By Atulya Nagar, Durga Prasad Mohapatra, Nabendu Chaki
Advanced Computing, Networking and Informatics are 3 designated and jointly unique disciplines of information without obvious sharing/overlap between them. even if, their convergence is saw in lots of actual global functions, together with cyber-security, web banking, healthcare, sensor networks, cognitive radio, pervasive computing amidst many others. This quantity court cases discover the mixed use of complex Computing and Informatics within the subsequent new release instant networks and protection, sign and photograph processing, ontology and human-computer interfaces (HCI). the 2 volumes jointly comprise 132 scholarly articles, that have been authorized for presentation from over 550 submissions within the 3rd foreign convention on complicated Computing, Networking and Informatics, 2015, held in Bhubaneswar, India in the course of June 23–25, 2015.
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Extra info for Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics: ICACNI 2015, Volume 1
In this paper, we extend the idea of adaptive inertia weight strategy to solve the LQR optimization problem. To implement an adaptive inertia weight strategy, it is important to evaluate the position of the swarm during every iteration step. Hence, the success percentage (SP) of particles is used to update the velocity adaptively. Large value of SP indicates that the particles have reached the best value and the particles are slowly progressing towards the optimum, whereas a small value of SP implies that the particles are fluctuating around the optimum value with very less improvement.
Commonly, trial and error approach is employed for selecting the weighting matrices, which not only burdens the design but also results in non-optimal response. Hence, to choose the elements of Q and R matrices optimally, an APSO algorithm is formulated and applied for tracking control of inverted pendulum. One of the notable changes introduced in the APSO over conventional PSO is that an adaptive inertia weight parameter (AIWP) is incorporated in the velocity update equation of PSO to increase the convergence rate of PSO.
J. Comput. Appl. Math. 234, 10–20 (2010) 8. : Comment on A fuzzy soft set theoretic approach to decision making problems. J. Comput. Appl. Math. 223, 540–542 (2009) 9. : Fuzzy soft sets. J. Fuzzy Math. 9, 589–602 (2001) 10. : Soft set theory. Comput. Math Appl. 45, 555–562 (2003) 11. : An application of soft sets in a decision making problem. Comput. Math Appl. 44, 1077–1083 (2002) 12. : Soft set theory-ﬁrst results. Comput. Math Appl. 37, 19–31 (1999) 13. : A fuzzy soft set theoretic approach to decision making problems.