I. Introduction

Estimates are not the same as the facts. Model validation is necessary to gain confidence in, or reject, a particular model. This basic fundamental principle applies to all engineering and other processes. In our particular case of flight vehicles, as discussed in Chapter 1 and depicted in Figs. 1.2 and 1.5, the parameter estimation and the model validation are integral parts of system identification. From the foregoing chapters, it is apparent to the readers that the parameter estimation methods provide an answer to the question: Given the system inputs and responses, what is the model? On the other hand, model validation tries to provide an answer to the question:1 How do you know that you got the right answer? It is obvious that the answer in this case means the identified model. In this chapter we deal with the issues related to the process of determining the correctness, accuracy, adequacy, and applicability of the identified model. We also attempt to check the validity of the underlying theoretical assumptions which were made in the derivation of the parameter estimation methods applied. An apt definition of model validation is provided by Schlesinger et al.2

Validation refers to the process of confirming that the conceptual model is applicable or useful by demonstrating an adequate correspondence between the computational results of the model and the actual data (if it exists) or other theoretical data.

The various different aspects of model validation can be broadly classified into three subcategories: 1) statistical properties of...