Abstract
In this article, three algorithms for training a general regression neural network (GRNN) will be presented. The first uses a nature-inspired optimization approach known as particle swarm optimization (PSO), while the latter two, i.e. the plug-in and the cross-validation, are based on classical mathematical methods, including the theory of kernel density estimators. The aforementioned algorithms will be applied to determining network smoothing parameters—which is the main task in GRNN learning. The trained GRNN will undergo benchmarking on repository data sets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kowalski, P.A., Kusy, M.: Determining the significance of features with the use of sobol’ method in probabilistic neural network classification tasks. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Federated Conference on Computer Science and Information Systems 2017 (FedCSIS 2017). Annals of Computer Science and Information Systems, vol. 11, pp. 39–48. IEEE, Prague (Czech Republic) (2017)
Kowalski, P.A., Kusy, M.: Determining significance of input neurons for probabilistic neural network by sensitivity analysis procedure. Computational Intelligence 34(3), 895–916 (2018)
Kowalski, P.A., Kusy, M.: Sensitivity analysis for probabilistic neural network structure reduction. IEEE Transactions on Neural Networks and Learning Systems 29(5), 1919–1932 (2018)
Kusy, M.: Dimensionality reduction for probabilistic neural network in medical data classification problems. International Journal Of Electronics and Telecommunications 61(3), 293–304 (2015)
Kusy, M., Kluska, J.: Assessment of prediction ability for reduced probabilistic neural network in data classification problems. Soft Computing 21(1), 199–212 (2017)
Kusy, M., Kowalski, P.A.: Weighted probabilistic neural network. Information Sciences 430–431, 65–76 (2018)
Li, C., Bovik, A.C., Wu, X.: Blind image quality assessment using a general regression neural network. IEEE Trans. Neural Networks 22(5), 793–799 (2011)
Liang, Y., Niu, D., Hong, W.C.: Short term load forecasting based on feature extraction and improved general regression neural network model. Energy 166, 653–663 (2019)
Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
Marini, F., Walczak, B.: Particle swarm optimization (PSO). A tutorial. Chemom. Intell. Lab. Syst. 149, 153–165 (2015)
Silverman, B.W.: Monographs on statistics and applied probability. Density Estim. Stat. Data Anal. 26, (1986)
Specht, D.F.: Probabilistic neural networks for classification, mapping, or associative memory. IEEE Int. Conf. Neural Netw. 1, 525–532 (1988)
Specht, D.F.: Probabilistic neural networks. Neural Networks 3(1), 109–118 (1990)
Specht, D.F., et al.: A general regression neural network. IEEE Trans. Neural Networks 2(6), 568–576 (1991)
Wand, M.P., Jones, M.C.: Kernel smoothing. Chapman and Hall/CRC (1994)
Xie, Y., Li, C., Lv, Y., Yu, C.: Predicting lightning outages of transmission lines using generalized regression neural network. Appl. Soft Comput. 78, 438–446 (2019)
Acknowledgements
This article is partially financed by Grant for Statutory Activity from Faculty of Physics and Applied Computer Science of the AGH University of Science and Technology and also by Department of Electronics Fundamentals, Rzeszow University of Technology, within the subsidy for maintaining research potential (UPB).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Kowalski, P.A., Kusy, M. (2022). Algorithms for Triggering General Regression Neural Network. In: Harmati, I.Á., Kóczy, L.T., Medina, J., Ramírez-Poussa, E. (eds) Computational Intelligence and Mathematics for Tackling Complex Problems 3. Studies in Computational Intelligence, vol 959. Springer, Cham. https://doi.org/10.1007/978-3-030-74970-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-74970-5_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-74969-9
Online ISBN: 978-3-030-74970-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)