A Neuro-Fuzzy Approach Based Voting Scheme for Fault Tolerant Systems Using Artificial Bee Colony Training

Authors: D. Uma Devi, P. Seetha Ramaiah

Abstract:

Voting algorithms are extensively used to make decisions in fault tolerant systems where each redundant module gives inconsistent outputs. Popular voting algorithms include majority voting, weighted voting, and inexact majority voters. Each of these techniques suffers from scenarios where agreements do not exist for the given voter inputs. This has been successfully overcome in literature using fuzzy theory. Our previous work concentrated on a neuro-fuzzy algorithm where training using the neuro system substantially improved the prediction result of the voting system. Weight training of Neural Network is sub-optimal. This study proposes to optimize the weights of the Neural Network using Artificial Bee Colony algorithm. Experimental results show the proposed system improves the decision making of the voting algorithms.

Keywords: Voting algorithms, Fault tolerance, Fault masking, Neuro-Fuzzy System (NFS), Artificial Bee Colony (ABC)

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1108100

References:

[1] Johnson, B. W. (1988). Design & analysis of fault tolerant digital systems. Addison-Wesley Longman Publishing Co., Inc..
[2] Latif-Shabgahi, G., Bass, J. M., & Bennett, S. (2004). A taxonomy for software voting algorithms used in safety-critical systems. Reliability, IEEE Transactions on, 53(3), 319-328.
[3] Latif-Shabgahi, G., Bennett, S., & Bass, J. M. (2003). Smoothing voter: a novel voting algorithm for handling multiple errors in fault-tolerant control systems. Microprocessors and Microsystems, 27(7), 303-313.
[4] Kwak, S. W., & You, K. H. (2004). Reliability Analysis and Fault Tolerance Strategy of TMR Real-time Control Systems. Journal of Institute of Control, Robotics and Systems, 10(8), 748-754.
[5] Kim, M. H., Lee, S., & Lee, K. C. (2008). Predictive hybrid redundancy using exponential smoothing method for safety critical systems. International Journal of Control Automation and Systems, 6(1), 126.
[6] Girault, A., Lavarenne, C., Sighireanu, M., & Sorel, Y. (2001, April). Generation of fault-tolerant static scheduling for real-time distributed embedded systems with multi-point links. In Parallel and Distributed Processing Symposium, International (Vol. 3, pp. 30125b-30125b). IEEE Computer Society.
[7] Dima, C., Girault, A., Lavarenne, C., & Sorel, Y. (2001). Off-line realtime fault-tolerant scheduling. In Parallel and Distributed Processing, 2001. Proceedings. Ninth Euromicro Workshop on (pp. 410-417). IEEE.
[8] Bala, N. International Journal of Advances In Computing And Information Technology.
[9] Latifi, Z., & Karimi, A. (2014). A TMR Genetic Voting Algorithm for Fault-tolerant Medical Robot. Procedia Computer Science, 42, 301-307.
[10] Saheb, P. B., Subbarao, K. & Dr. S.Phani kumar (2013). A Survey on Voting Algorithms Used In Safety Critical Systems
[11] Das, M. (2010). An approach towards History Based Weighted Average Voting Algorithm using Soft Dynamic Threshold (Doctoral dissertation, Jadavpur University Kolkata).
[12] Zarafshan, F., Latif-Shabgahi, G. R., & Karimi, A. (2010, July). Notice of Retraction A novel weighted voting algorithm based on neural networks for fault-tolerant systems. In Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on (Vol. 9, pp. 135-139). IEEE.
[13] Zhang, Y., Zhang, H., Cai, J., & Yang, B. (2014, May). A Weighted Voting Classifier Based on Differential Evolution. In Abstract and Applied Analysis (Vol. 2014). Hindawi Publishing Corporation.
[14] Karimi, A., & Zarafshan, F. (2010, June). An optimal parallel average voting for fault-tolerant control systems. In Networking and Information Technology (ICNIT), 2010 International Conference on (pp. 360-363). IEEE.
[15] Danecek, V., & Silhavy, P. (2011, August). The Fault-tolerant control system based on majority voting with Kalman filter. In Telecommunications and Signal Processing (TSP), 2011 34th International Conference on (pp. 472-477). IEEE.
[16] Ravindran, K., Rabby, M., & Adiththan, A. (2013, January). Autonomic management of replication in voting-based fault-tolerant data collection systems. In Communication Systems and Networks (COMSNETS), 2013 Fifth International Conference on (pp. 1-2). IEEE.
