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An Intelligent Cooling Technique for Internal Combustion Engine Using Optimal ANN and Fuzzy Logic Algorithms

K.V. Santhosh, Nalin Kumar Sharma

Abstract


Now days it’s very important to increase the efficiency of Internal Combustion (IC) engines. Due to many factors such as fuel economy, fuel crisis, and ultimately increase the output. During the process of combustion a large portion of heat is transferred to various engine components and the engine may be damaged unless the excess heat is carried away and these parts are adequately cooled. Adequate cooling is then a fundamental problem associated with internal combustion engines. This paper proposes an improved effective intelligent cooling system in IC engine using the optimal Artificial Neural Network (ANN) and Fuzzy logic algorithms. Optimal ANN is considered based on Mean Square Error (MSE) close to zero and Regression (R) closes to 1. The design was modeled and simulated using the LabVIEW platform. Results show that the proposed system was able to fulfill its desired objectives.

Keywords


IC Engines, Cooling System, ANN, Fuzzy Logic Algorithms, LabVIEW.

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References


Santhosh K V, Nalin Kumar Sharma, “LabVIEW Implementation of an Automated Cooling Technique for Internal Combustion Engine Using ANN”, Proc. International Conference on Computing and Control Engineering, Chennai, India, April, 2012.

Singer, Charles Joseph; Raper, Richard, A History of Technology: The Internal Combustion Engine, Clarendon Press, 1954-1978. pp. 157–176

Horst O. Hardenberg, The Middle Ages of the Internal Combustion Engine, 1999, Society of Automotive Engineers (SAE)

Haynes, Opel Omega & Senator Service and Repair Manual.. 1996. ISBN 1-85960-342-4.

Rankin Kennedy C.E. The Book of the Motor Car. Caxton. 1912.

Mahesh.L.Chugani, LabVlEW Signal Processing, Prentice-Hall India, 1998.

National Instruments, LabVIEW Help Manual.

J. Fernandez de Canete, S. Gonzalez-Perez, and P. del Saz-Orozco, “Artificial Neural Networks for Identification and Control of a Lab-Scale Distillation Column using LabVIEW”, World Academy of Science, Engineering and Technology, vol 47, pp 64-69, 2008.

R. Bishop, Learning with LabVIEW 7 Express, New Jersey, Prentice Hall, 2004.

Björck A, Numerical methods for least squares problems, SIAM Publications, Philadelphia. ISBN 0-89871-360-9, 1996.

Fletcher, Roger, Practical methods of optimization, 2nd Edition, John Wiley & Sons, New York. ISBN 978-0-471-91547-8, 1987.

Fernando Morgado Dias, Ana Antunes1, José Vieira, Alexandre Manuel Mota, Implementing The Levenberg-Marquardt Algorithm On-line: a sliding window approach with early stopping. Int. Conf. Proc. IFAC, USA, 2004.

Jeng-Bin Li, Yun-Kung Chung, A Novel Back propagation Neural Network Training Algorithm Designed by an Ant Colony Optimization, IEEE/PES Transmission and Distribution Conference & Exhibition: Asia and Pacific Dalian, China 2005

L. Bianchi, L.M. Gambardella, M.Dorigo, An ant colony optimization approach to the probabilistic travelling salesman problem, Proc. of PPSN-VII, Seventh Inter17 national Conference on Parallel Problem Solving from Nature, Springer Verlag, Berlin, Germany, 2002

Stuart Russell and Peter Norvig, Artificial Intelligence A Modern Approach, 3rd Edition, Prentice Hall New York, 2009.

Park J, Sandberg J W, Universal Approach Using Radial Basis Function Network, Neural Computation, Vol 3, pp. 246-257, 1991

T Poggio, F Girosi, Networks for approximation and learning, Proc. IEEE 78(9), pp. 1484-1487, 1990.

Paul Yee V and Simon Haykin, Regularized Radial Basis Function Networks: Theory and Applications, John Wiley, 2001.

Dr. B Prabhakara Rao, Deepak Voleti, “A Novel Approach of Designing Fuzzy Logic based Controller for Water Temperature of Heat Exchanger Process Control” , , International journal of advanced Engineering Sciences and Technologies, vol.11, no. 1,172-176, 2010.

J. Lee, "On methods for improving performance of Pl-type fuzzy logic controllers," IEEE Transactions on Fuzzy Systems, vol. 1, 1993.

H. Li and H. Gatland, "Conventional fuzzy control and its enhancement," IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 26, pp. 791-797, 1996.


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