Sequential Estimation of Feed Forward Networks for Small Embedded Hardware
Rajarajan G1, Madhur Bhatnagar2, Sharoni Roy Chowdhury3
1Rajarajan G*, Assistant Professor, School of Computer Science and Engineering, Vellore Institite of Technology, Vellore, India.
2Madhur Bhatnagar, School of Computer Science and Engineering, Vellore Institite of Technology, Vellore, India.
3Sharoni Roy Chowdhury, , School of Computer Science and Engineering, Vellore Institite of Technology, Vellore, India.
Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 7960-7962 | Volume-8 Issue-4, November 2019. | Retrieval Number: D4263118419/2019©BEIESP | DOI: 10.35940/ijrte.D4263.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Deterministic Finite Automata (DFA) and Non-Deterministic Automata (NFA) Systems require an input and lead to an output after stage traversals and based on the pathway so chosen leads to the either the acceptance or rejection of a language. Considering compilers, the compiler works first to understand the lexical correctness of the input and to do so follows steps to check for the validity of the same. If the input is of a valid form then the input is accepted else a suitable corresponding error is thrown. When considering a feed forward neural network, we see a pattern of an input being taken and passed to a hidden layer, which further may either pass to another hidden layer (making it a deep network) or lead it to an output layer. Neural networks find application in classification problems, regression analysis and recognition paradigms. On naïve speculation, a correlation can be made on similarities between finite automata and feed forward networks.
Keywords: Automata, Deterministic, Feed forward, Finite, Neural Networks, Non-Deterministic.
Scope of the Article: High Speed Networks.