, the conventional optimization techniques, including Tikhonov, L1-norm iteration shrinkage (L1-IS), and sparsity adaptive matching pursuit, were adopted to solve the meshless reconstruction. To evaluate the performance of the proposed MM-FMT, we performed numerical heterogeneous mouse experiments and in vivo bead-implanted mouse experiments. The results suggest that the proposed MM-FMT method can reduce the position error of the reconstruction result to smaller than 0.4 mm for the double-source case, which is a significant improvement for FMT.
Wang, Zhiheng
2014-12-10
A meshless local radial basis function method is developed for two-dimensional incompressible Navier-Stokes equations. The distributed nodes used to store the variables are obtained by the philosophy of an unstructured mesh, which results in two main advantages of the method. One is that the unstructured nodes generation in the computational domain is quite simple, without much concern about the mesh quality; the other is that the localization of the obtained collocations for the discretization of equations is performed conveniently with the supporting nodes. The algebraic system is solved by a semi-implicit pseudo-time method, in which the convective and source terms are explicitly marched by the Runge-Kutta method, and the diffusive terms are implicitly solved. The proposed method is validated by several benchmark problems, including natural convection in a square cavity, the lid-driven cavity flow, and the natural convection in a square cavity containing a circular cylinder, and very good agreement with the existing results are obtained.
Enhancing finite differences with radial basis functions: Experiments on the Navier-Stokes equations
Flyer, Natasha; Barnett, Gregory A.; Wicker, Louis J.
2016-07-01
Polynomials are used together with polyharmonic spline (PHS) radial basis functions (RBFs) to create local RBF-finite-difference (RBF-FD) weights on different node layouts for spatial discretizations that can be viewed as enhancements of the classical finite differences (FD). The presented method replicates the convergence properties of FD but for arbitrary node layouts. It is tested on the 2D compressible Navier-Stokes equations at low Mach number, relevant to atmospheric flows. Test cases are taken from the numerical weather prediction community and solved on bounded domains. Thus, attention is given on how to handle boundaries with the RBF-FD method, as well as a novel implementation for hyperviscosity. Comparisons are done on Cartesian, hexagonal, and quasi-uniform node layouts. Consideration and guidelines are given on PHS order, polynomial degree and stencil size. The main advantages of the present method are: 1) capturing the basic physics of the problem surprisingly well, even at very coarse resolutions, 2) high-order accuracy without the need of tuning a shape parameter, and 3) the inclusion of polynomials eliminates stagnation (saturation) errors. A MATLAB code is given to calculate the differentiation weights for this novel approach.
McClements, David Julian; Gumus, Cansu Ekin
2016-08-01
There is increasing consumer pressure for commercial products that are more natural, sustainable, and environmentally friendly, including foods, cosmetics, detergents, and personal care products. Industry has responded by trying to identify natural alternatives to synthetic functional ingredients within these products. The focus of this review article is on the replacement of synthetic surfactants with natural emulsifiers, such as amphiphilic proteins, polysaccharides, biosurfactants, phospholipids, and bioparticles. In particular, the physicochemical basis of emulsion formation and stabilization by natural emulsifiers is discussed, and the benefits and limitations of different natural emulsifiers are compared. Surface-active polysaccharides typically have to be used at relatively high levels to produce small droplets, but the droplets formed are highly resistant to environmental changes. Conversely, surface-active proteins are typically utilized at low levels, but the droplets formed are highly sensitive to changes in pH, ionic strength, and temperature. Certain phospholipids are capable of producing small oil droplets during homogenization, but again the droplets formed are highly sensitive to changes in environmental conditions. Biosurfactants (saponins) can be utilized at low levels to form fine oil droplets that remain stable over a range of environmental conditions. Some nature-derived nanoparticles (e.g., cellulose, chitosan, and starch) are effective at stabilizing emulsions containing relatively large oil droplets. Future research is encouraged to identify, isolate, purify, and characterize new types of natural emulsifier, and to test their efficacy in food, cosmetic, detergent, personal care, and other products.
