<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/3276" mdate="2008-06-27 00:00:00"> <author>W.S. Lim and M.V.C. Rao</author> <title>Effective Sonar Target Classification via Parallel Structure of Minimal Resource Allocation Network</title> <pages>2213 - 2220</pages> <year>2008</year> <volume>2</volume> <number>6</number> <journal>International Journal of Computer and Information Engineering</journal> <ee>https://publications.waset.org/pdf/3276</ee> <url>https://publications.waset.org/vol/18</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>In this paper, the processing of sonar signals has been carried out using Minimal Resource Allocation Network (MRAN) and a Probabilistic Neural Network (PNN) in differentiation of commonly encountered features in indoor environments. The stabilityplasticity behaviors of both networks have been investigated. The experimental result shows that MRAN possesses lower network complexity but experiences higher plasticity than PNN. An enhanced version called parallel MRAN (pMRAN) is proposed to solve this problem and is proven to be stable in prediction and also outperformed the original MRAN.</abstract> <index>Open Science Index 18, 2008</index> </article>