A Java application for the failure rate prediction using feed forward neural networks

Ene, Alexandru; Știrbu, Cosmin

doi:10.1109/ecai.2016.7861197

Cited by 2 publications

(2 citation statements)

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“…In [4] is presented a feed forward neural network that is able to learn a nonlinear process described by a single input and a single output. In this paper, we developed a neural network that is able to learn a nonlinear process, but it has more inputs.…”

Section: Feed Forward Neural Networkmentioning

confidence: 99%

The estimation with Artificial Neural Networks of some quality parameters for the surfaces processed by superfinishing

Anghel

Ene

2017

MATEC Web Conf.

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Abstract. This paper presents a study on the quality parameters obtained by superfinishing. The quality is characterized by the roughness. They are dependent on the following process parameters: circular feed, the contact pressure between the piece and the tool, frequency of oscillation of the tool, the coverage degree between the tool and the piece and the basic time. Because the dependence between inputs and outputs is a nonlinear one, in this paper we used an artificial feed forward neural network (ANN). The ANN is trained with the backpropagation algorithm, using as training patterns data measured from the mechanical process. The ANN is used to estimate some parameters from future experiments of the mechanical process.

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Section: Feed Forward Neural Networkmentioning

confidence: 99%

The estimation with Artificial Neural Networks of some quality parameters for the surfaces processed by superfinishing

Anghel

Ene

2017

MATEC Web Conf.

Self Cite

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“…An Artificial Neural Network (ANN) is composed of interconnected artificial neurons that mimic some properties of biological neurons [1]. There are many different types of neural networks, like: feed forward neural networks, Hopfield, Kohonen, etc.…”

Section: Feed Forward Neural Networkmentioning

confidence: 99%

Study of the influence of the technological parameters on the weld quality using artificial neural networks

Anghel

Ene

2018

MATEC Web Conf.

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Abstract. This paper presents a study on the weld quality obtained by different values of the input parameters. The weld quality is characterized by two categories of parameters: geometrical parameters and mechanical parameters. They are dependent on the following process parameters: electric arc voltage, electric current intensity, welding speed, the feed wire velocity. Because the dependence between inputs and outputs is a nonlinear one was used an artificial feed forward neural network (ANN). The ANN was trained with the backpropagation algorithm, using as training patterns data measured from the mechanical process. This ANN can be used to estimate some parameters from future experiments of the mechanical process.

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A Java application for the failure rate prediction using feed forward neural networks

Cited by 2 publications

References 0 publications

The estimation with Artificial Neural Networks of some quality parameters for the surfaces processed by superfinishing

The estimation with Artificial Neural Networks of some quality parameters for the surfaces processed by superfinishing

Study of the influence of the technological parameters on the weld quality using artificial neural networks

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