Neuro-Fuzzy Algorithmic (NFA) Models and Tools for Estimation

Accurate estimation such as cost estimation, quality estimation and risk analysis is a major issue in management. We propose a patent pending soft computing framework to tackle this challenging problem. Our generic framework is independent of the nature and type of estimation. It consists of neural network, fuzzy logic, and an algorithmic estimation model. We made use of the Constructive Cost Model (COCOMO), Analysis of Variance (ANOVA), and Function Point Analysis as the algorithmic models and validated the accuracy of the Neuro-Fuzzy Algorithmic (NFA) Model in software cost estimation using industrial project data. Our model produces more accurate estimation than using an algorithmic model alone. We also discuss the prototypes of our tools that implement the NFA Model. We conclude with our roadmap and direction to enrich the model in tackling different estimation challenges. 1. Introducing the NFA Model The soft computing framework, or NFA Model as presented in Figure 1, consists of the following components: • Pre-Processing Neuro-Fuzzy Inference System (PNFIS) used to resolve the effect of dependencies among contributing factors of the estimation problem, and to produce adjusted rating values for these factors, • Neuro-Fuzzy Bank (NFB) used to calibrate the contributing factors by mapping the adjusted rating values for these factors to generate their corresponding numerical parameter values, • Module that applies an algorithmic model relevant to the nature of the estimation problem to produce one or more output metrics. where N is the number of contributing factors, M is the number of other variables in the Algorithmic Model, RF is Factor Rating, ARF is Adjusted Factor Rating, NFB is the Neuro-Fuzzy Bank, FM is Numerical Factor/Multiplier for input to the Algorithmic Model, V is input to the Algorithmic Model, and Mo is Output Metric. Figure 1. Neuro-Fuzzy Algorithmic (NFA) Model for Estimation FM2 ... NFB1 NFBN Algorithmic Model NFB2 Output Metric Mo FM1