Identification of Continuous-time Models from Sampled Data presents an up-to-date view of this active area of research, describing recent methods and software tools and offering new results in areas such as: time and frequency domain optimal statistical approaches to identification; parametric identification for linear, nonlinear and stochastic systems; identification using instrumental variable, subspace and data compression methods; closed-loop and robust identification; and continuous-time modeling from non-uniformly sampled data and for systems with delay.

The CONTSID toolbox discussed in the final chapter gives an overview of developments and practical examples in which MATLAB^® can be used for direct time-domain identification of continuous-time systems. A valuable reference for a broad audience drawn from researchers and graduate students in signal processing as well as in systems and control this book also covers material suitable for specialised graduate courses in these areas.