Analysis of cornea curvature using radial basis functions - Part I: Methodology.
Griffiths, G W; Płociniczak, Ł; Schiesser, W E
2016-10-01
We discuss the solution of cornea curvature using a meshless method based on radial basis functions (RBFs). A full two-dimensional nonlinear thin membrane partial differential equation (PDE) model is introduced and solved using the multiquadratic (MQ) and inverse multiquadratic (IMQ) RBFs. This new approach does not rely on radial symmetry or other simplifying assumptions in respect of the cornea shape. It also provides an alternative to corneal topography modeling methods requiring accurate material parameter values, such as Young's modulus and Poisson ratio, that may not be available. The results show good agreement with published corneal data and allow back calculations for estimating certain physical properties of the cornea, such as tension and elasticity coefficient. All calculations and generation of graphics were performed using the R language programming environment [34] and RStudio, the integrated development environment (IDE) for R [36], both of which are open source and free to download. Part II [48] of this paper demonstrates how the method has been used to provide a very accurate fit to a corneal measured data set. PMID:27614697
Unification of Plasma Fluid and Kinetic Theory via Gaussian Radial Basis Functions
Candy, J. M.
2015-11-01
A fundamental macroscopic description of a magnetized plasma is the Vlasov equation supplemented by the nonlinear inverse-square force Fokker-Planck collision operator [Rosenbluth et al., Phys. Rev. 107, 1957]. The Vlasov part describes advection in a six-dimensional phase space whereas the collision operator contains friction and diffusion coefficients that are weighted velocity-space integrals of the particle distribution function. The Fokker-Planck collision operator is an integro-differential, nonlinear (bilinear) operator. Numerical discretization of the operator, in particular for collisions of unlike species, is extremely challenging. In this work, we describe a new approach to discretize the entire kinetic system based on an expansion in Gaussian Radial Basis functions (RBFs). This approach is particularly well-suited to treat the collision operator because the friction and diffusion coefficients can be analytically calculated. Although the RBF method is known to be a powerful scheme for the interpolation of scattered multidimensional data, Gaussian RBFs also have a deep physical interpretation in statistical mechanics and plasma physics as local thermodynamic equilibria. We outline the general theory, highlight the connection to plasma fluid theories, and also give 2D and 3D numerical solutions of the nonlinear Fokker-Planck equation. A broad spectrum of applications for the new method is anticipated in both astrophysical and laboratory plasmas. In particular, we believe that the RBF method may provide a new bridge between fluid and kinetic descriptions of magnetized plasma. Work supported in part by US DOE under DE-FG02-08ER54963.
The Edinburgh human metabolic network reconstruction and its functional analysis
Ma, Hongwu; Sorokin, Anatoly; Mazein, Alexander; Selkov, Alex; Selkov, Evgeni; Demin, Oleg; Goryanin, Igor
2007-01-01
A better understanding of human metabolism and its relationship with diseases is an important task in human systems biology studies. In this paper, we present a high-quality human metabolic network manually reconstructed by integrating genome annotation information from different databases and metabolic reaction information from literature. The network contains nearly 3000 metabolic reactions, which were reorganized into about 70 human-specific metabolic pathways according to their functional...
Functional Extinctions of Species in Ecological Networks
Säterberg, Torbjörn
2016-01-01
Current rates of extinctions are estimated to be around 1000 times higher than background rates that would occur without anthropogenic impacts. These extinction rates refer to the traditional view of extinctions, i.e. numerical extinctions. This thesis is about another type of extinctions: functional extinctions. Those occur when the abundance of a species is too small to uphold the species’ ecologically interactive role. I have taken a theoretical approach and used dynamical models to invest...
Functional brain networks develop from a "local to distributed" organization.
