A systematic approach to the optimization of artificial neural networks

Lin, Tzu‐Yu; He, Ping; Hsu, Tz-Heng; Wang, L. C.; Chen, C. C.; Wu, Cang-Pu; Liu, T. C.; Lin, Chun-Cheng; Lin, Yu-Cheng; Chang, Fa-Liang

doi:10.1109/iccsn.2011.6014853

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…In this paper, we used DoE to find the best setting of the ANN parameters in order to achieve a minimum error in force estimation. The applications of DoE techniques to optimize the ANN parameters have been reported in the literature [13][14][15][16][17][18]. It has been found that some factors such as the number of neurons in the hidden layers, transfer function, and training function have significant effects on the ANN performance.…”

Section: Introductionmentioning

confidence: 99%

Application of statistical techniques and artificial neural network to estimate force from sEMG signals

Khoshdel

Akbarzadeh

2016

JAIDM

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This paper presents an application of the design of experiment(DoE) techniques to determine the optimized parameters of the artificial neural network (ANN)model, which are used to estimate the force from the electromyogram (sEMG) signals. The accuracy of the ANN model is highly dependent on the network parameter settings. There are plenty of algorithms that are used to obtain the optimal ANN settings. However, to the best of our knowledge, no regression analysis has yet been used to model the effect of each parameter as well as presenting the percent contribution and significance level of the ANN parameters for force estimation. In this paper, the sEMG experimental data is collected, and the ANN parameters are regulated based on an orthogonal array design table to train the ANN model. The Taguchi method helps us to find the optimal parameters settings. The analysis of variance (ANOVA) technique is then used to obtain the significance level as well as the contribution percentage of each parameter I order to optimize ANN' modeling in the human force estimation. The results obtained indicate that DoE is a promising solution to estimate the human force from the sEMG signals.

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Section: Introductionmentioning

confidence: 99%

Application of statistical techniques and artificial neural network to estimate force from sEMG signals

Khoshdel

Akbarzadeh

2016

JAIDM

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“…NN is capable of solving different types of problems in many application areas like pattern recognition, classification, prediction, optimization, and control systems. 6–8 NN is found to be effective in modeling the nonlinear and highly correlated data sets and has extensively been used in manufacturing industry within last decades. Figure 1 illustrates an NN model which consists of single hidden layer along with the input and output layers comprising of neurons and transfer functions.…”

Section: Introductionmentioning

confidence: 99%

Optimization of neural network parameters using Grey–Taguchi methodology for manufacturing process applications

Kumar¹,

Gupta

Chandna

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part C:

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Performance of neural networks depends upon several input parameters. Several attempts have been made for optimization of neural network parameters using Taguchi methodology for achieving single objective such as computation effort, computation time, etc. Determination of optimum setting to these parameters still remains a difficult task. Trial-and-error method is one of the frequently used approaches to determine the optimal choice of these parameters. Keeping in view the problems with trial-and-error method, a systematic approach is required to find the optimum value of different parameters of neural network. In the present work, three most important distinct performance measures such as mean square error between actual and prediction, number of iteration, and total training time consumption have been probably considered first time concurrently. The multiobjective problem has been solved using Grey–Taguchi methodology. In this study, optimal combinations of different neural network parameters have been identified by using the Taguchi-based Grey relational analysis. The data set includes 81 sets of milling data corresponding to three-level full factorial experimental design for four cutting parameters, i.e. cutting speed, feed, axial depth of cut, and radial depth of cut, respectively. The output is average surface roughness for the experiment. The performance of different neural network models has been tabulated in L36 orthogonal array. Confidence interval has also been estimated for 95% consistency level to validate the optimum level of different parameters. It was found that the Taguchi-based Grey relational analysis approach can effectively be used as a structured method to optimize the neural network parameters settings, which can be easily implemented to enhance the performance of the neural network model with a relatively small size and time saving experiment. The result clearly indicates that the optimal combination of neural network parameters obtained by using the proposed approach performs better in terms of low mean square error, small number of iterations, and lesser training time required to perform the analysis which further results in lesser computation effort and processing time. Methodology proposed in this work can be utilized for any type of neural network application to find the optimum levels of different parameters.

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