Modular neural networks for MAP classification of time series and the partition algorithm

1996 ◽  
Vol 7 (1) ◽  
pp. 73-86 ◽  
Author(s):  
V. Petridis ◽  
A. Kehagias
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Modular Neural Networks

scholarly journals ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Eva Volna ◽  
Martin Kotyrba ◽  
Hashim Habiballa
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Ecg Signals ◽  
Detection Systems ◽  
Language Interpretation ◽  
Different Types ◽  
Time Courses ◽  
Natural Language Interpretation ◽  
Unique Capability

The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.

Modular Neural Networks with Fuzzy Integration Applied to Time Series Prediction

2006 ◽  
pp. 241-250
Author(s):  
Patricia Melin ◽  
Ileana Leal ◽  
Valente Ochoa ◽  
Luis Valenzuela ◽  
Gabriela Torres ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Series Prediction ◽  
Modular Neural Networks

scholarly journals Convolutional Neural Network Classification of Telematics Car Driving Data

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 6 ◽  
Author(s):  
Guangyuan Gao ◽  
Mario Wüthrich
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Convolutional Neural Network ◽  
High Frequency ◽  
Location Data ◽  
Neural Network Classification ◽  
Feature Information ◽  
Car Driving

The aim of this project is to analyze high-frequency GPS location data (second per second) of individual car drivers (and trips). We extract feature information about speeds, acceleration, deceleration, and changes of direction from this high-frequency GPS location data. Time series of this feature information allow us to appropriately allocate individual car driving trips to selected drivers using convolutional neural networks.

Time-Series Segmentation Using Predictive Modular Neural Networks

1997 ◽  
Vol 9 (8) ◽  
pp. 1691-1709 ◽  
Author(s):  
Athanasios Kehagias ◽  
Vassilios Petridis
Keyword(s):  
Time Series ◽  
Source Identification ◽  
Predictive Accuracy ◽  
Identification Algorithm ◽  
Time Series Classification ◽  
Time Step ◽  
Modular Neural Networks ◽  
Time Series Segmentation ◽  
Network Method

A predictive modular neural network method is applied to the problem of unsupervised time-series segmentation. The method consists of the concurrent application of two algorithms: one for source identification, the other for time-series classification. The source identification algorithm discovers the sources generating the time series, assigns data to each source, and trains one predictor for each source. The classification algorithm recursively computes a credit function for each source, based on the competition of the respective predictors, according to their predictive accuracy; the credit function is used for classification of the time-series observation at each time step. The method is tested by numerical experiments.

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