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.
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.
492137
Blind source extraction in various ill-conditioned cases
The paper discusses blind source extraction in various ill-conditioned cases based on a simple extraction network model. Extractability is first analyzed for the following ill-conditioned cases: the mixing matrix is square but-singular; the number of sensors is smaller than that of sources; the number of sensors is larger than that of sources, but the column rank of the mixing matrix is deficient; the number of sources is unknown and the column rank of the mixing matrix is deficient. A necessary and sufficient condition for extractability is obtained. A cost function and an unsupervised learning algorithm for the extraction network model are developed. Simulation results are also presented to show the validity of the theoretical results and the performance and characteristics of the learning algorithm.
11945508
Content-Based Audio Classification Using Support Vector Machines and Independent Component Analysis
In this paper, we present a new audio classification system. First, a frame-based multiclass support vector machine (SVM) for audio classification is proposed. The accuracy rate has significant improvements over conventional file-based SVM audio classifier. In feature selection, this study transforms the log powers of the critical-band filters based on independent component analysis (ICA). This new audio feature is combined with mel-frequency cepstral coefficients (MFCCs) and five perceptual features to form an audio feature set. The superiority of the proposed system has been demonstrated via a 15-class sound database with a 91.7% accuracy rate
7844670
A sparse support vector machine approach to region-based image categorization
Automatic image categorization using low-level features is a challenging research topic in computer vision. In this paper, we formulate the image categorization problem as a multiple-instance learning (MIL) problem by viewing an image as a bag of instances, each corresponding to a region obtained from image segmentation. We propose a new solution to the resulting MIL problem. Unlike many existing MIL approaches that rely on the diverse density framework, our approach performs an effective feature mapping through a chosen metric distance function. Thus the MIL problem becomes solvable by a regular classification algorithm. Sparse SVM is adopted to dramatically reduce the regions that are needed to classify images. The selected regions by a sparse SVM approximate to the target concepts in the traditional diverse density framework. The proposed approach is a lot more efficient in computation and less sensitive to the class label uncertainty. Experimental results are included to demonstrate the effectiveness and robustness of the proposed method.
10201400
RF/sup */IPF: a weighting scheme for multimedia information retrieval
The region-based approach has become a popular research trend in the field of multimedia database retrieval. We present the Region Frequency and Inverse Picture Frequency (RF/sup */IPF) weighting, a measure developed to unify region-based multimedia retrieval systems with text-based information retrieval systems. The weighting measure gives the highest weight to regions that occur often in a small number of images in the database. These regions are considered discriminators. With this weighting measure, we can blend image retrieval techniques with TF/sup */IDF-based text retrieval techniques for large-scale Web applications. The RF/sup */IPF weighting has been implemented as a part of our experimental SIMPLIcity image retrieval system and tested on a database of about 200000 general-purpose images. Experiments have shown that this technique is effective in discriminating images of different semantics. Additionally, the overall similarity approach enables a simple querying interface for multimedia information retrieval systems.
23774
On Understanding Transient Interdomain Routing Failures
The convergence time of the interdomain routing protocol, BGP, can last as long as 30 minutes. Yet, routing behavior during BGP route convergence is poorly understood. During route convergence, an end-to-end Internet path can experience a transient loss of reachability. We refer to this loss of reachability as transient routing failure. Transient routing failures can lead to packet losses, and prolonged packet loss bursts can make the performance of applications such as Voice-over-IP and interactive games unacceptable. In this paper, we study how routing failures can occur in the Internet. With the aid of a formal model that captures transient failures of the interdomain routing protocol, we derive the sufficient conditions that transient routing failures could occur. We further study transient routing failures in typical BGP systems where commonly used routing policies are applied. Network administrators can apply our analysis to improve their network performance and stability.
11111374
Neural Network Based Stereotyping for User Profiles
This paper discusses an approach of establishing system models of users’ task related characteristics, such as domain knowledge in human-computer interaction. Several neural networks are tested for the modelling process. These networks function as associative memories that capture the causal relationships among assumptions about the users’ characteristics. The outputs from the networks are considered as stereotypes assigned to individual users. It is suggested that this approach can be expected to overcome some limitations of user modelling approaches in terms of pattern recognition and classification.