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Implementation of neuro-fuzzy system with modified high performance genetic algorithm on embedded systems
Affiliation:1. Department of Computing, São Paulo State University, Av. Eng. Luiz Edmundo Carrijo Coube, 14-01, Bauru, SP 17033-360, Brazil;2. School of Science and Technology, Middlesex University, NW4 4BT, United Kingdom;1. Department of Statistics and Informatics, Rural Federal University of Pernambuco, Brazil;2. Center of Informatics, Federal University of Pernambuco, Brazil;1. Polish Academy of Sciences, Systems Research Institute, Centre of Information Technology for Data Analysis Methods; AGH University of Science and Technology, Faculty of Physics and Applied Computer Science, Division for Information Technology and Systems Research, Poland;2. Polish Academy of Sciences, Systems Research Institute, Ph.D.-Studies, Poland;1. School of Information Science and Engineering, Central South University, Changsha, Hunan 410083, China;2. School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, Hunan 410004, China;3. Collaborative Innovation Center of Resource-conserving & Environment-friendly Society and Ecological Civilization, Changsha, Hunan 410083, China;1. Programa de Pós-Graduação em Engenharia Elétrica, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte, MG, Brazil;2. Departamento de Engenharia Elétrica, Centro Federal de Educação Tecnológica de Minas Gerais, Av. Amazonas 7675, Belo Horizonte, MG, Brazil;3. Operations Research and Complex Systems Laboratory (ORCS Lab.), Departamento de Engenharia Elétrica, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte, MG, Brazil
Abstract:In this paper implementation of ANFIS on embedded systems based on single-core and multi-core ARM processors is presented. A novel evolutionary optimization tool named, modified high performance genetic algorithm (mHPGA) with bacterial conjugation operator is applied to ANFIS as a training method. Fixed point and floating point number representations are applied and compared. Moreover new mutation algorithm has been proposed for fixed point numbers. The proposed method is designed to sweep numbers space to search possible solutions in large state space. Concurrency nature of mHPGA benefits implementation of multi threading feature on ARM cortex-A53 with four cores.
Keywords:ANFIS  Neuro-fuzzy  Embedded systems  Genetic algorithms  Bacterial conjugation  Concurrency  Fixed point
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