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Multiobjective Improved Particle Swarm Optimisation for Transmission Congestion and Voltage Profile Management using Multilevel UPFC

). Particle Swarm Optimization with an Aging Leader and Challengers Algorithm for the Solution of Optimal Power Flow Problem. Applied Soft Computing Journal, 40, pp. 161–177. Raj, S. and Bhattacharyya, B. (2018). Optimal Placement of TCSC and SVC for Reactive Power Planning Using Whale Optimization Algorithm. Swarm and Evolutionary Computation , 40, pp. 131–143. Singh, K., Yadav, V. K., Padhy, N. P. and Sharma, J. (2014). Congestion Management Considering Optimal Placement of Distributed Generator in Deregulated Power System Networks. Electric Power

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Application of Competitive and Transition Petri Layers in Adaptive Neuro-Fuzzy Controller

-Implemented Adaptive Recurrent Fuzzy Controller Designed by Evolutionary Algorithm, IEEE Trans. Circuits and Systems, 2005, Vol. 52, No. 11, 2376-2384. [9] KABZIŃSKI J., KACERKA J., TSK Fuzzy Modeling with Nonlinear Consequences, Artificial Intelligence Applications and Innovations, 2014, 498-507. [10] KLUSKA J., HAJDUK Z., Digital Implementation of Fuzzy Petri Net Based on Asynchronous Fuzzy RS Flip-Flop, Proc. 7th Int. Conf., Zakopane, Poland, 2004. [11] KNYCHAS S., SZABAT K., Adaptive Recurrent Neuro-Fuzzy Control of the Complex

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CLIMATE PARAMETERS IN BUILT ENVIRONMENT FROM ENERGETIC POINT OF VIEW – TOWARDS CLIMATE SEVERITY INDEX IN HUNGARY

., RETEK, M., Application of Evolutionary Algorithms for Energy Efficient Building Design, (2011) IWACIII 2011 - International Workshop on Advanced Computational Intelligence and Intelligent Informatics, Proceedings, pp. 6. [11] TALAMON, A., Energy Efficiency Opportunities of the Buildings Built with Industrialized Technology in Central Europe, (2011) Proceedings of the 2011 3rd International Youth Conference on Energetics, IYCE 2011, art. no. 6028323 [12] TALAMON, A., CSOKNYAI, T., Monitoring of a Performance-oriented Policy Model for

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Embedded Adaptive Neuro Fuzzy Inference System with Hardware Implemented Real Time Parameter Update

-23, 2010 [6] Zangeneh, A.Z., Mansouri, M., Teshnehlab, M., Sedigh, A.K., "Training ANFIS system with DE algorithm," Advanced Computational Intelligence (IWACI), pp. 308-314, 2011. [7] Carrano, E.G., Takahashi, R.H.C., Caminhas, W.M., Neto O.M., "A genetic algorithm for multiobjective training of ANFIS fuzzy networks," Evolutionary Computation CEC . IEEE Congress, Vols. 3259-3265, 2008. [8] Banu, U.S., Uma, G., "ANFIS gain scheduled CSTR with genetic algorithm based PID minimizing integral square error," Information and

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Automated evaluation of agricultural damage using UAV survey

., “Evolutionary algorithm for optimizing parameters of GPGPU-based image segmentation”, (2013) Acta Polytechnica Hungarica , 10 (5), pp. 7-28. [14] Kertész, G., Szénási, S., Vámossy, Z., “Parallelization Methods of the Template Matching Method on Graphics Accelerators”, (2015) 16th IEEE International Symposium on Computational Intelligence and Informatics – CINTI , pp. 161-164. ISBN:978-1-4673-8520-6

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