9478197
A support vector machine with a hybrid kernel and minimal Vapnik-Chervonenkis dimension
We present a mechanism to train support vector machines (SVMs) with a hybrid kernel and minimal Vapnik-Chervonenkis (VC) dimension. After describing the VC dimension of sets of separating hyperplanes in a high-dimensional feature space produced by a mapping related to kernels from the input space, we proposed an optimization criterion to design SVMs by minimizing the upper bound of the VC dimension. This method realizes a structural risk minimization and utilizes a flexible kernel function such that a superior generalization over test data can be obtained. In order to obtain a flexible kernel function, we develop a hybrid kernel function and a sufficient condition to be an admissible Mercer kernel based on common Mercer kernels (polynomial, radial basis function, two-layer neural network, etc.). The nonnegative combination coefficients and parameters of the hybrid kernel are determined subject to the minimal upper bound of the VC dimension of the learning machine. The use of the hybrid kernel results in a better performance than those with a single common kernel. Experimental results are discussed to illustrate the proposed method and show that the SVM with the hybrid kernel outperforms that with a single common kernel in terms of generalization power.
12016558
Constrained motion control of flexible robot manipulators based on recurrent neural networks
In this paper, a neural network approach is presented for the motion control of constrained flexible manipulators, where both the contact force everted by the flexible manipulator and the position of the end-effector contacting with a surface are controlled. The dynamic equations for vibration of flexible link and constrained force are derived. The developed control, scheme can adaptively estimate the underlying dynamics of the manipulator using recurrent neural networks (RNNs). Based on the error dynamics of a feedback controller, a learning rule for updating the connection weights of the adaptive RNN model is obtained. Local stability properties of the control system are discussed. Simulation results are elaborated on for both position and force trajectory tracking tasks in the presence of varying parameters and unknown dynamics, which show that the designed controller performs remarkably well.
16046090
Nonlinear blind source separation using higher order statistics and a genetic algorithm
This paper presents a novel method for blindly separating unobservable independent source signals from their nonlinear mixtures. The demixing system is modeled using a parameterized neural network whose parameters can be determined under the criterion of independence of its outputs. Two cost functions based on higher order statistics are established to measure the statistical dependence of the outputs of the demixing system. The proposed method utilizes a genetic algorithm (GA) to minimize the highly nonlinear and nonconvex cost functions. The GA-based global optimization technique is able to obtain superior separation solutions to the nonlinear blind separation problem from any random initial values. Compared to conventional gradient-based approaches, the GA-based approach for blind source separation is characterized by high accuracy, robustness, and convergence rate. In particular, it is very suitable for the case of limited available data. Simulation results are discussed to demonstrate that the proposed GA-based approach is capable of separating independent sources from their nonlinear mixtures generated by a parametric separation model.
18621072
Primal and dual neural networks for shortest-path routing
Presents two recurrent neural networks for solving the shortest path problem. Simplifying the architecture of a recurrent neural network based on the primal problem formulation, the first recurrent neural network called the primal routing network has less complex connectivity than its predecessor. Based on the dual problem formulation, the second recurrent neural network called the dual routing network has even simpler architecture. While being simple in architecture, the primal and dual routing networks are capable of shortest-path routing like their predecessor.
8287323
A recurrent neural network for solving linear projection equations
Linear projection equations arise in many optimization problems and have important applications in science and engineering. In this paper, we present a recurrent neural network for solving linear projection equations in real time. The proposed neural network has two layers and is amenable to parallel implementation with simple hardware. In the theoretical aspect, we prove that the proposed neural network can converge globally to the solution set of the problem when the matrix involved in the problem is positive semidefinite and can converge exponentially to a unique solution when the matrix is positive definite. In addition, we analyze the stability of the related dynamic system in detail. As an application, we show that the proposed neural network can be used directly to solve linear and convex quadratic programming problems and linear complementary problems with positive semidefinite matrices. The validity and transient behavior of the neural network are demonstrated by using three numerical examples.
18182406
Recurrent neural networks for minimum infinity-norm kinematic control of redundant manipulators
This paper presents two neural network approaches to minimum infinity-norm solution of the velocity inverse kinematics problem for redundant robots. Three recurrent neural networks are applied for determining a joint velocity vector with its maximum absolute value component being minimal among all possible joint velocity vectors corresponding to the desired end-effector velocity. In each proposed neural network approach, two cooperating recurrent neural networks are used. The first approach employs two Tank-Hopfield networks for linear programming. The second approach employs two two-layer recurrent neural networks for quadratic programming and linear programming, respectively. Both the minimal 2-norm and infinity-norm of joint velocity vector can be obtained from the output of the recurrent neural networks. Simulation results demonstrate that the proposed approaches are effective with the second approach being better in terms of accuracy and optimality.
