Gradient-based morphological approach for software development cost estimation

In this paper we present a gradient-based morphological approach to solve the software development cost estimation (SDCE) problem. The proposed approach consists of a dilation-erosion perceptron (DEP) trained by a gradient steepest descent method using the back propagation (BP) algorithm and a systematic approach to overcome the problem of nondifferentiability of morphological operators. Furthermore, we compare the proposed approach with other neural and statistical models using five complex SDCE problems.