Simulation design of an Intelligent system for Automotive transmission Gearbox Based on FPGA
In this paper, an artificialÂ intelligent system has been designed, realized, and downloaded intoÂ FPGA (Field Programmable Gate Array), which is used to control five speed ratio steps ( 1,2,3,4,5) of an electrically controlled type ofÂ automotive transmission gearbox of a vehicle, the first speed ratio step (1) is characterized by theÂ highest torque, a lowest velocity, while, theÂ fifth step is characterized by the lowest torque, and highest velocity.
The Back-propagation neural network has been used as the intelligent system for the proposed system. The proposed neural network is composed fromÂ Â eight neurons in the input layer, five neurons in the hidden layer, and five neurons in the output layer. For real downloading into the FPGA, Satlins and Satlin linear activation function has been used for the hidden and output layers respectively. The training function Trainlm ( Levenberg-Marqurdt training) has been used as a learning method for the proposed neural network, which it has a powerful algorithm.
Â The proposed simulation system has been designed and downloaded into the FPGA using MATLAB and ISE Design Suit software packages.
Mayur R. Mogre," Comparative Study between Automatic and Manual Transmission Car," International Conference on Mechanical, Automobile and Biodiesel Engineering, Dubai (UAE), Oct. 6-7, 2012.
Darko Stanojevi, Vladimir Spasojevi, Igor Stevanovi, and Aleksandar Nedi, " The Contemporary Automatic Gearboxes- review of the current state and interpretation of advantages and disadvantages of their use with respect to vehicle performance and terrific safety," University of Belgrade, Serbia, 2013.
Andrew Moskalik, Aaron Hula, Daniel Barba, and John Kargul," Investigating the Effect of Advanced Automatic Transmissions on Fuel Consumption Using Vehicle Testing and Modeling," SAE International J. Engines, April 01, 2016.
Ngo D.V.," Gear Shift Strategies for Automotive Transmissions," Technische Universiteit Endhoven TU/e Co., January, 2012.
Wenchen Shen, Huilong Yu, Yuhui Hu, and Junqiang Xi," Optimization of Shift Schedule for Hybrid Electric Vehicle with Automated Manual Transmission," Energies Journal, 2016.
Jian Yao, Li Chen, and Fengyu Liu," Experimental Study on Improvement in the Shift Quality for an Automatic Transmission Using a Motor-Driven Wedge Clutch," Jian Yao et al., January, 2014.
Long-Chang Hsieh, and Hsiu-Chen Tang," The Innovate Design of Automatic Transmission for Electric Motorcycle," Canadian Society for Mechanical Engineering, Vol. 37, No. 3, 2013.
Jing Li, Ji-hang Cheng, Jing-yuan Shi, and Fei Huang," Brief Introduction of Back Propagation (BP) Neural Network Algorithm and Its Improvement," Springer-Verlag Berlin Heidelberg, Vol. 2, pp. 553â€“558, 2012.
Chih-Yao Lo," Back Propagation Neural Network on the Forecasting System of Sea Food Material Demand," Springer-Verlag Berlin Heidelberg, Part II, pp. 147â€“154, 2011.
Jyh-Woei Lin, Chun-Tang Chao, and Juing-Shian Chiou," Back-propagation Neural Network as Earthquake Early Warning Tool using a new Elementary Modified Levenbergâ€“Marquardt Algorithm to minimise Back-propagation Errors," Author(s), May, 2018.
Bin Lin1, Gaotong Lin, Xianyun Liu, Jianshe Ma, Xianchuan Wang, Feiyan Lin, and Lufeng Hu," Application of back-propagation artificial neural network and curve estimation in pharmacokinetics of losartan in rabbit," Int J Clin Exp. Med., ISSN:1940-5901, 2015.
Taufik Ari Gunawan1, M. Syahril Badri Kusuma, M. Cahyono, and Joko Nugroho," The application of backpropagation neural network method to estimate the sediment loads," EDP Sciences Co., 2017.
Amit Ganatra, Y. P. Kosta, Gaurang Panchal, and Chintan Gajjar," Initial Classification Through Back-propagation in a Neural Network Following Optimization Through GA to Evaluate the Fitness of an Algorithm," International Journal of Computer Science and Information Technology (IJCSIT), Vol. 3, No. 1, February, 2011.
Ali Hossain, Mijanur Rahman, Uzzal Kumar Prodhan, and Farukuzzaman Khan," Implementation Of Back-Propagation Neural Network For Isolated Bangla Speech Recognition," International Journal of Information Sciences and Techniques (IJIST) Vol.3, No.4, July, 2013.
Rashmi Amardeep, and K. Thippe Swamy," Training Feed forward Neural Network with Back-propogation Algorithm," International Journal Of Engineering And Computer Science, ISSN: 2319-7242, Vol. 6, Issue 1, January, 2017.
Kamil Dimililer," Backpropagation Neural Network Implementation for Medical Image Compression," Hindawi Publishing Corporation Journal of Applied athematics, 2013.
Rama Kishore, and Taranjit Kaur," Backpropagation Algorithm: An Artificial Neural Network Approach for Pattern Recognition," International Journal of Scientific & Engineering Research, Vol. 3, Issue 6, June, 2012.
Omaima N. A. AL-Allaf," Improving the Performance of Backpropagation Neural Network Algorithm for Image Compression/Decompression System," Journal of Computer Science, ISSN 1549-3636, 2010.
Nazri Mohd. Nawi, Abdullah Khan, and Mohammad Zubair Rehman," A New Back-Propagation Neural Network Optimized with Cuckoo Search Algorithm," Springer-Verlag Berlin Heidelberg, Part I, LNCS 7971, pp. 413â€“426, 2013.
Ramya J., and B. Parvathavarthini," Feed Forward Back-propagation Neural Network Based Character Recognition System for Tamil Palm Leaf Manuscripts," Journal of Computer Science, ISSN: 1549-3636, 2014.
Wei Li, Lijuan Cui, Yaqiong Zhang, Zhangjie Cai, Manyin Zhang, Weigang Xu, Xinsheng Zhao, Yinru Lei, Xu Pan, Jing Li, and Zhiguo Dou," Using a Back-propagation Artificial Neural Network to Predict Nutrient Removal in Tidal Flow Constructed Wetlands," Water Journal, January, 2018.
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