Applications of error back propagation algorithm

The model summaries of curve estimation were showed in Table 1. The determinant coefficient of Logarithmic curve estimation for Iv administration was higher than Ig administration, which indicated Iv administration was suitable for fitting. Their concentration-time curves were showed in Figure 2. In Iv administration, the predicted concentrations of losartan were consistent with the measured concentration both in Bp-ANN and Logarithmic model.

The measured and simulated pharmacokinetic parameters of losartan by Ig and Iv administration were all calculated in two-compartmental model.

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Its principle as follows: The next signal is then presented and the process repeated. BP-ANN has integrated system, explicit algorithmic process, data identification and simulation function, and also owns the excellent ability to solve nonlinear problem. Generally, the structure of BP-ANN is determined by the number of hidden layers, neural and activated form of the function of neurons [ 18 ]. The learning process continues until the desired output signals is less than a preset value.

In this study, three layers BP network with tansig function as excitation function was developed, the learning rate was 0. Although, the logarithmic algorithm also obtained high goodness of fit index in Iv administration, the BP-ANN was still better than it, because of its smaller residuals.

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In Ig administration, which has more complicated time-concentration curve than Iv administration, the advantage of BP-ANN was fully embedded. We also calculated the pharmacokinetic parameters according to the predicted concentration generated by BP-ANN model. More importantly, we can predict the drug concentration at any time point during the pharmacokinetic study by BP-ANN.


BP-ANN can be used as a utility tool to improve the accuracy and convenience of pharmacokinetic experiment. National Center for Biotechnology Information , U. Int J Clin Exp Med. Author information Article notes Copyright and License information Disclaimer.

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Address correspondence to: Received Sep 9; Accepted Dec 5. This article has been cited by other articles in PMC. Abstract In order to develop pharmacokinetic model, a well-known multilayer feed-forward algorithm back-propagation artificial neural networks BP-ANN was applied to the pharmacokinetics of losartan in rabbit. Artificial neural network, back-propagation, pharmacokinetics, losartan. Introduction Artificial neural network ANN , one of widely used statistical learning algorithm in machine learning and cognitive science, is inspired by biological neural networks and basically consists of several non-linear processing units which called neurons or nodes [ 1 ].

Instrumentations Agilent Series liquid chromatograph equipped with a quaternary pump, a degasser, an autosampler, a thermostatted column compartment, and a Bruker Esquire HCT mass spectrometer Bruker Technologies, Bremen, Germany equipped with an electrospray ion source. Open in a separate window. Figure 1. Regression modeling and statistical analysis In order to assess the accuracy of BP-ANN model, eleven methods of curve estimation, which included logarithmic, inverse, quadratic, cubic, compound, power, S, growth, exponential, logistic, were developed by using SPSS statistical software, version Results BP artificial neural networks After the model was trained and well performed in two groups, the good fitness was indicated by following four values: Curve estimation model The model summaries of curve estimation were showed in Table 1.

Figure 2.

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Figure 3. Pharmacokinetic parameters The measured and simulated pharmacokinetic parameters of losartan by Ig and Iv administration were all calculated in two-compartmental model. Disclosure of conflict of interest None. References 1. Fasel B. An introduction to bio-inspired artificial neural network architectures. Acta Neurol Belg. J Clin Microbiol. Sci Total Environ. Sarotti AM. Org Biomol Chem. Assessing the effect of quantitative and qualitative predictors on gastric cancer individuals survival using hierarchical artificial neural network models.

Iran Red Crescent Med J. A model to predict 3-month mortality risk of acute-on-chronic hepatitis B liver failure using artificial neural network. J Viral Hepat. Prediction of calcium concentration in human blood serum using an artificial neural network.

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Application of artificial neural network to predict the retention time of drug metabolites in two-dimensional liquid chromatography. Drug Test Anal. Limited sampling models and Bayesian estimation for mycophenolic acid area under the curve prediction in stable renal transplant patients co-medicated with ciclosporin or sirolimus.

Clin Pharmacokinet. Tolbutamide, flurbiprofen, and losartan as probes of CYP2C9 activity in humans. Skip to search form Skip to main content. Our objective is to investigate the basic characteristics of back-propagation, and study how the framework of multi-layer perceptrons can be exploited in phonetic recognition. Save to Library. Create Alert. View Paper. From This Paper Figures, tables, and topics from this paper. Explore Further: Citations Publications citing this paper.

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Back Propagation Algorithm / Back Propagation Of Error In Artificial Neural Network Explained

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