Discovering Admissible Models of complex Systems Based on Scale-Types and Identity Constraints

摘要

SDS is a discovery system from numeric measurement data. It outperforms the existing systems in every aspect of search efficiency, noise tolerancy, credibility of the resulting equations and complexity of the target system that it can handle. The power of SDS comes fro mthe use of the scale-types of the measurmeent data and mathematical property of identity by which to constrain the admissible solutions. Its algorithm is described with a compelx working example and the performance comparison with other systems are discussed.