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Symbolic Function Network: Theory and Implementation

Symbolic Function Network: Theory and Implementation

George S. Eskander, Amir Atiya
Copyright: © 2013 |Pages: 32
ISBN13: 9781466621756|ISBN10: 1466621753|EISBN13: 9781466621763
DOI: 10.4018/978-1-4666-2175-6.ch014
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MLA

Eskander, George S., and Amir Atiya. "Symbolic Function Network: Theory and Implementation." Artificial Higher Order Neural Networks for Modeling and Simulation, edited by Ming Zhang, IGI Global, 2013, pp. 293-324. https://doi.org/10.4018/978-1-4666-2175-6.ch014

APA

Eskander, G. S. & Atiya, A. (2013). Symbolic Function Network: Theory and Implementation. In M. Zhang (Ed.), Artificial Higher Order Neural Networks for Modeling and Simulation (pp. 293-324). IGI Global. https://doi.org/10.4018/978-1-4666-2175-6.ch014

Chicago

Eskander, George S., and Amir Atiya. "Symbolic Function Network: Theory and Implementation." In Artificial Higher Order Neural Networks for Modeling and Simulation, edited by Ming Zhang, 293-324. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2175-6.ch014

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Abstract

This chapter reviews a recent HONN-like model called Symbolic Function Network (SFN). This model is designed with the goal to impart more flexibility than both traditional and HONNs neural networks. The main idea behind this scheme is the fact that different functional forms suit different applications and that no specific architecture is best for all. Accordingly, the model is designed as an evolving network that can discover the best functional basis, adapt its parameters, and select its structure simultaneously. Despite the high modeling capability of SFN, it is considered as a starting point for developing more powerful models. This chapter aims to open a door for researchers to propose new formulations and techniques that impart more flexibility and result in sparser and more accurate models. Through this chapter, the theoretical basis of SFN is discussed. The model optimization computations are deeply illustrated to enable researchers to easily implement and test the model.

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