Abstract
Complex Adaptive Structures can be viewed as a
combination of Complex Adaptive Systems and fully
integrated autonomous Smart Structures. Traditionally
when designing a structure, one combines rules of thumb
with theoretical results to develop an acceptable
solution. This methodology will have to be extended for
Complex Adaptive Structures, since they, by definition,
will participate in their own design. In this paper we
introduce a new methodology for Emergent System
Identification that is concerned with combining the
methodologies of self-organizing functional networks
(GMDH - Alexy G. Ivakhnenko), Particle Swarm
Optimization (PSO - James Kennedy and Russell C.
Eberhart) and Genetic Programming (GP - John Koza).
This paper will concentrate on the use of Particle
Swarm Optimization in this effort and discuss how
Particle Swarm Optimization relates to our ultimate
goal of emergent self-organizing functional networks
that can be used to identify overlapping internal
structural models. The ability for Complex Adaptive
Structures to identify emerging internal models will be
a key component for their success.
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