TY - GEN
T1 - Efficient Implementation of Artificial Neural Networks for Sensor Data Analysis Based on a Genetic Algorithm
AU - D’Estefani, André
AU - Cordero, Raymundo
AU - Onofre, João
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - The reliability of many industrial processes depends on the sensor system. However, these sensors can be affected by noise, perturbations and failures. Hence, sensor monitoring and diagnosis are fundamental to guarantee the quality of an industrial process. Nowadays, artificial neural networks (ANN) are widely used in sensor signal processing and diagnosis. However, those ANNs usually require many artificial neurons, being difficult to implement in software and hardware due to their high computational costs. This paper presents an optimized implementation of artificial neurons in ANNs for sensor data analysis using a Genetic Algorithm (GA). The objective of GA is to find an adequate segmentation to reduce the activation function approximation error. One of the advantages of the proposed approach is that the cost function used in GA considers the effect of factors such as the ANN architecture or the number of bits used in arithmetic operations. The proposed ANN implementation technique aims to get the best possible approximation for a specific ANN architecture, making easier its implementation in software and hardware. Simulation and experimental results using FPGA (Field Programmable Gate Array) prove the advantages of the proposed approach for implementing sensor data analysis systems based on ANNs.
AB - The reliability of many industrial processes depends on the sensor system. However, these sensors can be affected by noise, perturbations and failures. Hence, sensor monitoring and diagnosis are fundamental to guarantee the quality of an industrial process. Nowadays, artificial neural networks (ANN) are widely used in sensor signal processing and diagnosis. However, those ANNs usually require many artificial neurons, being difficult to implement in software and hardware due to their high computational costs. This paper presents an optimized implementation of artificial neurons in ANNs for sensor data analysis using a Genetic Algorithm (GA). The objective of GA is to find an adequate segmentation to reduce the activation function approximation error. One of the advantages of the proposed approach is that the cost function used in GA considers the effect of factors such as the ANN architecture or the number of bits used in arithmetic operations. The proposed ANN implementation technique aims to get the best possible approximation for a specific ANN architecture, making easier its implementation in software and hardware. Simulation and experimental results using FPGA (Field Programmable Gate Array) prove the advantages of the proposed approach for implementing sensor data analysis systems based on ANNs.
UR - http://www.scopus.com/inward/record.url?scp=85127664183&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-96794-9_40
DO - 10.1007/978-3-030-96794-9_40
M3 - Conference contribution
AN - SCOPUS:85127664183
SN - 9783030967932
T3 - Lecture Notes in Mechanical Engineering
SP - 433
EP - 442
BT - 15th WCEAM Proceedings
A2 - Pinto, João Onofre
A2 - Kimpara, Marcio Luiz
A2 - Reis, Renata Rezende
A2 - Seecharan, Turuna
A2 - Upadhyaya, Belle R.
A2 - Amadi-Echendu, Joe
PB - Springer Science and Business Media Deutschland GmbH
T2 - 15th ISEAM flagship World Congress on Engineering Asset Management, WCEAM 2021
Y2 - 15 August 2021 through 18 August 2021
ER -