Ways of Fusing Different Types of Information and How Systemic Yoyo Model is Applied in Complex Systems Evaluation and Estimation

Abstract

Purpose – Continuing the works in (Duan and Lin, 2011), we show in this paper how different types of information can be fused together consistently in order to produce accurate evaluations and estimations for complex systems. Design/methodology/approach – The theoretical part of this presentation is based on the standard statistical reasoning, while the ending part constructs three case studies in order to validate the main thinking logic and results obtained in (Duan and Lin, 2011) and in this paper. Findings – It is shown that (1) for linear systems, when fusing data of different types, the weights placed on the data have profound effects on the outcomes and the achieved precisions, meaning that in this case, the unique optimal weight matrix is determined by the precisions of the data (Gauss-Markov Theorem of linear models); (2) for nonlinear models, when fusing heterogeneous sets of data with varied scales of precision, the structure of the weight matrix is no longer uniquely determined by the precisions，but also related to the degree of model nonlinearity, indicating that the classical Gauss-Markov Theorem of linear models no longer holds true. At the same time, a specific method of determining the optimal weighting factor and the relevant computational method for estimating the parameters are established. Combined with the process of conserved information applied in systems evaluation, we provide three case studies, including (1) how to quantitatively measure prior knowledge and observational data so that prior knowledge can be considered in obtaining much improved optimal systems evaluations; (2) how to excavate new sources of observational data of processes so that the established models can be validated jointly using process data collected under different test environments and the directly measured information of the specific indices of concern in order to improve the quality of systems evaluation and estimation and to obtain model validation results of better accuracy; and (3) how to more effectively fuse prior knowledge and heterogeneous sets of data. All of these case studies further witness the epistemological validity of the information conservation existing in the systemic recognition process beneath the systems model description, prior knowledge, and observational data, and their transformational relationship, as obtained in (Duan and Lin, 2011). Practical implications – Because other than establishing the theory, particular procedures are provided, conclusions of this work can be directly employed in system evaluations and estimations and related works. Originality/value – This work shows how systemic thinking can be practically applied to benefit the efforts of system evaluation and model estimations involved in various engineering projects.