Software Performance Estimate using Fuzzy Based Backpropagation Learning

With the rapid development of computer technology, it is becoming increasingly important in everyday activity of an individual resulting in increase of dependability on computer technology. Therefore, every individual uses a software application based on the different parameters with performance being of high priority. An individual starts relying on software based on the performance which makes it very important from user’s perspective. Software performance prediction modeling techniques are the basis ABSTRACT

[1]  Yan Shi,et al.  A new approach of neuro-fuzzy learning algorithm for tuning fuzzy rules , 2000, Fuzzy Sets Syst..

[2]  Xizhao Wang,et al.  OFFSS: optimal fuzzy-valued feature subset selection , 2003, IEEE Trans. Fuzzy Syst..

[3]  Ali Behforooz,et al.  Software engineering fundamentals , 1996 .

[4]  Thomas I. Strasser,et al.  Artificial neural networks for fault detection in large-scale data acquisition systems , 2004, Eng. Appl. Artif. Intell..

[5]  C. P. Wang,et al.  An intelligent sales forecasting system through integration of artificial neural networks and fuzzy neural networks with fuzzy weight elimination , 2002, Neural Networks.

[6]  Sushmita Mitra,et al.  Neuro-fuzzy rule generation: survey in soft computing framework , 2000, IEEE Trans. Neural Networks Learn. Syst..

[7]  Sung-Kwun Oh,et al.  The hybrid multi-layer inference architecture and algorithm of FPNN based on FNN and PNN , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[8]  Hongxing Li,et al.  Efficient learning algorithms for three-layer regular feedforward fuzzy neural networks , 2004, IEEE Trans. Neural Networks.

[9]  Xiaoming Xu,et al.  Design neural networks based fuzzy logic , 2000, Fuzzy Sets Syst..

[10]  Mario Piattini,et al.  A Metric-Based Approach for Predicting Conceptual Data Models Maintainability , 2001, Int. J. Softw. Eng. Knowl. Eng..

[11]  Chia-Feng Juang,et al.  A fuzzified neural fuzzy inference network for handling both linguistic and numerical information simultaneously , 2007, Neurocomputing.

[12]  Antonio A. Márquez,et al.  A multi-objective evolutionary algorithm with an interpretability improvement mechanism for linguistic fuzzy systems with adaptive defuzzification , 2010, International Conference on Fuzzy Systems.

[13]  Chin-Yu Huang,et al.  An Adaptive Reliability Analysis Using Path Testing for Complex Component-Based Software Systems , 2011, IEEE Transactions on Reliability.

[14]  Francisco Herrera,et al.  Integration of an Index to Preserve the Semantic Interpretability in the Multiobjective Evolutionary Rule Selection and Tuning of Linguistic Fuzzy Systems , 2010, IEEE Transactions on Fuzzy Systems.

[15]  Gianluca Bontempi,et al.  A data analysis method for software performance prediction , 2002, Proceedings 2002 Design, Automation and Test in Europe Conference and Exhibition.

[16]  Nikola K. Kasabov,et al.  DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction , 2002, IEEE Trans. Fuzzy Syst..

[17]  Sung-Kwun Oh,et al.  Fuzzy polynomial neural networks: hybrid architectures of fuzzy modeling , 2002, IEEE Trans. Fuzzy Syst..

[18]  Nikola K. Kasabov,et al.  On-line learning, reasoning, rule extraction and aggregation in locally optimized evolving fuzzy neural networks , 2001, Neurocomputing.

[19]  Rudy Setiono.  Extracting M-of-N rules from trained neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..

[20]  Wlodzislaw Duch,et al.  A new methodology of extraction, optimization and application of crisp and fuzzy logical rules , 2001, IEEE Trans. Neural Networks.

[21]  Taisook Han,et al.  Iterative inversion of fuzzified neural networks , 2000, IEEE Trans. Fuzzy Syst..

[22]  Márta Takács.  Approximate Reasoning in Fuzzy Systems Based on Pseudo-analysis and Uninorm Residuum , 2004 .