Abstract
This paper is about the design and development of a computer tool for identifying and estimating the characteristic parameters of linear time-invariant systems through collective intelligence based on emerging bio-inspired systems. The tool is named System Identifier (SysID) which uses Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) to find the optimal model of a unknow linear system, and it presents the result as a transfer function whether it is first order with or without dead time, second-order and inverse response systems. We performed an experimental evaluation of the effectiveness and efficiency of SysID using synthetic data of real-life processes. The results of our experiment demonstrate that SysID is an accurate tool to determine appropriate values of model systems using model fitting by minimizing a cost function.
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