Introduced Two Simple Approaches for Prediction Heat of Explosion of high energetic materials by using MLR and ANN moels

rahmani, mehdi; vahedi, mohamad kazem; mahmoodnejad, mahmoodreza; ahmadi, behzad

Volume 9, Issue 3 (3-2014) مواد پرانرژی 2014, 9(3): 67-80 | Back to browse issues page

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rahmani M, vahedi M K, mahmoodnejad M, ahmadi B. Introduced Two Simple Approaches for Prediction Heat of Explosion of high energetic materials by using MLR and ANN moels. مواد پرانرژی 2014; 9 (3) :67-80
URL: http://isaem.ir/article-1-384-en.html

Introduced Two Simple Approaches for Prediction Heat of Explosion of high energetic materials by using MLR and ANN moels

Mehdi Rahmani ^*

, Mohamad kazem Vahedi

, Mahmoodreza Mahmoodnejad

, Behzad Ahmadi

, rahmani.mehdi82@gmail.com

Abstract: (11988 Views)

Abstract In this work tow simple approaches have been introduced to predict heat of explosion of high energetic materials. experimental heat of explosion of 74 energetic compound were collected from articles and this dataset was separated randomly into two groups, training and prediction sets, respectively, which were used for generation and evaluation of suitable models. Multiple linear regression (MLR) analysis was employed to select the best subset of descriptors and to build linear models while nonlinear models were developed by means of artificial neural network (ANN). The obtained models with four descriptors involved show good predictive power for the test set: a squared correlation coefficient (R2) of 0.798 and a standard error of estimation (s) of 606.48 were achieved by the MLR model while by the ANN model, R2 and SE were 0.98 and 189.4, respectively. Based on the large R2 -value and small SE values, significantly for ANN model, one can deduce that the predicted results are in good agreement with the measured values. Calculated heats of explosion are also compared with corresponding two famous cods, namely EDPHT and LOTUSE. It can be seen that the performance of the present models is better than these cods

Keywords: Key words: energetic materials, heat of explosion, Multiple linear regression, artificial neural network

Full-Text [PDF 774 kb] (326 Downloads)

Type of Study: T | Subject: مدلسازی
Published: 2014/03/4