Advances in Computational Intelligence: Proceedings of by Sudip Kumar Sahana, Sujan Kumar Saha

By Sudip Kumar Sahana, Sujan Kumar Saha

This quantity includes the court cases of the foreign convention on Computational Intelligence 2015 (ICCI15). This e-book goals to collect paintings from major academicians, scientists, researchers and study students from around the globe on all elements of computational intelligence. The paintings consists typically of unique and unpublished result of conceptual, positive, empirical, experimental, or theoretical paintings in all components of computational intelligence. Specifically, the foremost issues coated comprise classical computational intelligence versions and synthetic intelligence, neural networks and deep studying, evolutionary swarm and particle algorithms, hybrid platforms optimization, constraint programming, human-machine interplay, computational intelligence for the net analytics, robotics, computational neurosciences, neurodynamics, bioinspired and biomorphic algorithms, go disciplinary themes and functions. The contents of this quantity might be of use to researchers and pros alike.

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Remaining energy (ERE)—Energy level remaining of the node. • Distance to sink (D2S)—Nodes distance (Euclidean distance) to sink. • Centrality (Cen)—A value which classifies the nodes based on the distance from the neighbors with proportion to network dimension. The first input fuzzy set is remaining energy; Fig. 1 shows membership functions of input variable remaining energy. The fuzzy sets in the form of linguistic variables include low, medium, and high. The second input fuzzy set is distance to sink of the node; Fig.

17] and Anno et al. [18] make use of fuzzy logic in the selection of CHs. Apart from this, Bagci et al. [10, 19] and the authors [20] proposed an unequal clustering approach, which uses fuzzy logic to assign distinct clustering range for CHs. On-demand clustering is a recent trend in clustering WSN where the cluster is formed when required. Taheri et al. proposed energy-aware distributed clustering protocol using fuzzy logic (ECPF) [6] which introduced on-demand-based clustering, and it uses fuzzy logic to select the CHs.

10. The only difference is programmer written in the embedded system block. (b) In fuzzy control method L, C is taken as 20 μH and 500 μF. In place of R load of boost converter, a single-phase inverter is connected in parallel with the LC filter, where L = 4 mH and C = 25 μF. The simulation block diagram of fuzzy logic control method is shown in Fig. 7, and the simulation result of this method is shown in Fig. 12. In the fuzzy control method in place of DC source, PV is taken, then it is connected to boost converter.

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