TY - GEN

T1 - Hybrid evolutionary programming

T2 - 5th International Workshop on Computer Aided Systems Theory, EUROCAST 1995

AU - Jacak, Witold

AU - Dreiseitl, Stephan

PY - 1996

Y1 - 1996

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84949008840&partnerID=8YFLogxK

U2 - 10.1007/bfb0034768

DO - 10.1007/bfb0034768

M3 - Conference contribution

AN - SCOPUS:84949008840

SN - 9783540607489

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 289

EP - 304

BT - Computer Aided Systems Theory - EUROCAST 1995 - A Selection of Papers from the 5th International Workshop on Computer Aided Systems Theory, 1995, Proceedings

A2 - Pichler, Franz

A2 - Moreno-Diaz, Roberto

A2 - Albrecht, Rudolf

PB - Springer

Y2 - 22 May 1995 through 25 May 1995

ER -