Stochastic analysis of neural network modeling and identification of nonlinear memoryless MIMO systems
Neural Network (NN) approaches have been widely applied for modeling and identification of nonlinear Multiple-Input Multiple-Output (MIMO) systems. This paper proposes a stochastic analysis of a class of these NN algorithms. The class of MIMO systems considered in this paper is composed of a set of single-input nonlinearities followed by a linear combiner. The NN model consists of a set of single-input memoryless NN blocks followed by a linear combiner. A gradient descent algorithm is used for the learning process. Here the authors give analytical expressions for the Mean Squared Error (MSE), explore the stationary points of the algorithm, evaluate the misadjustment error due to weight fluctuations, and derive recursions for the mean weight transient behavior during the learning process.