By Sudip Kumar Sahana, Sujan Kumar Saha
This quantity contains the court cases of the overseas convention on Computational Intelligence 2015 (ICCI15). This publication goals to collect paintings from prime academicians, scientists, researchers and study students from around the globe on all facets of computational intelligence. The paintings consists in general of unique and unpublished result of conceptual, optimistic, empirical, experimental, or theoretical paintings in all components of computational intelligence. Specifically, the most important issues lined comprise classical computational intelligence types 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, pass disciplinary subject matters and purposes. The contents of this quantity might be of use to researchers and pros alike.
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Extra resources for Advances in Computational Intelligence: Proceedings of International Conference on Computational Intelligence 2015
11 shows the FFT analysis using I&C method, and Fig. 13 shows the FFT analysis using fuzzy control method. Harmonics Minimization in Inverter Using Fuzzy Controller … 31 6 Conclusion This paper gives a brief idea about a single-phase photovoltaic (PV) inverter concept with fuzzy-based MPPT control. It presents the fuzzy logic control for controlling the MPPT of the PV system. The proposed algorithm is fuzzy logic control which is able to simulate. The simulation results shows that this system accept fuzzy parameter for faster response and good performance in FFT analysis.
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)  which introduced on-demand-based clustering, and it uses fuzzy logic to select the CHs. 3 The Proposed Approach The on-demand clustering algorithms proposed earlier shows excellent improvement over the every round based clustering approach. But the on-demand clustering approach proposed so far uses equal cluster range for CHs.
Step 6 The aggregate values (for evaluations) T ̃j of the alternatives xj (j = 1, m 2,3,…,n) are obtained as T j̃ = ∑ f ij̃ ðj = 1, 2, 3, . . , nÞ i=1 Step 7 The non-increasing order of T ̃j ðj = 1, 2, 3, . . , nÞ is determined according to some ranking method of intuitionistic fuzzy numbers wherein they are ranked and the best alternative is obtained. 4 Numerical Example An example is considered here wherein the four inventory policies IP1, IP2, IP3 and IP4 as per the Sect. 1 consider the attributes C1, C2, C3, C4, C5 and C6 as mentioned in Sect.
Advances in Computational Intelligence: Proceedings of International Conference on Computational Intelligence 2015 by Sudip Kumar Sahana, Sujan Kumar Saha