Abstract
In this chapter, the application of nature-inspired paradigms on system identification is discussed. A review of the recent applications of techniques such as genetic algorithms, genetic programming, immuno-inspired algorithms, and particle swarm optimization to the system identification is presented, discussing the application to linear, nonlinear, time invariant, time variant, monovariable, and multivariable cases. Then the application of an immuno-inspired algorithm to solve the linear time variant multivariable system identification problem is detailed with examples and comparisons to other methods. Finally, the future directions of the application of nature-inspired paradigms to the system identification problem are discussed, followed by the chapter conclusions.
Reference25 articles.
1. State Space Modeling of Time Series
2. Barreto, G. (2002). Modelagem computacional distribuída e paralela de sistemas e de séries temporais multivariáveis no espaço de estado (PhD thesis). Universidade Estadual de Campinas.
3. Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials
4. Cutello, V., Narizi, G., Nicosia, G., & Pavone, M. (2006). Real coded clonal selection algorithm for global numerical optimization using a new inversely proportional hypermutation operator. In 21st Annual ACM Symposium on Applied Computing, (pp. 950–954). Academic Press.
5. Cutello, V., & Nicosia, G. (2002). An immunological approach to combinatorial optimization problems. In Advances in Artificial Intelligence IBERAMIA, (pp. 361-370). Academic Press.