학술논문
Artificial evolution of pulsed neural networks on the motion pattern classification system
Document Type
Conference
Author
Source
Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation. Computational Intelligence in Robotics and Automation for the New Millennium (Cat. No.03EX694) Computational intelligence in robotics and automation Computational Intelligence in Robotics and Automation, 2003. Proceedings. 2003 IEEE International Symposium on. 1:318-323 vol.1 2003
Subject
Language
Abstract
Categorization is one of the most important cognitive abilities for autonomous agents. In natural systems, animals discriminate any object not only by its figure but also by its motion pattern. In this work, we applied the standard GA to evolve pulsed neural controllers for the motion pattern classification system in order to investigate how evolved agents perform the discrimination task, its evolutionary dynamics and the process of self-organization in the neural controllers. The results demonstrate that the agent controlled by the evolved neural networks can discriminate between the objects with the different motion. In the process of evolution, the fitness is improved by the modulation in the connection weights among neurons.