Model Based Estimation

The high temperature solid oxide fuel cell (SOFC) is identified as one of the leading fuel cell technology contenders to capture the energy market in years to come. However, in order to operate as an efficient energy generating system, the SOFC requires an appropriate control system which in turn requires a detailed modelling of process dynamics.

Although LinSig started life as a design and modelling tool for individual traffic signal junctions it has been extended and improved many times to now be a fully fledged modelling tool for networks of signals as well as retaining the ability to model single junctions. A central idea behind LinSig is that it models traffic signal junctions in a similar way to how a real traffic signal controller actually works. This means that LinSig takes account of the features and constraints of the controlling equipment, thereby ensuring that all modelling accurately reflects how existing junctions work, and how any design proposals would operate if implemented.

This monograph is an exposition of a novel method for solving inverse problems, a method of parameter estimation for time series data collected from simulations of real experiments. These time series might be generated by measuring the dynamics of aircraft in flight, by the function of a hidden Markov model used in bioinformatics or speech recognition or when analyzing the dynamics of asset pricing provided by the nonlinear models of financial mathematics.