Nonlinear System Identification Using Probabilistic Universal Learning Networks

Nonlinear System Identification Using Spatiotemporal Neural Networks

The so-called spatiotemporal neural network is considered. This is a neural network where the conventional weight multiplication operation is replaced by a linear filtering operation [l]. A training algorithm is derived for such networks. The problem of nonlinear system identification is considered as an application for spatiotemporal networks. Nonlinear system identification is one of the chal...

Nonlinear System Identification and Control Using Local Model Networks

Nonlinear System Identification Using Rbf Networks with Linear Input Connections

This paper presents a modified RBF network with additional linear input connections together with a hybrid training algorithm. The training algorithm is based on kmeans clustering with square root updating method and Givens least squares algorithm with additional linear input connections features. Two real data sets have been used to demonstrate the capability of the proposed RBF network archit...

Nonlinear System Identification using Discrete-time Neural Networks with Stable Learning Algorithm

This paper presents a stable neural sytem identification for nonlinear systems. An input output discrete time representation is considered. No a priori knowledge about the nonlinearities of the system is assumed. The proposed learning rule is the backpropagation algorithm under the condition that the learning rate belongs to a specified range defining the stability domain. Satisfying such condi...

Ship Identification Using Probabilistic Neural Networks

This paper proposed ship classification based on covariance of discrete wavelet using probability Neural Network A set of ship profiles are used to build a covariance matrix by discrete wavelet transform using Neural Network. It is found that this method for ship classifier design offers good class discriminacy when trained with 5 ship classes. This method can discriminate noisy ship very well....