Immunized neural networks for complex system identification

The possibility of using artificial neural networks along with concepts from the field of immunology in the modeling of complex dynamic systems is addressed. Biological immune systems can be thought of as very robust systems, capable of dealing with an enormous variety of disturbances. They use a finite number of discrete building blocks to achieve this robustness. A technique which attempts to reproduce the robustness of a biological immune system in an artificial neural network is outlined