Active control of noise transmission using self organizing neural network

The neural network has been applied to many engineering problems such as pattern recognition, optimization, system identification and control because of its nonlinear mapping capability. Although the network has a great ability to acquire solution for a given problems through learning, however, several problems are known such as the convergence to a local minimum solution, and the tuning of network parameter manually to obtain desired accuracy. In the present paper, we deal with the self-organizing neural network where the spatially scattered neurons are connected automatically using Cellular Automata. The network is applied to the active noise control problem as an adaptive system, where the model consists of a simply supported plate embedded in an infinite rigid baffle above a rectangular cavity. The fully coupled acoustic-plate interaction equations including characteristics of piezoelectric transducers are solved using time-varying Green's function techniques, and the performance of the controller is discussed through numerical investigation as well as experiment.