International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 9, No. 10, 8 Oct. 2017
Cover page and Table of Contents: PDF (size: 1265KB)
Neural Network Synchronous Binary Counter Using Hybrid Algorithm Training
Full Text (PDF, 1265KB), PP.38-49
Views: 0 Downloads: 0
Author(s)
Ravi Teja Yakkali
1,*
N S Raghava
2
Index Terms
Artificial Neural Networks, Hybrid Algorithms, Synchronous Binary Counter, Back Propagation Algorithm, Evolutionary Algorithms
Abstract
Information processing using Neural Network Counter can result in faster and accurate computation of data due to their parallel processing, learning and adaptability to various environments. In this paper, a novel 4-Bit Negative Edge Triggered Binary Synchronous Up/Down Counter using Artificial Neural Networks trained with hybrid algorithms is proposed. The Counter was built solely using logic gates and flip flops, and then they are trained using different evolutionary algorithms, with a multi objective fitness function using the back propagation learning. Thus, the device is less prone to error with a very fast convergence rate. The simulation results of proposed hybrid algorithms are compared in terms of network weights, bit-value, percentage error and variance with respect to theoretical outputs which show that the proposed counter has values close to the theoretical outputs.
Cite This Paper
Ravi Teja Yakkali, N S Raghava," Neural Network Synchronous Binary Counter Using Hybrid Algorithm Training", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.9, No.10, pp. 38-49, 2017. DOI: 10.5815/ijigsp.2017.10.05
Reference
[1]A. Patnaik, D. E. Anagnostou, R. K. Mishra and Christodoulou CG, “Applications of Neural Networks in Wireless Communication”, IEEE Antennas and Propagation Magazine, Volume: 46, Issue: 3, 3 June, 2004 pp. 130-137.
[2]W. J. Lee, D. S. Kim, S. W. Kang and W. J. Yi, “Material depth reconstruction method of multi-energy X-ray images using Neural Networks”, Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, Volume:33 , Issue:5,28,Aug-Sep.1998, pp. 1514-1517.
[3]R. Garg, N. Jayakumar, S. P. Khatri and G. S. Choi, “Circuit Level Design Approaches for Radiation-Hard Digital Electronics”, IEEE Transactions on Very Large Scale Integration (VLSI) Systems,Volume:17, Issue:6,June,2009 pp. 781-791.
[4]C.Melear, “Control of Electromagnetic radiation in Digital Circuits”, Wescon Conference Proceedings, 4-6 Nov 1997, pp. 208-218.
[5]C.D Boloes, B.E Boser, B.H Hasegawa and J.A Heanue, “A multimode digital detector for solid-state medical imaging”, IEEE Journal of Solid-State Circuits, 1991 Volume:33, Issue:5, May.1998 pp.733-742.
[6]Parisa Soleimani, Reza Sabbaghi-Nadooshan, Sattar Mirzakuchaki, and Mahdi Bagheri,“Using Genetic Algorithm in the Evolutionary Design of Sequential Logic Circuits”, International Journal of Computer Science Issues(IJCSI), Vol. 8, Issue 3, May 2011, pp.1-7.
[7]K. Otsu , A. Ishiguro ,A. Fujii, T. Aoki and P. Eggenberrer, “Evolving an Adaptive Controller for a quadrupled-robot with dynamically arranging Neural Networks”, IEEE/RSJ International Conference on Intelligent Robots and Systems, Proceedings, Volume: 4, 2001, pp. 2036-2044.
[8]J. Dheeba and A. Padma,“Intelligent Adaptive Noise Cancellation using Cascaded Correlation Neural Networks”, International Conference on Signal Processing, Communications and Networking (ICSCN '07), 22-25 Feb,2007 pp. 178-182.
[9]T. P.Nam, Hue Ind. Coll, D. T. Viet and L. Van Ut, “Application of neural network in voltage stability assessment in real-time power market”, IPEC, Conference on Power & Energy, 12-14 Dec, 2012, pp. 196-200.
[10]D. P. Sharma, “Neural Network Simulation of Digital Circuits”, International Journal of Computer Applications, Vol.79 No.6, October 2013, pp. 7-13.
[11]Hisayuki Tatsumi, Yasuyuki and Shinji Tokumasu, “Logic Circuit Diagnosis using Neural Networks”, 31st International Sympsonium Multivalued Logic, 2001, pp.345-350.
[12]Zhouxun and Wangxiaoli, “Hybrid Artificial Bee Colony Algorithm for neural network training”, 24th Chinese Control and Decision Conference (CCDC), 23-25 May, 2012, pp. 2297-2299.
[13]H. K. Lam and Frank H. F. Leung, “Design and training for Combinational Neural-Logic Systems”, IEEE transactions on Industrial Electronics, October 2007, Vol. 54, No.1, pp.612-619.