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Damien A Fair
2009-05-01
Full Text Available The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI, graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward 'segregation' (a general decrease in correlation strength between regions close in anatomical space and 'integration' (an increased correlation strength between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more "distributed" architecture in young adults. We argue that this "local to distributed" developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths are similar in child and adult graphs, with both showing "small-world"-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults
Functional deficits of the attentional networks in autism
Fan, Jin; Bernardi, Silvia; Van Dam, Nicholas T.; Anagnostou, Evdokia; Gu, Xiaosi; Martin, Laura; Park, Yunsoo; Liu, Xun; Kolevzon, Alexander; Soorya, Latha; Grodberg, David; Hollander, Eric; Hof, Patrick R.
2012-01-01
Attentional dysfunction is among the most consistent observations of autism spectrum disorders (ASD). However, the neural nature of this deficit in ASD is still unclear. In this study, we aimed to identify the neurobehavioral correlates of attentional dysfunction in ASD. We used the Attention Network Test-Revised and functional magnetic resonance imaging to examine alerting, orienting, and executive control functions, as well as the neural substrates underlying these attentional functions in ...
Institute of Scientific and Technical Information of China (English)
林涛; 葛玉敏; 安玳宁
2015-01-01
提出一种基于 RBF 神经网络的数据挖掘方法，将 RBF 神经网络应用于数据挖掘的分类和预测中，解决钢构件过程中的性能预测问题。其中用黄金分割法确定基于 RBF 神经网络的隐层节点数，减少该算法的计算复杂度，最终将其应用于某钢铁企业质量控制系统。构建对钢构件质量检测的数据挖掘及质量追溯平台，该平台是基于 RBF 神经网络的数据挖掘技术的。实际应用证明，产品的质量合格率可达到96.27%，符合国家相关的标准和技术指标。%To solve the performance prediction problem in the steel production process,this paper presentsed an ap-proach which is based on RBF neural network data mining method and uses RBF neural network in classification and predic-tion of data mining.The hidden layer nodes of the RBF neural network were determined by the golden section method to re-duce the computational complexity of the algorithm,which were applied to a steel enterprise quality control system.Finally, a platform of data mining and quality retrospective,which is based on RBF neural network data mining technology,was con-structed in product quality testing in steel companies.Practical application shows that the qualified rate of products can reach 96.27%,in line with national standards and technical specifications.
Personal networking function of the Internet
Petrović Dalibor
2009-01-01
The aim of the paper is to understand the role of Internet in creating new forms of sociability in the modern society. In the first part the history of social studies of Internet is reviewed, and the conclusion put forward that the anti-social role of the Internet cannot be proved. In the theoretical part of the paper the author presents his idea of two basic roles of Internet as interpersonal interaction tool: transmissional and procreative. These two Internet functions are very important me...
Additive functions in boolean models of gene regulatory network modules.
Darabos, Christian; Di Cunto, Ferdinando; Tomassini, Marco; Moore, Jason H; Provero, Paolo; Giacobini, Mario
2011-01-01
Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity
Directory of Open Access Journals (Sweden)
Christian Rössert
2015-10-01
Full Text Available Models of the cerebellar microcircuit often assume that input signals from the mossy-fibers are expanded and recoded to provide a foundation from which the Purkinje cells can synthesize output filters to implement specific input-signal transformations. Details of this process are however unclear. While previous work has shown that recurrent granule cell inhibition could in principle generate a wide variety of random outputs suitable for coding signal onsets, the more general application for temporally varying signals has yet to be demonstrated. Here we show for the first time that using a mechanism very similar to reservoir computing enables random neuronal networks in the granule cell layer to provide the necessary signal separation and extension from which Purkinje cells could construct basis filters of various time-constants. The main requirement for this is that the network operates in a state of criticality close to the edge of random chaotic behavior. We further show that the lack of recurrent excitation in the granular layer as commonly required in traditional reservoir networks can be circumvented by considering other inherent granular layer features such as inverted input signals or mGluR2 inhibition of Golgi cells. Other properties that facilitate filter construction are direct mossy fiber excitation of Golgi cells, variability of synaptic weights or input signals and output-feedback via the nucleocortical pathway. Our findings are well supported by previous experimental and theoretical work and will help to bridge the gap between system-level models and detailed models of the granular layer network.
