Abstract
Finite state machines have so many applications in the day-to-day life. Design of Finite State machines spread its role from the simple systems to complex systems. As Artificial Intelligence rule all over the technology world by its very effective applications, Finite state machines can also significantly use its essence in the process of next state prediction. The predictive analysis of Artificial intelligence helps to speed up the process of Finite state machines. This paper explores the design of anticipative state machines with the help of Artificial Neural Networks. To get the higher performance, less training time and low error prediction, Back propagation algorithm is used in ANN which helps to analyze the critical parameters in real time applications. Our proposed technique provides better results than the previously used technique and also provides less prediction and training time error with increasing number of inputs.
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References
Kushik N, El-Fakih K, Yevtushenko N, Cavalli AR (2014) On adaptive experiments for nondeterministic finite state machines. Springer
Schmidhuber J (2014) Deep learning in neural networks: an overview. Technical Report IDSIA-03-14/arXiv:1404.7828 v4 [cs.NE] (88 pages, 888 references). The Swiss AI Lab IDSIA Istituto Dalle Molle di Studisull’ Intelligenza Artificiale. IEEE
El-Maleh AH, Sait SM, Bala A (2015) State assignment for area minimization of sequential circuits based on cuckoo search optimization. Elsevier
Wysocki A, Ławry´nczuk M (2015) Jordan neural network for modelling and predictive control of dynamic systems. IEEE
Ardakani A (Student Member, IEEE, François), Leduc-Primeau F, Onizawa N (Member, IEEE), Hanyu T (Senior Member, IEEE), Gross WJ (Senior Member, IEEE) (2016) VLSI implementation of deep neural network using integral stochastic computing
Kayri M (2016) Predictive abilities of bayesian regularization and Levenberg–Marquardt algorithms in artificial neural networks: a comparative empirical study on social data. MDPI
Rastogi P, Cotterell R, Eisner J (2016) Weighting finite-state transductions with neural context. In: NAACL-HLT Proceedings
Song T, Zhen P, Wong MLD, Wang X (2016) Design of logic gates using spiking neural P systems with homogeneous neurons and astrocytes-like control. Elsevier
Duan S (Member, IEEE), Hu X (Student Member, IEEE), Dong Z (Student member, IEEE) (2015) Memristor-based cellular nonlinear/neural network: design, analysis, and applications. Transaction
Reddy PR, Prasad D (2015) Low-power analysis of VLSI circuit using efficient techniques. IJNTSE
Giles CL, Ororbia II A Recurrent neural networks: state machines and pushdown automata. The Pennsylvania State University, University Park, PA, USA
Goyal R, Vereme V (2000) Application of neural networks to efficient design of wireless and RF circuits and systems. AMSACTA
Reynaldi A, Lukas S, Margaretha H (2012) Back propagation and Levenberg-Marquardt algorithm for training finite element neural network. IEEE
Soeken M, Wille R, Otterstedt C, Drechsler R (2014) A synthesis flow for sequential reversible circuits. IEEE
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Anjum, N., Prajapat, B. (2019). ANN-Based Predictive State Modeling of Finite State Machines. In: Mishra, D., Yang, XS., Unal, A. (eds) Data Science and Big Data Analytics. Lecture Notes on Data Engineering and Communications Technologies, vol 16. Springer, Singapore. https://doi.org/10.1007/978-981-10-7641-1_34
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DOI: https://doi.org/10.1007/978-981-10-7641-1_34
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