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

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

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 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

Search for a simultaneous signal from small transient events in the Pierre Auger Observatory and the Tupi muon telescopes

We present results of a search for a possible signal from small scale solar transient events (such as flares and interplanetary shocks) as well as possible counterparts to Gamma-Ray Burst (GRB) observed simultaneously by the Tupi muon telescope Niteroi-Brazil, 22.90S, 43.20W, 3 m above sea level) and the Pierre Auger Observatory surface detectors (Malargue-Argentina, 69.30S, 35.30W, altitude 1400 m). Both cosmic ray experiments are located inside the South Atlantic Anomaly (SAA) region. Our analysis of several examples shows similarities in the behavior of the counting rate of low energy (above 100 MeV) particles in association with the solar activity (solar flares and interplanetary shocks). We also report an observation by the Tupi experiment of the enhancement of muons at ground level with a significance higher than 8 sigma in the 1-sec binning counting rate (raw data) in close time coincidence (T-184 sec) with the Swift-BAT GRB110928B (trigger=504307). The GRB 110928B coordinates are in the field of view of the vertical Tupi telescope, and the burst was close to the MAXI source J1836-194. The 5-min muon counting rate in the vertical Tupi telescope as well as publicly available data from Auger (15 minutes averages of the scaler rates) show small peaks above the background fluctuations at the time following the Swift-BAT GRB 110928B trigger. In accordance with the long duration trigger, this signal can possibly suggest a long GRB, with a precursor narrow peak at T-184 sec.Comment: 9 pages, 13 figure

Design of scalar functional observers of order less than (v-1)

This paper presents a new method of designing scalar functional observers of order less than the well-known upper bound (&nu; - 1). A condition for the existence of observers of order p where 1 &le; p &le; (&nu; - 1) is given. A simple and effective algorithm for solving the constrained generalized Sylvester equation is proposed. Several numerical examples are given to illustrate the attractiveness of the design algorithm. <br /