Multimodal functional network connectivity: an EEG-fMRI fusion in network space.
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Xu Lei
Full Text Available EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs are extracted using spatial independent component analysis (ICA in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA. Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI. Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state.
Characteristic functions and process identification by neural networks
Dente, J A
1997-01-01
Principal component analysis (PCA) algorithms use neural networks to extract the eigenvectors of the correlation matrix from the data. However, if the process is non-Gaussian, PCA algorithms or their higher order generalisations provide only incomplete or misleading information on the statistical properties of the data. To handle such situations we propose neural network algorithms, with an hybrid (supervised and unsupervised) learning scheme, which constructs the characteristic function of the probability distribution and the transition functions of the stochastic process. Illustrative examples are presented, which include Cauchy and Levy-type processes
Optimal Computation of Symmetric Boolean Functions in Collocated Networks
Kowshik, Hemant
2011-01-01
We consider collocated wireless sensor networks, where each node has a Boolean measurement and the goal is to compute a given Boolean function of these measurements. We first consider the worst case setting and study optimal block computation strategies for computing symmetric Boolean functions. We study three classes of functions: threshold functions, delta functions and interval functions. We provide exactly optimal strategies for the first two classes, and a scaling law order-optimal strategy with optimal preconstant for interval functions. We also extend the results to the case of integer measurements and certain integer-valued functions. We use lower bounds from communication complexity theory, and provide an achievable scheme using information theoretic tools. Next, we consider the case where nodes measurements are random and drawn from independent Bernoulli distributions. We address the problem of optimal function computation so as to minimize the expected total number of bits that are transmitted. In ...
Emergence of Functional Specificity in Balanced Networks with Synaptic Plasticity.
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Sadra Sadeh
2015-06-01
Full Text Available In rodent visual cortex, synaptic connections between orientation-selective neurons are unspecific at the time of eye opening, and become to some degree functionally specific only later during development. An explanation for this two-stage process was proposed in terms of Hebbian plasticity based on visual experience that would eventually enhance connections between neurons with similar response features. For this to work, however, two conditions must be satisfied: First, orientation selective neuronal responses must exist before specific recurrent synaptic connections can be established. Second, Hebbian learning must be compatible with the recurrent network dynamics contributing to orientation selectivity, and the resulting specific connectivity must remain stable for unspecific background activity. Previous studies have mainly focused on very simple models, where the receptive fields of neurons were essentially determined by feedforward mechanisms, and where the recurrent network was small, lacking the complex recurrent dynamics of large-scale networks of excitatory and inhibitory neurons. Here we studied the emergence of functionally specific connectivity in large-scale recurrent networks with synaptic plasticity. Our results show that balanced random networks, which already exhibit highly selective responses at eye opening, can develop feature-specific connectivity if appropriate rules of synaptic plasticity are invoked within and between excitatory and inhibitory populations. If these conditions are met, the initial orientation selectivity guides the process of Hebbian learning and, as a result, functionally specific and a surplus of bidirectional connections emerge. Our results thus demonstrate the cooperation of synaptic plasticity and recurrent dynamics in large-scale functional networks with realistic receptive fields, highlight the role of inhibition as a critical element in this process, and paves the road for further computational
Directory of Open Access Journals (Sweden)
Gisele Tessari Santos
2009-08-01
