Global parameters estimation and convergence proof of isomorphic networks using historical data

In recent years, the wireless sensors networks raise a great attention in the world. In this paper we proposed a method-multi-innovation coupled stochastic gradient (MICSG) algorithm for the global parameters estimation of the distributed sensors. This algorithm utilizes the identified result of the previous adjacent node and the local historical data to modify own estimated parameters. Then we make a proof of parameters convergence of proposed algorithm. Two examples are presented in the simulation. The first example concerns the influence of different length of historical data to the convergence rate and error rate. The second one exhibits the method applying the structure healthy management. Simulation shows that increasing the length of multi-innovation vector can improve the convergence effect and accelerate the convergence rate in a certain range.