Determination of the internal structure of neutron stars from gravitational wave spectra

In this paper the internal structure of a neutron star is shown to be inferrable from its gravitational-wave spectrum. Iteratively applying the inverse scheme of the scaled coordinate logarithmic perturbation method for neutron stars proposed by Tsui and Leung [Astrophys. J. {\bf 631}, 495 (2005)], we are able to determine the mass, the radius and the mass distribution of a star from its quasi-normal mode frequencies of stellar pulsation. In addition, accurate equation of state of nuclear matter can be obtained from such inversion scheme. Explicit formulas for the case of axial $w$-mode oscillation are derived here and numerical results for neutron stars characterized by different equations of state are shown.Comment: 26 pages, 14 figures, submitted to Physical Review

Perturbative Analysis of Universality and Individuality in Gravitational Waves from Neutron Stars

The universality observed in gravitational wave spectra of non-rotating neutron stars is analyzed here. We show that the universality in the axial oscillation mode can be reproduced with a simple stellar model, namely the centrifugal barrier approximation (CBA), which captures the essence of the Tolman VII model of compact stars. Through the establishment of scaled co-ordinate logarithmic perturbation theory (SCLPT), we are able to explain and quantitatively predict such universal behavior. In addition, quasi-normal modes of individual neutron stars characterized by different equations of state can be obtained from those of CBA with SCLPT.Comment: 29 pages, 10 figures, submitted to Astrophysical Journa

Sequential RBF function estimator: memory regression network

The newal-network training algorithm can be divided into 2 categories: (I) Batch mode and (2) Sequential mode. In this paper, a novel online RBF network called "Memory Regression Network (MRN)" is proposed. Different from the previous approaches [2, 11], MRN involves two types of memories: Experience and Neuron, which handle short and long term memories respectively. By simulating human's learning behavior, a given function can be estimated without memorizing the whole training set. Two sets of function estimation experiments are examined in order to illustrate the performance of the proposed algorithm. The results show that MRN can effectively approximate the given function within a reasonable time and acceptable mean square error. © 2004 IEEE.published_or_final_versio

Image enlargement as an edge estimation

A robust image enlargement algorithm is presented in this paper. We formulate the image enlargement process as an edge information estimation process. In order to achieve a higher resolution, we first perform Pixel Duplication on the target image to form an initial high resolution image. Then the edge details of the enlarged image are estimated by using a novel neural network called "Agent Swarm Regression Network ASRN", which is trained by a set of low resolution (LR) / high resolution (HR) image patch pairs. Two benchmark images were used to verify the performance of the proposed algorithm. The results show that the enlarged images by the proposed algorithm are sharper than those by the conventional methods.published_or_final_versio

Response knowledge learning of autonomous agent

In robot applications, the performance of a robot agent is measured by the quantity of award received from its response. Many literatures [1-5] define the response as either a state diagram or a neural network. Due to the absence of a desired response, neither of them is applicable to an unstructural environment. In this paper, a novel Response Knowledge Learning algorithm is proposed to handle this domain. By using a set of experiences, the algorithm can extract the contributed experiences to construct the response function. Two sets of environments are provided to illustrate the performance of the proposed algorithm. The results show that it can effectively construct the response function that receives an award which is very close to the true maximum.published_or_final_versio

Agent swarm classification network ASCN

In this paper we introduced a newly RBF Classification Network - "Agent Swarm Classification Network ASCN", which is trained by a Multi-agent systems (MAS) approach. MAS can be regarded as a swarm of independent software agents interact with each other to achieve common goals, complete concurrent distributed tasks under autonomous control. By treating each neuron as an agent, the weights of neurons can be determined through a set of pre-defined simple agent behavior. Three sets of experiments are examined to observe the effectiveness of the proposed method. © 2004 IEEE.published_or_final_versio

Agent swarm regression network ASRN

A multi-agent system (MAS), with independent software agents interacting with each other to achieve common goals will complete concurrent distributed tasks under autonomous control. In this paper, novel RBF Regression Network - "Agent Swarm Regression Network ASRN" is proposed and will be trained by a MAS. Each neuron of the ASRN is considered as an agent, which consists of per-deflned simple agent behavior set. After a sufficient number of iterations, the weights of neurons can be determined. Two sets of experiment will be examined to observe the effectiveness of the proposed method. © 2004 IEEE.published_or_final_versio