Connexionnist theory for the identification of complex processes and the parametric conversion of 'static data base' models. application to internal combustion engines

摘要

In order to regulate and control a real system, a mathematical representation is required which provides a satisfactory estimation of the process and which can be obtained by identification. System identification is the subject of much research and many articles propose often complex, discrete algorithms. Moreover, the models and identification methods used are different according to whether the real system is linear or nonlinear. This paper presents an identification methodology based on a single model deduced from Neural network theory. The first paragraph proves end defines the model which allow identification of a real complex process, whether it be linear or non linear. The following paragraph describes the method of calculation for a polynomial model as an alternative to a static data base representation (internal combustion engine map). For each model, it measures and analyses the degree of precision obtained, and defines the influence parameters for convergence of network errors.