<abstract xmlns="http://www.w3.org/1999/xhtml"><p> In a general regression neural network (GRNN), the number of neurons in the pattern layer is proportional to the number of training samples in the dataset. The use of a GRNN in applications that have relatively large datasets becomes troublesome due to the architecture and speed required. The great number of neurons in the pattern layer requires a substantial increase in memory usage and causes a substantial decrease in calculation speed. Therefore, there is a strong need for pattern layer size reduction. In this study, a self-organizing map (SOM) structure is introduced as a pre-processor for the GRNN. First, an SOM is generated for the training dataset. Second, each training record is labelled with the most similar map unit. Lastly, when a new test record is applied to the network, the most similar map units are detected, and the training data that have the same labels as the detected units are fed into the network instead of the entire training dataset. This scheme enables a considerable reduction in the pattern layer size. The proposed hybrid model was evaluated by using fifteen benchmark test functions and eight different UCI datasets. According to the simulation results, the proposed model significantly simplifies the GRNN’s structure without any performance loss.</p></abstract>

In a general regression neural network (GRNN), the number of neurons in the pattern layer is proportional to the number of training samples in the dataset. The use of a GRNN in applications that have relatively large datasets becomes troublesome due to the architecture and speed required. The great number of neurons in the pattern layer requires a substantial increase in memory usage and causes a substantial decrease in calculation speed. Therefore, there is a strong need for pattern layer size reduction. In this study, a self-organizing map (SOM) structure is introduced as a pre-processor for the GRNN. First, an SOM is generated for the training dataset. Second, each training record is labelled with the most similar map unit. Lastly, when a new test record is applied to the network, the most similar map units are detected, and the training data that have the same labels as the detected units are fed into the network instead of the entire training dataset. This scheme enables a considerable reduction in the pattern layer size. The proposed hybrid model was evaluated by using fifteen benchmark test functions and eight different UCI datasets. According to the simulation results, the proposed model significantly simplifies the GRNN’s structure without any performance loss.

Pattern Layer Reduction for a Generalized Regression Neural Network by Using a Self–Organizing Map

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

In a general regression neural network (GRNN), the number of neurons in the pattern layer is proportional to the number of training samples in the dataset. The...

Pattern Layer Reduction for a Generalized Regression Neural Network by Using a Self–Organizing Map

Publié en ligne: 29 juin 2018

Pages: 411 - 424

Reçu: 27 avr. 2017

Accepté: 25 oct. 2017

DOI: https://doi.org/10.2478/amcs-2018-0031

Mots clés
generalized regression neural network, artificial neural network, self-organizing maps, nearest neighbour, reduced dataset

© 2018 Serkan Kartal, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Pattern Layer Reduction for a Generalized Regression Neural Network by Using a Self–Organizing Map

Publié en ligne: 29 juin 2018

Pages: 411 - 424

Reçu: 27 avr. 2017

Accepté: 25 oct. 2017

DOI: https://doi.org/10.2478/amcs-2018-0031

Mots clésgeneralized regression neural network, artificial neural network, self-organizing maps, nearest neighbour, reduced dataset

© 2018 Serkan Kartal, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
generalized regression neural network, artificial neural network, self-organizing maps, nearest neighbour, reduced dataset