Abstract
With the development of new computing paradigms, such as neural networks and genetic algorithms, new tools have become available in computer-aided systems theory. These tools can be used to tackle problems that are considered “hard” in traditional systems theory, like the modeling and identification of nonlinear dynamical systems.
We present a general methodology based on neural networks and genetic algorithms that can be applied to a wide range of problems. The main emphasis is on using the approximation capabilities of neural networks to model systems based on their input-output behavior.
We first show how the inverse problem of a static system can be solved by two feedforward neural networks in a feedback loop.
We then present a general methodology for modeling nonlinear systems with known rank (i.e., state space dimension) by feedforward networks with external delay units.
We further show how genetic algorithms can be employed to find neural networks to model dynamical systems of unknown rank. Two genetic algorithms are presented for this case: one that determines the best feed-forward network with external delay, and one that searches for a network with arbitrary topology and memory cells within each neuron.
This work was supported by the RWC Partnership project “Hybrid Evolutionary Programming”.
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© 1996 Springer-Verlag Berlin Heidelberg
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Jacak, W., Dreiseitl, S. (1996). Hybrid evolutionary programming: The tools for CAST. In: Pichler, F., Díaz, R.M., Albrecht, R. (eds) Computer Aided Systems Theory — EUROCAST '95. EUROCAST 1995. Lecture Notes in Computer Science, vol 1030. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0034768
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DOI: https://doi.org/10.1007/BFb0034768
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