Adaptive Interaction and Its Application to Neural Networks

Adaptive interaction is a new approach to introduce adaptability into man made systems In this approach a system is decomposed into interconnected subsystems that we call devices and adaptation occurs in the interactions More precisely interac tion weights among these devices will be adapted in order to achieve the objective of minimizing a given cost function The adaptation algorithm developed is mathemati cally equivalent to a gradient descent algorithm but requires only local information in its implementation One particular application of adaptive interaction that we study in this paper is in neural networks By applying adaptive interaction we can achieve essentially the same adaptation as that using the well known back propagation algo rithm but without the need of a feedback network to propagate the errors which has many advantages in practice A simulation is provided to show the e ectiveness of our approach