Enhance Estimation Results Using Neuro Fuzzy Modeling techniques & Black Box Adaptation

Mr.N.Suresh

doi:https://doi.org/10.14445/23488387/IJCSE-V3I5P105

Research Article | Open Access

Volume 3 | Issue 5 | Year 2016 | Article Id. IJCSE-V3I5P105 | DOI : https://doi.org/10.14445/23488387/IJCSE-V3I5P105

Enhance Estimation Results Using Neuro Fuzzy Modeling techniques & Black Box Adaptation

Mr.N.Suresh, Dr.D.Manimegalai

Citation :

Mr.N.Suresh, Dr.D.Manimegalai, "Enhance Estimation Results Using Neuro Fuzzy Modeling techniques & Black Box Adaptation," International Journal of Computer Science and Engineering , vol. 3, no. 5, pp. 31-33, 2016. Crossref, https://doi.org/10.14445/23488387/IJCSE-V3I5P105

Abstract

Present Estimation techniques does not gives desirable results with present COCOMO models, normal neural networks etc. These techniques are expensive and time taken process in estimation of metrics results. Its gives some errors result. These errors show with covariance matrices representation process. These error estimation results give the good guidance to control the errors in previous projects. Now here we introduce neuro fuzzy modeling technique in estimation of cost and size. New estimation approach works based on black box adaptation. In each and every stage apply the statistical performance identification. Statistical performance identification works as a prediction model. These types of prediction models are gives the good guidance to enhance the estimation of performance results in implementation. Using neuro fuzzy modeling start the empirical process show the good efficiency compares to previous all techniques.

Keywords

Black box adaptation, neuro fuzzy modeling, margin technology.

References

1. Software Evolution Prediction Using Seasonal Time Analysis: a Comparative Study, Miguel Goulão, Nelson Fonte, Michel Wermelinger, Fernando Brito e Abreu,2012
2. An Adaptive Learning Approach to Software Cost Estimation, Anupama Kaushik, A.K. Soni, Rachna Soni,2012 3. www.rand.org
4. Distribution Systems - Program 180
5. Software Cost Estimating
6. Application of Adaptive Artificial Neural Network Method to Model the Excitation Currents of Synchronous Motors, Ramazan Bayindir, Ilhami Colak, Seref Sagiroglu, Hamdi Tolga Kahraman,2012.
7. Early Effort Estimation by AHP: A Case Study of Project Metrics in Small Organizations,2012, Yan Zhang∗ †, Xiaokun Zhang∗ , Xuying Zhao∗ and Tian Zhang‡†1
8. Meta-heuristic linear modeling technique for estimating excitation current of synchronous motor, Dr.Hamdi Tolga Kahraman, 2009
9. Neural-Network-Based Model Reference Adaptive Systems for High-Performance Motor Drives and Motion Controls, Malik E. Elbuluk, Liu Tong, and Iqbal Husain,2002.
10. A Novel Neural Network Approach For Software Cost Estimation Using Functional Link Artificial Neural Network (FLANN), B.Tirumal rao, B. Sameet, G. Kiran Swathi, K. Vikram Gupta, Ch. RaviTeja, S.Sumana,2010 11. Estimating Development Time of Software Projects Using a Neuro Fuzzy Approach, 2008.