Combining Models in System Identification

Combining Models in System Identification

Patching different kinds of models obtained from experimental data is considered in this paper. The problem of combining different experiments and models is analysed. As a related issue, iteratively improving models and combining models for control oriented identification is investigated. A simulated example is given to illustrate the approach. Copyright  2000 IFAC

System Identification Using Fuzzy Models

Applications of Kautz Models in System Identification

FIR, ARX or AR model structures can be used to describe many industrial processes. Simple linear regression techniques can be applied to estimate such models from experimental data. However, for low signal to noise ratios in combination with transfer function poles and noise model zeros close to the unit circle, a large number of model parameters are needed to generate adequate models. The Kaut...

Using Artificial Intelligence Models in System Identification

Artificial Intelligence (AI) techniques are known for its ability in tackling problems found to be unyielding to traditional mathematical methods. A recent addition to these techniques are the Computational Intelligence (CI) techniques which, in most cases, are nature or biologically inspired techniques. Different CI techniques found their way to many control engineering applications, including...

Homosexuality identification models: review study

Identity exploration process is one of the most important tasks that everyone should do in life which is, in fact, the same answer to question of "Who am I". During the identification process, person initially concludes that "I am a man" or " I am a woman" which is named gender identity. After defining themselves, the person chooses a partner, that this choice represents his sexual orientation....