State encoding for low power in FSM using non-oscillating self-adaptive particle swarm optimization (NOS-SAPSO)-Reference-Cited by-同舟云学术

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State encoding for low power in FSM using non-oscillating self-adaptive particle swarm optimization (NOS-SAPSO)

Published:2020-02-17 Issue:2 Volume:41 Page:509-519
ISSN:0252-2667
Container-title:Journal of Information and Optimization Sciences
language:en
Short-container-title:Journal of Information and Optimization Sciences

Author:

Raj Deepti¹,Kalpana A. B.²,Singh Manoj Kumar³

Affiliation:

1. Department of Electronics & Telecommunication Engineering, Dayananda Sagar College of Engineering, Bangalore 560078, Karnataka, India,

2. Department of Electronics & Communication Engineering, Bangalore Institute of Technology, Bangalore 560004, Karnataka, India,

3. Manuro Tech Research Private Limited, Bangalore 560097, Karnataka, India

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/02522667.2020.1724615

Reference13 articles.

1. Kennedy and Eberhart, Particle Swarm Optimization, Proceedings of IEEE International Conference on Neural Networks. IV. pp. 1942–1948(1995).

2. Jun Sun, Choi-Hong Lai, Xiao-Jun Wu, Particle Swarm Optimisation: Classical and Quantum Perspectives, CRC Press,2019.

3. State assignment of finite state machines using a genetic algorithm

4. Finite State Machine Decomposition for Low Power

5. Valentin Hristov and Gergana Kalpachka, Use of web based calculator of genetic algorithms for optimal coding the states of finite state machines, MECATRONICS2014- Tokyo, (2014).

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1. A self-adaptive population Rao algorithm for optimization of selected bio-energy systems;Journal of Computational Design and Engineering;2020-09-04

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