[14]Hiroshi Ninomtya, Kunitaka EGAWA, Takeshi Kamio and Hideki Asai, “Design and Implementation of Neural Network Logic Circuits with Global Convergence”, IEEE International Conference on Neural Networks, 1996, Vol.22, pp. 980-985.
[15]Dipayan Bhadra, Tanvir Ahmed Tarique, Sultan Uddin Ahmed, Md. Shahjahan and Kazuyuki Murase, “ An Encoding Technique for Design and Optimization of Combinational Logic Circuit”, Proceedings of 13th International Conference on Computer and Information Technology (ICCIT 2010), 23-25 December, 2010, Dhaka, Bangladesh, pp. 744-750.
[16]Zeynab Mirzadeh, Jean-Francois Boland and Yvon Savaria,“Modeling the Faulty Behaviour of Digital Designs Using a Feed Forward Neural Network Approach”, IEEE International Symposium on Circuits and Systems (ISCAS), 2015 pp.1518-1521.
[17]Joao Ranhel,Cacilda V.Lima,Julio L.R Monterio,Joao E. Kogler and Jr.,Marcio L.Netto, “ Bi-Stable Memory and Binary Counters in Spiking Neural Network ”, IEEE Symposium on Foundation of Computational Intelligence(FOCI) , October 2011,Vol. 22,No.10, pp.66-73.
[18]James Kennedy and Russell Eberhart, “Particle Swarm Optimization”, Proceedings of IEEE International Conference on Neural Networks, Vol.4, October 1995,pp. 1942-1948.
[19]D. Karaboga, “AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION”, TECHNICAL REPORT-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department 2005.
[20]David E. Rummelhart, Geofrrey E.Hinton and Ronald J.Williams, “Learning representations by back-propagating error”, International weekly journal of Science (Nature), 31 July, 1996, pp. 533-536.
[21]M. J. Roberts, “A Statistical Investigation of cost function derivatives for neural networks with continuous activation function”, Second International Conference on Artificial Neural Networks, Volume: 9, Issue:1,18-20 Nov.1991 pp. 34-38.
[22]M. T. Tommiska, “Efficient Digital Implementation of sigmoid Function for reprogrammable hardware”, IEEE Proceedings - Computers and Digital Techniques, Volume: 150, Issue: 62, 17 Nov.2003, pp. 403-411.
[23]Jing-Ru-Jang, Jung Jang,Tat-Ming Lok,Michael and R.Lyu, “A hybrid Particle Swarm Optimization-back propagation Algorithm for feedforward neural network training”, Applied Mathematics and Computation,Science Direct,Vol.185,Issue 2,15 Feb, 2007, pp. 1026-1037.
[24]M. Liu and J.He, “A Hybrid Genetic Algorithm with Hyper-Mutation and Elitist Strategies for Automated and Analog Circuit Design”, International Workshop on Intelligent Systems and Applications, 2009, ISA 2009. 23-24 May, 2015, pp. 1-4.
[25]Chen-Lun Lin, Aya Mimori, and Yen-Wei Chen ,“Hybrid Particle Swarm Optimization and its applications to Multimodal 3D Medical Image Restoration”, Computation Intelligence and Neuroscience, 2012.
[26]C. Ozturk and D. Karaboga, “Hybrid Artificial Bee Colony Algorithm for neural network training”, 2011 IEEE Congress on Evolutionary Computation (CEC), 5-8 June, 2011, pp. 84-88.
[27]Bruno A Silva, A.Dias, Jordge, A Silva and Fernando S.Osorio, “Genetic Algorithm and Artificial Neural Network to Combinational Circuit Generation on Reconfigurable Hardware”, International Conference on Reconfigurable Computing (Nature), 13-15 December, 2010, pp. 180-184.
[28]Md. Mijanur Rahman and Tania Akter Setu, “An Implementation for Combining Neural Networks and Genetic Algorithms”, IJCST, Vol. 6, Issue: 3, July - Sept 2015, pp.218-222.
[29]Sudarshan Nandy, Partha Pratim Sarkar and Achintya Das, “Training a Feed-Forward Neural Network with Artificial Bee Colony based Back propagation Method”, International Journal of Computer Science & Information Technology (IJCSIT) , Vol.4, No. 4, August 2012, pp. 33-46.
[30]Feihu Ji and Guang Shu, “Back Propagation Neural Net Based on Artificial Bee Colony Algorithm” , 7th International Conference on Strategic Technology (IFOST), 18-21 September, 2012, pp. 1-4.
[31]Oyebade K. Oyedotun and Kamil Dimililer, “Pattern Recognition: Invariance Learning in Convolutional Auto Encoder Network”, I.J Image, Graphics and Signal Processing, 2016, 3, pp. 19-27.
[32]Md. Mahbubar Rahman, M. A. H. Akhand, Shahidul Islam, Pintu Chandra Shill, “Bangla Handwritten Character Recognition using Convolution Neural Network ”, I.J Image, Graphics and Signal Processing, 2015, 8, pp. 42-49.