Feed-Forward and Recurrent Neural Networks in Signal Prediction
نویسندگان
چکیده
The paper is devoted to time series prediction using linear, perceptron and Elman neural networks of the proposed pattern structure. Signal wavelet de-noising in the initial stage is discussed as well. The main part of the paper is devoted to the comparison of different models of time series prediction. The proposed algorithm is applied to the real signal representing gas consumption.
منابع مشابه
Signal Prediction by Layered Feed - Forward Neural Network (RESEARCH NOTE).
In this paper a nonparametric neural network (NN) technique for prediction of future values of a signal based on its past history is presented. This approach bypasses modeling, identification, and parameter estimation phases that are required by conventional parametric techniques. A multi-layer feed forward NN is employed. It develops an internal model of the signal through a training operation...
متن کاملPREDICTION OF COMPRESSIVE STRENGTH AND DURABILITY OF HIGH PERFORMANCE CONCRETE BY ARTIFICIAL NEURAL NETWORKS
Neural networks have recently been widely used to model some of the human activities in many areas of civil engineering applications. In the present paper, artificial neural networks (ANN) for predicting compressive strength of cubes and durability of concrete containing metakaolin with fly ash and silica fume with fly ash are developed at the age of 3, 7, 28, 56 and 90 days. For building these...
متن کاملProtein Secondary Structure Prediction with Long Short Term Memory Networks
Prediction of protein secondary structure from the amino acid sequence is a classical bioinformatics problem. Common methods use feed forward neural networks or SVM’s combined with a sliding window, as these models does not naturally handle sequential data. Recurrent neural networks are an generalization of the feed forward neural network that naturally handle sequential data. We use a bidirect...
متن کاملPrediction of Permanent Earthquake-Induced Deformation in Earth Dams and Embankments Using Artificial Neural Networks
This research intends to develop a method based on the Artificial Neural Network (ANN) to predict permanent earthquake-induced deformation of the earth dams and embankments. For this purpose, data sets of observations from 152 published case histories on the performance of the earth dams and embankments, during the past earthquakes, was used. In order to predict earthquake-induced deformation o...
متن کاملNeural Prediction of Buckling Capacity of Stiffened Cylindrical Shells
Estimation of the nonlinear buckling capacity of thin walled shells is one of the most important aspects of structural mechanics. In this study the axial buckling load of 132 stiffened shells were numerically calculated. The applicability of artificial neural networks (ANN) in predicting the buckling capacity of vertically stiffened shells was studied. To this end feed forward (FF) multi-layer ...
متن کامل