18821545
Multiperiodicity and Exponential Attractivity Evoked by Periodic External Inputs in Delayed Cellular Neural Networks
We show that an n-neuron cellular neural network with time-varying delay can have 2n periodic orbits located in saturation regions and these periodic orbits are locally exponentially attractive. In addition, we give some conditions for ascertaining periodic orbits to be locally or globally exponentially attractive and allow them to locate in any designated region. As a special case of exponential periodicity, exponential stability of delayed cellular neural networks is also characterized. These conditions improve and extend the existing results in the literature. To illustrate and compare the results, simulation results are discussed in three numerical examples.
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.
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.
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.
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.
6599744
A multilayer recurrent neural network for solving continuous-time algebraic Riccati equations
A multilayer recurrent neural network is proposed for solving continuous-time algebraic matrix Riccati equations in real time. The proposed recurrent neural network consists of four bidirectionally connected layers. Each layer consists of an array of neurons. The proposed recurrent neural network is shown to be capable of solving algebraic Riccati equations and synthesizing linear-quadratic control systems in real time. Analytical results on stability of the recurrent neural network and solvability of algebraic Riccati equations by use of the recurrent neural network are discussed. The operating characteristics of the recurrent neural network are also demonstrated through three illustrative examples.
15313247
Performance of a MAC protocol for WDM networks with on-line scheduling
This paper investigates the performance of media access protocols based on on-line scheduling for an optically connected star-coupled system with wavelength division multiple access channels. Hybrid access protocols combining reservation and pre-allocation of receiver channels have been previously proposed. Transmission consisted of a reservation phase followed by a data phase. Each node was allowed to place a reservation for one WDM channel. This protocol is extended in this paper by allowing reservations on multiple channels and using simple on-line scheduling algorithms. Existing scheduling algorithms (all are off-line) for similar reservation problems tend to have high implementation and computational complexity. We require that scheduling algorithms be real-time and amenable to hardware implementation. The performance of the extended protocol incorporating two simple on-line scheduling algorithms is presented in this paper. We show that the slight increased computational overhead with scheduling is justified by reduced packet latency and higher utilization, especially for client-server traffic.
5498425
Representing moving images with layers
We describe a system for representing moving images with sets of overlapping layers. Each layer contains an intensity map that defines the additive values of each pixel, along with an alpha map that serves as a mask indicating the transparency. The layers are ordered in depth and they occlude each other in accord with the rules of compositing. Velocity maps define how the layers are to be warped over time. The layered representation is more flexible than standard image transforms and can capture many important properties of natural image sequences. We describe some methods for decomposing image sequences into layers using motion analysis, and we discuss how the representation may be used for image coding and other applications.
39453122
Xml Clustering And Retrieval Through Principal Component Analysis
XML is increasingly important in data exchange and information management. A great deal of efforts have been spent in developing efficient techniques for storing, querying, indexing and accessing XML documents. In this paper we propose a new approach to clustering XML data. In contrast to previous work, which focused on documents defined by different DTDs, the proposed method works for documents with the same DTD. Our approach is to extract features from documents, modeled by ordered labeled trees, and transform the documents to vectors in a high-dimensional Euclidean space based on the occurrences of the features in the documents. We then reduce the dimensionality of the vectors by principal component analysis (PCA) and cluster the vectors in the reduced dimensional space. The PCA enables one to identify vectors with co-occurrent features, thereby enhancing the accuracy of the clustering. We also discuss an extension of our techniques to XML retrieval. Experimental results based on documents obtained from Wisconsin's XML data bank show the effectiveness and good performance of the proposed techniques.
1365809
New techniques for extracting features from protein sequences
In this paper we propose new techniques to extract features from protein sequences. We then use the features as inputs for a Bayesian neural network (BNN) and apply the BNN to classifying protein sequences obtained from the PIR (Protein Information Resource) database maintained at the National Biomedical Research Foundation. To evaluate the performance of the proposed approach, we compare it with other protein classifiers built based on sequence alignment and machine learning methods. Experimental results show the high precision of the proposed classifier and the complementarity of the bioinformatics tools studied in the paper.
8013928
OMAR: Utilizing Multiuser Diversity in Wireless Ad Hoc Networks
One of the most promising approaches to improving communication efficiency in wireless communication systems is the use of multiuser diversity. Although it has been widely investigated and shown feasible and efficient in cellular networks, there is little work for the ad hoc networks, especially in real protocol and algorithm design. In this paper, we propose a novel scheme, namely, the opportunistic medium access and auto rate (OMAR), to efficiently utilize the shared medium in IEEE 802.11-based ad hoc networks by taking advantage of diversity, distributed scheduling, and adaptivity. In an ad hoc network, especially in a heterogeneous ad hoc network or a mesh network, some nodes may need to communicate with multiple one-hop nodes. We allow such a node with a certain number of links to function as a clusterhead to locally coordinate multiuser communications. We introduce a CDF-based (cumulative distribution function) K-ary opportunistic splitting algorithm and a distributed stochastic scheduling algorithm to resolve intra and intercluster collisions, respectively. Fairness is formulated and solved in terms of social optimality within and across clusters. Analytical and simulation results show that our scheme can significantly improve communication efficiency while providing social fairness