### Abstract

Available methods for calculating frequency in cantilever beams have much complexity. In this study we present a new method for calculating natural frequencies in cantilever beams. For this purpose, we use the finite element method (FEM), dynamic analysis and artificial neural network (ANN) techniques to calculate the natural frequency. Finite element software was used to analyze 100 samples of cantilever beams, and the results were used as training and testing data sets in artificial neural networks. For the ANN. the multilayer feed-forward network and back-propagation algorithms were used. We made use of different transfer functions and built 45 different networks in order to find the best network performance. Mean squared error (MSE) was used to evaluate the network performance. Finally, the natural frequencies which were predicted by the ANN techniques were compared to the natural frequencies calculated from theoretical formulation, as well as to those obtained from FEM methods. The results obtained show that the error was quite small.

Original language | English |
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Title of host publication | Lecture Notes in Networks and Systems |

Publisher | Springer |

Pages | 441-449 |

Number of pages | 9 |

DOIs | |

Publication status | Published - 1 Jan 2018 |

### Publication series

Name | Lecture Notes in Networks and Systems |
---|---|

Volume | 16 |

ISSN (Print) | 2367-3370 |

ISSN (Electronic) | 2367-3389 |

### Fingerprint

### Keywords

- Artificial neural networks
- Cantilever beams
- Finite elements
- Mean squared error
- Natural frequency

### ASJC Scopus subject areas

- Computer Networks and Communications
- Signal Processing
- Control and Systems Engineering

### Cite this

*Lecture Notes in Networks and Systems*(pp. 441-449). (Lecture Notes in Networks and Systems; Vol. 16). Springer. https://doi.org/10.1007/978-3-319-56991-8_33

**Cantilever Beam Natural Frequency Prediction Using Artificial Neural Networks.** / Mohamed Haris, Sallehuddin; Mohammadi, Hamed.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

*Lecture Notes in Networks and Systems.*Lecture Notes in Networks and Systems, vol. 16, Springer, pp. 441-449. https://doi.org/10.1007/978-3-319-56991-8_33

}

TY - CHAP

T1 - Cantilever Beam Natural Frequency Prediction Using Artificial Neural Networks

AU - Mohamed Haris, Sallehuddin

AU - Mohammadi, Hamed

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Available methods for calculating frequency in cantilever beams have much complexity. In this study we present a new method for calculating natural frequencies in cantilever beams. For this purpose, we use the finite element method (FEM), dynamic analysis and artificial neural network (ANN) techniques to calculate the natural frequency. Finite element software was used to analyze 100 samples of cantilever beams, and the results were used as training and testing data sets in artificial neural networks. For the ANN. the multilayer feed-forward network and back-propagation algorithms were used. We made use of different transfer functions and built 45 different networks in order to find the best network performance. Mean squared error (MSE) was used to evaluate the network performance. Finally, the natural frequencies which were predicted by the ANN techniques were compared to the natural frequencies calculated from theoretical formulation, as well as to those obtained from FEM methods. The results obtained show that the error was quite small.

AB - Available methods for calculating frequency in cantilever beams have much complexity. In this study we present a new method for calculating natural frequencies in cantilever beams. For this purpose, we use the finite element method (FEM), dynamic analysis and artificial neural network (ANN) techniques to calculate the natural frequency. Finite element software was used to analyze 100 samples of cantilever beams, and the results were used as training and testing data sets in artificial neural networks. For the ANN. the multilayer feed-forward network and back-propagation algorithms were used. We made use of different transfer functions and built 45 different networks in order to find the best network performance. Mean squared error (MSE) was used to evaluate the network performance. Finally, the natural frequencies which were predicted by the ANN techniques were compared to the natural frequencies calculated from theoretical formulation, as well as to those obtained from FEM methods. The results obtained show that the error was quite small.

KW - Artificial neural networks

KW - Cantilever beams

KW - Finite elements

KW - Mean squared error

KW - Natural frequency

UR - http://www.scopus.com/inward/record.url?scp=85062923443&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062923443&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-56991-8_33

DO - 10.1007/978-3-319-56991-8_33

M3 - Chapter

AN - SCOPUS:85062923443

T3 - Lecture Notes in Networks and Systems

SP - 441

EP - 449

BT - Lecture Notes in Networks and Systems

PB - Springer

ER -