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

International Journal of Computer Science and Engineering
© 2016 by SSRG - IJCSE Journal
Volume 3 Issue 5
Year of Publication : 2016
Authors : Mr.N.Suresh, Dr.D.Manimegalai
: 10.14445/23488387/IJCSE-V3I5P105

pdf
Citation:
MLA Style:

Mr.N.Suresh, Dr.D.Manimegalai, "Enhance Estimation Results Using Neuro Fuzzy Modeling techniques & Black Box Adaptation" SSRG International Journal of Computer Science and Engineering 3.5 (2016): 31-33.

APA Style:

Mr.N.Suresh, Dr.D.Manimegalai, (2016). Enhance Estimation Results Using Neuro Fuzzy Modeling techniques & Black Box Adaptation. SSRG International Journal of Computer Science and Engineering 3.5, 31-33.

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.

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.

Key Words:

Black box adaptation, neuro fuzzy modeling, margin technology.