Analysis of a nonlinear system via internal-state of a neural network

Summary form only given. The internal state of a network has been inspected to evaluate the performance of the network. In particular, the weight vectors of the network have been applied for the analysis of a time series such as biological signals and nonstationary signals. The complexity (eg. nonlinearity and nonstationarity) of such signals often makes it a challenging task to use them in the signal processing field. In this paper, we propose a new neural network based technique to address these problems. We show that a feed forward, multi-layered neural network can conveniently capture the parameter change of a nonlinear system in its connection weight-space, after a process of supervised training. The performance of the proposed method is investigated with a linear and nonlinear system simulated via a mathematical equation.