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Research Papers

A New Multivalued Neural Network for Isomorphism Identification of Kinematic Chains

[+] Author and Article Information
Gloria Galán-Marín

Department of Mechanical, Energetic and Materials Engineering, University of Extremadura, Avda. de Elvas s/n; 06006 Badajoz, Spaingloriagm@unex.es

Domingo López-Rodríguez

Department of Applied Mathematics, University of Malaga, Campus de Teatinos s/n; 29071 Malaga, Spaindlopez@ctima.uma.es

Enrique Mérida-Casermeiro

Department of Applied Mathematics, University of Malaga, Campus de Teatinos s/n; 29071 Malaga, Spainmerida@ctima.uma.es

J. Comput. Inf. Sci. Eng 10(1), 011009 (Mar 10, 2010) (4 pages) doi:10.1115/1.3330427 History: Received February 22, 2008; Revised October 06, 2009; Published March 10, 2010; Online March 10, 2010

A lot of methods have been proposed for the kinematic chain isomorphism problem. However, the tool is still needed in building intelligent systems for product design and manufacturing. In this paper, we design a novel multivalued neural network that enables a simplified formulation of the graph isomorphism problem. In order to improve the performance of the model, an additional constraint on the degree of paired vertices is imposed. The resulting discrete neural algorithm converges rapidly under any set of initial conditions and does not need parameter tuning. Simulation results show that the proposed multivalued neural network performs better than other recently presented approaches.

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Copyright © 2010 by American Society of Mechanical Engineers
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Figures

Grahic Jump Location
Figure 1

Different pairs of isomorphic and nonisomorphic kinematic chains

Grahic Jump Location
Figure 2

Different pairs of isomorphic and nonisomorphic graphs

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