Soft computing (immune networks) in artificial intelligence

Proposes a reactive distributed artificial intelligence (dynamic) using immune networks and other soft computing methods. Firstly, extended soft computing is defined by adding immune networks and chaos theory including fractals and wavelets to conventional soft computing which is the fusion or combination of fuzzy systems, neural networks and genetic algorithms and is suited to cognitive distributed artificial intelligence (static). Next, a novel fuzzy neural net (general parameter radial based function neural network) is developed in order to use it for communication among agents in immune networks. The general parameter method is extended to an adaptive structured general algorithm to obtain much faster convergence rate. An unbiasedness criterion using distorter (a radial based function network) in order to optimize parameters resulting in the reactive distributed artificial intelligence kind of GMDH is applied to obtain better generalization properties. Then, this developed neural net is extended to obtain high performance