4384823
Capturing and rendering geometry details for BTF-mapped surfaces
Bidirectional texture functions, or BTFs, accurately model reflectance variation at a fine (meso-) scale as a function of lighting and viewing direction. BTFs also capture view-dependent visibility variation, also called masking or parallax, but only within surface contours. Mesostructure detail is neglected at silhouettes, so BTF-mapped objects retain the coarse shape of the underlying model.We augment BTF rendering to obtain approximate mesoscale silhouettes. Our new representation, the 4D mesostructure distance function (MDF), tabulates the displacement from a reference frame where a ray first intersects the mesoscale geometry beneath as a function of ray direction and ray position along that reference plane. Given an MDF, the mesostructure silhouette can be rendered with a per-pixel depth peeling process on graphics hardware, while shading and local parallax are handled by the BTF. Our approach allows real-time rendering, handles complex, non-height-field mesostructure, requires that no additional geometry be sent to the rasterizer other than the mesh triangles, is more compact than textured visibility representations used previously, and, for the first time, can be easily measured from physical samples. We also adapt the algorithm to capture detailed shadows cast both by and onto BTF-mapped surfaces. We demonstrate the efficiency of our algorithm on a variety of BTF data, including real data acquired using our BTF–MDF measurement system.
12107864
Kernel Trick Embedded Gaussian Mixture Model
In this paper, we present a kernel trick embedded Gaussian Mixture Model (GMM), called kernel GMM. The basic idea is to embed kernel trick into EM algorithm and deduce a parameter estimation algorithm for GMM in feature space. Kernel GMM could be viewed as a Bayesian Kernel Method. Compared with most classical kernel methods, the proposed method can solve problems in probabilistic framework. Moreover, it can tackle nonlinear problems better than the traditional GMM. To avoid great computational cost problem existing in most kernel methods upon large scale data set, we also employ a Monte Carlo sampling technique to speed up kernel GMM so that it is more practical and efficient. Experimental results on synthetic and real-world data set demonstrate that the proposed approach has satisfing performance.
14138215
Multiresolution object-of-interest detection for images with low depth of field
This paper describes a novel multiresolution image segmentation algorithm for separating sharply focused objects-of-interest from other foreground or background objects in low depth-of-field (DOF) images, such as sports, telephoto, macro, and microscopic images. The algorithm takes a multiscale context-dependent approach to segment images based on features extracted from wavelet coefficients in high-frequency bands. The algorithm is fully automatic in that all parameters are image-independent. Experiments with the algorithm on more than 100 low DOF images have shown results close to the human segmentation of these images. Besides high accuracy, the algorithm also provides high speed. A 768/spl times/512 pixel image can be segmented within two seconds on a Pentium Pro 300 MHz PC.
2808817
BGP routing stability of popular destinations
The Border Gateway Protocol (BGP) plays a crucial role in the delivery of traffic in the Internet. Fluctuations in BGP routes cause degradation in user performance, increased processing load on routers, and changes in the distribution of traffic load over the network. Although earlier studies have raised concern that BGP routes change quite often, previous work has not considered whether these routing fluctuations affect a significant portion of the traffic. This paper shows that the small number of popular destinations responsible for the bulk of Internet traffic have remarkably stable BGP routes. The vast majority of BGP instability stems from a small number of unpopular destinations. We draw these conclusions from a joint analysis of BGP update messages and flow-level traffic measurements from AT&T's IP backbone. In addition, we analyze the routing stability of destination prefixes corresponding to the NetRating's list of popular Web sites using the update messages collected by the RouteViews and RIPE-NCC servers. Our results suggest that operators can engineer their networks under the assumption that the BGP advertisements associated with most of the traffic are reasonably stable.
21940463
A hybrid method for unconstrained handwritten numeral recognition by combining structural and neural "gas" classifiers
Abstract This paper presents a hybrid method for handwritten numeral recognition that combines two compensatory recognition algorithms by analysing their performance for several aspects. The skeleton-based structural recognition algorithm employed in this method is robust under distortion but sensitive to noise and flaws. On the other hand, the neural network classifier, which uses scaled binary images as features and the neural “gas” model for classification, is relatively immune to noise and flaws but sensitive to distortion. The different performances of the two algorithms for broken, connected or slanted numerals, and the measurement-level decision provided by the neural network are detected and combined with different strategies to develop matching rules for each recognition method. Five combination methods based on performance analysis are developed to meet different requirements. As the two algorithms have fairly compensatory properties, the proposed method improves the recognition rate and reliability by exploiting the advantages and avoiding the weaknesses of each classifier. The experimental results from a large set of data show the efficiency and robustness of the proposed method.