Abstract
The paper demonstrates that holon networks can be used effectively for the identification of nonlinear dynamical systems. The emphasis of the paper is on modeling of complicated systems which have a great deal of uncertainty and unknown interactions between their elements and parameters.
The concept of applying a quantitative model building, for example, to environmental or ecological systems is not new. In a previous paper we presented a holon network model as an another alternative to quantitative modeling. Holon networks have a hierarchical construction where each level of hierarchy consists of networks with reciprocal actions among their elements. The networks are able to evolve by self-organizing their structure and adapt their parameters to environments. This was achived by an autonomous decentralized adaptation method.
Holon networks, being constructed by a number of elements and hence having high degree of parameter freedom, have great flexibility of their functions. But, at the same time, such networks are computationally expensive.
In this paper we propose a new emergent evolution method to reduce the computation times. In this new evolution method the initial holon networks consists of only a few elements and it grows gradually with each new observation and autonomous criterion in order to fit their function to the environment. Some examples show that this method can lead to network structures which have sufficient flexibility and adapt well to various environments.