Full Text Available A large number of financial engineering problems involve non-linear equations with non-linear or time-dependent boundary conditions. Despite available analytical solutions, many classical and modified forms of the well-known Black-Scholes (BS equation require fast and accurate numerical solutions. This work introduces the radial basis function (RBF method as applied to the solution of the BS equation with non-linear boundary conditions, related to path-dependent barrier options. Furthermore, the diffusional method for solving advective-diffusive equations is explored as to its effectiveness to solve BS equations. Cubic and Thin-Plate Spline (TPS radial basis functions were employed and evaluated as to their effectiveness to solve barrier option problems. The numerical results, when compared against analytical solutions, allow affirming that the RBF method is very accurate and easy to be implemented. When the RBF method is applied, the diffusional method leads to the same results as those obtained from the classical formulation of Black-Scholes equation.Muitos problemas de engenharia financeira envolvem equações não-lineares com condições de contorno não-lineares ou dependentes do tempo. Apesar de soluções analíticas disponíveis, várias formas clássicas e modificadas da conhecida equação de Black-Scholes (BS requerem soluções numéricas rápidas e acuradas. Este trabalho introduz o método de função de base radial (RBF aplicado à solução da equação BS com condições de contorno não-lineares relacionadas a opções de barreira dependentes da trajetória. Além disso, explora-se o método difusional para solucionar equações advectivo-difusivas quanto à sua efetividade para solucionar equações BS. Utilizam-se funções de base radial Cúbica e Thin-Plate Spline (TPS, aplicadas à solução de problemas de opções de barreiras. Os resultados numéricos, quando comparados com as soluções analíticas, permitem afirmar
International Nuclear Information System (INIS)
Unstructured three-dimensional fluid velocity data were interpolated using Gaussian radial basis function (RBF) interpolation. Data were generated to imitate the spatial resolution and experimental uncertainty of a typical implementation of defocusing digital particle image velocimetry. The velocity field associated with a steadily rotating infinite plate was simulated to provide a bounded, fully three-dimensional analytical solution of the Navier–Stokes equations, allowing for robust analysis of the interpolation accuracy. The spatial resolution of the data (i.e. particle density) and the number of RBFs were varied in order to assess the requirements for accurate interpolation. Interpolation constraints, including boundary conditions and continuity, were included in the error metric used for the least-squares minimization that determines the interpolation parameters to explore methods for improving RBF interpolation results. Even spacing and logarithmic spacing of RBF locations were also investigated. Interpolation accuracy was assessed using the velocity field, divergence of the velocity field, and viscous torque on the rotating boundary. The results suggest that for the present implementation, RBF spacing of 0.28 times the boundary layer thickness is sufficient for accurate interpolation, though theoretical error analysis suggests that improved RBF positioning may yield more accurate results. All RBF interpolation results were compared to standard Gaussian weighting and Taylor expansion interpolation methods. Results showed that RBF interpolation improves interpolation results compared to the Taylor expansion method by 60% to 90% based on the average squared velocity error and provides comparable velocity results to Gaussian weighted interpolation in terms of velocity error. RMS accuracy of the flow field divergence was one to two orders of magnitude better for the RBF interpolation compared to the other two methods. RBF interpolation that was applied to
Ribe, N. M.; Castelnau, O.
2008-12-01
Current methods for calculating the evolution of flow-induced seismic anisotropy in the upper mantle describe Lattice Preferred Orientation (LPO) using ensembles of 103-104 individual grains, and are nowaday too computationally expensive to be incorporated into three-dimensional time-dependent convection models. We propose a much faster (by a factor ~ 103) method wherein LPO is described by a small number of 'structured basis functions' (SBFs.) The number of SBFs required is equal to the number of active slip systems (= 3 for olivine), and each SBF represents the 'virtual' LPO that would be produced by the action of just one of those systems. Analytical expressions for the SBFs are obtained using a simple 'single-slip' (SS) model, and are then tested against the predictions of the second-order (SO) self-consistent model of Ponte-Castaneda (J. Mech. Phys. Solids 50, 737-757, 2002) in which several slip systems act simultaneously. Remarkably, the SS model reproduces exactly (99.9% variance reduction) the orientation- dependence of the slip rate ·γ predicted by the SO model for each active slip system, once the overall amplitude of the SS expression for ·γ has been determined by least-squares fitting to the SO prediction. Having thus demonstrated that the analytical SBFs are physically realistic, we develop a scheme for representing an arbitrary LPO as a superposition of the SBFs and for determining the evolution equations satisfied by the expansion coefficients. We illustrate the method both for simple uniform deformations (uniaxial compression, simple shear) and for more geophysically realistic nonuniform deformation histories.