Abstract
As a special type of Self-Organizing Maps (SOM), the Dynamic Cell Structures (DCS) network has topology-preserving adaptive learning capabilities that can, in theory, respond and learn to abstract from a wide variety of complex data manifolds. However, the highly complex learning algorithm and non-linearity behind the dynamic learning pose serious challenge to validating the performance of DCS and impede its spread in control applications, safety-critical systems in particular. In this paper, we analyze the performance of DCS network by pro- viding sensitivity analysis on its structure and confidence measures on its predictions. We evaluate how the quality of each parameter of the network (e.g., weight) influences the output of the network by defining a metric for parameter sensitivity for DCS network. We present the validity index (VI), an estimated confidence associated with each DCS
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