Thermochemical systems appear in applications as widespread as in combustion engines, in industrial chemical plants and inside biological cells. Science in all these areas is going towards a more model based thinking, and it is therefore important to develop good methods for system identification, especially fit for these kind of systems. The presented systems are described as nonlinear differe...

Linear error models are an integral part of several parameter identification methods for feedforward and feedback control systems and lead in connection with the L2-norm to a convex distance measure which has to be minimised for identification purposes. The parameters are hereby often subject to specific restrictions whose intersections span a convex solution set with non-differentiability poin...

We are using Gaussian Mixture Models (GMM) as a tool to construct local mappings of nonlinear Multi-Input Multi-Output (MIMO) systems. In this work we combine the advantages of GMM with the Kalman filter. To improve the accuracy of the local linear mappings in a potentially large dimensional state space, we propose to initialize the GMM parameters with Vector Quantization (VQ) or its more parsi...

In practical system identification it is often desirable to simultaneously handle several objectives and constraints. In some cases, these objectives and constraints are often non-commensurable and the objective functions are explicitly/mathematically not available. In this paper, Interactive Evolutionary Computation (IEC) is used to effectively handle these identification problems. IEC is an o...