The book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. Additionally, it provides the reader with the necessary background on optimization techniques making the book self-contained. The emphasis is put on modern methods based on neural networks and fuzzy systems without neglecting the classical approaches. This is supported by many illustrative figures.
Advanced mathematics is avoided.
Thus, the book is suitable for last year undergraduate and graduate courses as well as research and development engineers in industries. Introduction to Optimization 23 3. Linear Optimization 35 4. Nonlinear Local Optimization 79 5. Nonlinear Global Optimization 113 6.