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A comparison of the predictive capabilities of artificial neural networks and regression models for knowledge discovery
In this paper, Artificial Neural Networks (ANN) and Regression Analysis models were considered to determine which of them performs better. Prediction was done using one hidden layer and three processing elements in the ANN model. Furthermore, prediction was done using regression analysis. The parame...
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2013
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| LEADER | 00000njm a2000000a 4500 | ||
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| 001 | oai:repository.ui.edu.ng:123456789/11356 | ||
| 042 | |a dc | ||
| 720 | |a Ojo, A. K. |e author | ||
| 720 | |a Adeyemo, A. B. |e author | ||
| 260 | |c 2013 | ||
| 520 | |a In this paper, Artificial Neural Networks (ANN) and Regression Analysis models were considered to determine which of them performs better. Prediction was done using one hidden layer and three processing elements in the ANN model. Furthermore, prediction was done using regression analysis. The parameters of regression model were estimated using Least Square method. To determine the better prediction, mean square errors (MSE) attached to ANN and regression models were used. Seven real series were fitted and predicted with in both models. It was found out that the mean square error attached to ANN model was smaller than regression model which made ANN a better model in prediction. | ||
| 024 | 8 | |a 2167-1710 | |
| 024 | 8 | |a ui_art_ojo_comparison_2013 | |
| 024 | 8 | |a Computing, Information Systems, Development Informatics and Allied Research Journal 4(2), pp. 15-22 | |
| 024 | 8 | |a https://repository.ui.edu.ng/handle/123456789/11356 | |
| 653 | |a Artificial Neural Networks | ||
| 653 | |a Regression | ||
| 653 | |a Least Square | ||
| 653 | |a Processing Element | ||
| 653 | |a Hidden Layer | ||
| 653 | |a Mean Square Error | ||
| 245 | 0 | 0 | |a A comparison of the predictive capabilities of artificial neural networks and regression models for knowledge discovery |