Symbolic function network

In this paper a model called symbolic function network (SFN) is introduced; that is based on using elementary functions (for example powers, the exponential function, and the logarithm) as building blocks. The proposed method uses these building blocks to synthesize a function that best fits the training data in a regression framework. The resulting network is of the form of a tree, where adding nodes horizontally means having a summation of elementary functions and adding nodes vertically means concatenating elementary functions. Several new algorithms were proposed to construct the tree based on the concepts of forward greedy search and backward greedy search, together with applying the steepest descent concept. The method is tested on a number of examples and it is shown to exhibit good performance.