Add to ORKGA nonlinear adaptive time series predictor has been developed using a new type of piecewise linear (PWL) network for its underlying model structure. The PWL Network is a D-FANN (Dynamical Functional Artificial Neural Network) the activation functions of which are piecewise linear. The new realization is presented with the associated training algorithm. Properties and characteristics are discussed. This network has been successfully used to model and predict an important class of highly dynamic and nonstationary signals, namely speech signals.Fil: Figueroa, Jose Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones en Ingeniería Eléctrica "Alfredo ...
This paper reports about a comparative study on several linear and nonlinear feedforward and recurre...
For prediction of nonlinear and nonstationary signals, as well as in nonlinear system identification...
Abstract—The paper is devoted to time series prediction using linear, perceptron and Elman neural ne...
Abstract — A nonlinear adaptive time series predictor has been developed using a new type of piecewi...
Time series prediction is a very important technology in a wide variety of field. The actual time se...
Abstract: In this paper we describe a neural network for the nonlinear adaptive prediction of non-st...
An attempt has been made to establish a nonlinear dynamic discrete-time neuron model, the so called ...
New learning algorithms for an adaptive nonlinear forward predictor that is based on a pipelined rec...
In this work, dynamic neural networks are evaluated as non-linear models for efficient prediction of...
Nonlinear techniques for signal processing and recognition have the promise of achieving systems whi...
金沢大学大学院自然科学研究科情報システムA nonlinear time series predictor was proposed, in which a nonlinear sub-predict...
A novel linearised Recursive Least Squares (LRLS) learning algorithm is presented for an adaptive no...
The problem of predicting nonlinear and nonstationary signals is complex since the physical law that...
The analysis of an observed univariate time series is often undertaken in order to get a prediction ...
金沢大学大学院自然科学研究科情報システムTime series prediction is very important technology in a wide variety of fields....
This paper reports about a comparative study on several linear and nonlinear feedforward and recurre...
For prediction of nonlinear and nonstationary signals, as well as in nonlinear system identification...
Abstract—The paper is devoted to time series prediction using linear, perceptron and Elman neural ne...
Abstract — A nonlinear adaptive time series predictor has been developed using a new type of piecewi...
Time series prediction is a very important technology in a wide variety of field. The actual time se...
Abstract: In this paper we describe a neural network for the nonlinear adaptive prediction of non-st...
An attempt has been made to establish a nonlinear dynamic discrete-time neuron model, the so called ...
New learning algorithms for an adaptive nonlinear forward predictor that is based on a pipelined rec...
In this work, dynamic neural networks are evaluated as non-linear models for efficient prediction of...
Nonlinear techniques for signal processing and recognition have the promise of achieving systems whi...
金沢大学大学院自然科学研究科情報システムA nonlinear time series predictor was proposed, in which a nonlinear sub-predict...
A novel linearised Recursive Least Squares (LRLS) learning algorithm is presented for an adaptive no...
The problem of predicting nonlinear and nonstationary signals is complex since the physical law that...
The analysis of an observed univariate time series is often undertaken in order to get a prediction ...
金沢大学大学院自然科学研究科情報システムTime series prediction is very important technology in a wide variety of fields....
This paper reports about a comparative study on several linear and nonlinear feedforward and recurre...
For prediction of nonlinear and nonstationary signals, as well as in nonlinear system identification...
Abstract—The paper is devoted to time series prediction using linear, perceptron and Elman neural ne...