[17] Namazi, A., & Nourani, M. (2007, October). Distributed voting for fault-tolerant nanoscale systems. In Computer Design, 2007. ICCD 2007. 25th International Conference on (pp. 568-573). IEEE.
[18] Sui, J., Hua, Z., Yang, L., Tian, Y., & Zhang, Y. (2008, June). Adaptive fuzzy fault-tolerant voting mechanism based on EKF. In Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on (pp. 740-744). IEEE.
[19] Askari, S., Dwivedi, B., Saeed, A., & Nourani, M. (2009, November). Scalable mean voting mechanism for fault tolerant analog circuits. In Design and Test Workshop (IDT), 2009 4th International (pp. 1-6). IEEE.
[20] Linda, O., & Manic, M. (2011). Interval type-2 fuzzy voter design for fault tolerant systems. Information Sciences, 181(14), 2933-2950.
[21] La-inchua, J., Chivapreecha, S., & Thajchayapong, S. (2013, May). A new system for traffic incident detection using fuzzy logic and majority voting. In Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2013 10th International Conference on(pp. 1-5). IEEE.
[22] La-inchua, J., Chivapreecha, S., & Thajchayapong, S. (2014, May). Fuzzy logic-based traffic incident detection system with discrete wavelet transform. In Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2014 11th International Conference on(pp. 1-6). IEEE.
[23] Kwiat, K., Taylor, A., Zwicker, W., Hill, D., Wetzonis, S., & Ren, S. (2010, November). Analysis of binary voting algorithms for use in faulttolerant and secure computing. In Computer Engineering and Systems (ICCES), 2010 International Conference on (pp. 269-273). IEEE.
[24] Alahmadi, A., Soh, B., & Alghamdi, S. (2013, April). A hybrid history based weighted voting algorithm for smart mobile e-health monitoring systems. InIntelligent Sensors, Sensor Networks and Information Processing, 2013 IEEE Eighth International Conference on (pp. 402- 407). IEEE.
[25] Zhou, W., & Chen, L. (2011, August). A Research and Design of Byzantine Fault Tolerant DNS. In Internet Technology and Applications (iTAP), 2011 International Conference on (pp. 1-4). IEEE.
[26] Namazi, A., Nourani, M., & Saquib, M. (2010). A fault-tolerant interconnect mechanism for NMR nanoarchitectures. Very Large Scale Integration (VLSI) Systems, IEEE Transactions on, 18(10), 1433-1446.
[27] Derasevic, S., Proenza, J., & Barranco, M. (2014, September). Using FTT-ethernet for the coordinated dispatching of tasks and messages for node replication. In Emerging Technology and Factory Automation (ETFA), 2014 IEEE (pp. 1-4). IEEE.
[28] Öğüt, D. (2003). A behavior based robot control system using neurofuzzy approach (Doctoral dissertation, Middle East Technical University).
[29] Singamsetty, P., & Panchumarthy, S. (2012). Automatic fuzzy parameter selection in dynamic fuzzy voter for safety critical systems. International Journal of Fuzzy System Applications (IJFSA), 2(2), 68-90.
[30] Mitra, S., & Hayashi, Y. (2000). Neuro-fuzzy rule generation: survey in soft computing framework. Neural Networks, IEEE Transactions on, 11(3), 748-768.
[31] Alsaade, F. (2010). Neuro-Fuzzy Logic Decision in a Multimodal Biometrics Fusion System. Scientific Journal of King Faisal University (Basic and Applied Sciences), 11(2), 14.
[32] Pathak, A., Agarwal, T., & Mohan, A. (2015). A Novel Fuzzy Membership Partitioning for Improved Voting in Fault Tolerant System. Journal of Intelligent Learning Systems and Applications, 7(01), 1.
[33] Bolaji, A. L. A., Khader, A. T., Al-Betar, M. A., & Awadallah, M. A. (2013). Artificial bee colony algorithm, its variants and applications: a survey. Journal of Theoretical & Applied Information Technology, 47(2).
[34] Zhao, Z., Yang, J., Che, H., Sun, H., & Yang, H. (2013). Application of artificial bee colony algorithm to select architecture of a optimal neural network for the prediction of rolling force in hot strip rolling process. Journal of Chemical & Pharmaceutical Research, 5(9).
[35] Jin, F., & Shu, G. (2012, September). Back propagation neural network based on artificial bee colony algorithm. In Strategic Technology (IFOST), 2012 7th International Forum on (pp. 1-4). IEEE.
[36] Bullinaria, J. A., & AlYahya, K. (2014). Artificial Bee Colony training of neural networks. In Nature Inspired Cooperative Strategies for Optimization (NICSO 2013) (pp. 191-201). Springer International Publishing.