The institutional determinants of CO2 emissions: a computational modeling approach using Artificial Neural Networks and Genetic Programming

2011 ◽  
Vol 22 (1) ◽  
pp. 42-49 ◽  
Author(s):  
Marcos Álvarez-Díaz ◽  
Gonzalo Caballero-Miguez ◽  
Mario Soliño
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Genetic Programming ◽  
Computational Modeling ◽  
Co2 Emissions ◽  
Modeling Approach ◽  
Institutional Determinants ◽  
Artificial Neural

Using Genetic Programming to Extract Knowledge from Artificial Neural Networks

2008 ◽  
pp. 308-327
Author(s):  
Daniel Rivero ◽  
Miguel Varela ◽  
Javier Pereira
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Genetic Programming ◽  
Recurrent Neural Networks ◽  
Know How ◽  
Artificial Neural

A technique is described in this chapter that makes it possible to extract the knowledge held by previously trained artificial neural networks. This makes it possible for them to be used in a number of areas (such as medicine) where it is necessary to know how they work, as well as having a network that functions. This chapter explains how to carry out this process to extract knowledge, defined as rules. Special emphasis is placed on extracting knowledge from recurrent neural networks, in particular when applied in predicting time series.

EXTENDED DAILY EXCHANGE RATES FORECASTS USING WAVELET TEMPORAL RESOLUTIONS

2005 ◽  
Vol 01 (01) ◽  
pp. 79-107 ◽  
Author(s):  
MAK KABOUDAN
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Exchange Rate ◽  
Random Walk ◽  
Genetic Programming ◽  
Exchange Rates ◽  
Us Dollar ◽  
Artificial Neural ◽  
Japanese Yen ◽  
The One

Applying genetic programming and artificial neural networks to raw as well as wavelet-transformed exchange rate data showed that genetic programming may have good extended forecasting abilities. Although it is well known that most predictions of exchange rates using many alternative techniques could not deliver better forecasts than the random walk model, in this paper employing natural computational strategies to forecast three different exchange rates produced two extended forecasts (that go beyond one-step-ahead) that are better than naïve random walk predictions. Sixteen-step-ahead forecasts obtained using genetic programming outperformed the one- and sixteen-step-ahead random walk US dollar/Taiwan dollar exchange rate predictions. Further, sixteen-step-ahead forecasts of the wavelet-transformed US dollar/Japanese Yen exchange rate also using genetic programming outperformed the sixteen-step-ahead random walk predictions of the exchange rate. However, random walk predictions of the US dollar/British pound exchange rate outperformed all forecasts obtained using genetic programming. Random walk predictions of the same three exchange rates employing raw and wavelet-transformed data also outperformed all forecasts obtained using artificial neural networks.

Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks

2010 ◽  
Vol 36 (5) ◽  
pp. 620-627 ◽  
Author(s):  
Mohammad Ali Ghorbani ◽  
Rahman Khatibi ◽  
Ali Aytek ◽  
Oleg Makarynskyy ◽  
Jalal Shiri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Genetic Programming ◽  
Water Level ◽  
Sea Water ◽  
Artificial Neural
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