Development of multilayer perceptron networks for isothermal time temperature transformation prediction of UMoX alloys
Abstract
In this work, a multilayered perceptron (MLP) network is used to develop predictive isothermal timetemperaturetransformation (TTT) models covering a range of UMo binary and ternary alloys. The selected ternary alloys for model development are UMoRu, UMoNb, UMoZr, UMoCr, and UMoRe. These model’s ability to predict 'novel' UMo alloys is shown quite well despite the discrepancies between literature sources for similar alloys which likely arise from different thermalmechanical processing conditions. These models are developed with the primary purpose of informing experimental decisions. Additional experimental insight is necessary in order to reduce the number of experiments required to isolate ideal alloys. These models allow test planners to evaluate areas of experimental interest; once initial tests are conducted, the model can be updated and further improve followon testing decisions. The model also improves analysis capabilities by reducing the number of data points necessary from any particular test. For example, if one or two isotherms are measured during a test, the model can construct the rest of the TTT curve over a wide range of temperature and time. This modeling capability reduces the cost of experiments while also improving the value of the results from the tests. The reduced costs could result in improvedmore »
 Authors:
 Publication Date:
 Research Org.:
 Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1356480
 Report Number(s):
 PNNLSA120740
Journal ID: ISSN 00223115; 453040075
 DOE Contract Number:
 AC0576RL01830
 Resource Type:
 Journal Article
 Resource Relation:
 Journal Name: Journal of Nuclear Materials; Journal Volume: 490
 Country of Publication:
 United States
 Language:
 English
Citation Formats
Johns, Jesse M., and Burkes, Douglas. Development of multilayer perceptron networks for isothermal time temperature transformation prediction of UMoX alloys. United States: N. p., 2017.
Web. doi:10.1016/j.jnucmat.2017.03.050.
Johns, Jesse M., & Burkes, Douglas. Development of multilayer perceptron networks for isothermal time temperature transformation prediction of UMoX alloys. United States. doi:10.1016/j.jnucmat.2017.03.050.
Johns, Jesse M., and Burkes, Douglas. 2017.
"Development of multilayer perceptron networks for isothermal time temperature transformation prediction of UMoX alloys". United States.
doi:10.1016/j.jnucmat.2017.03.050.
@article{osti_1356480,
title = {Development of multilayer perceptron networks for isothermal time temperature transformation prediction of UMoX alloys},
author = {Johns, Jesse M. and Burkes, Douglas},
abstractNote = {In this work, a multilayered perceptron (MLP) network is used to develop predictive isothermal timetemperaturetransformation (TTT) models covering a range of UMo binary and ternary alloys. The selected ternary alloys for model development are UMoRu, UMoNb, UMoZr, UMoCr, and UMoRe. These model’s ability to predict 'novel' UMo alloys is shown quite well despite the discrepancies between literature sources for similar alloys which likely arise from different thermalmechanical processing conditions. These models are developed with the primary purpose of informing experimental decisions. Additional experimental insight is necessary in order to reduce the number of experiments required to isolate ideal alloys. These models allow test planners to evaluate areas of experimental interest; once initial tests are conducted, the model can be updated and further improve followon testing decisions. The model also improves analysis capabilities by reducing the number of data points necessary from any particular test. For example, if one or two isotherms are measured during a test, the model can construct the rest of the TTT curve over a wide range of temperature and time. This modeling capability reduces the cost of experiments while also improving the value of the results from the tests. The reduced costs could result in improved material characterization and therefore improved fundamental understanding of TTT dynamics. As additional understanding of phenomena driving TTTs is acquired, this type of MLP model can be used to populate unknowns (such as material impurity and other thermal mechanical properties) from past literature sources.},
doi = {10.1016/j.jnucmat.2017.03.050},
journal = {Journal of Nuclear Materials},
number = ,
volume = 490,
place = {United States},
year = 2017,
month = 7
}

An accelerated learning algorithm for multilayer perceptron networks
An accelerated learning algorithm (ABPadaptive back propagation) is proposed for the supervised training of multilayer perceptron networks. The learning algorithm is inspired from the principle of forced dynamics'' for the total error functional. The algorithm updates the weights in the direction of steepest descent, but with a learning rate a specific function of the error and of the error gradient norm. This specific form of this function is chosen such as to accelerate convergence. Furthermore, ABP introduces no additional tuning'' parameters found in variants of the backpropagation algorithm. Simulation results indicate a superior convergence speed for analog problems only, asmore » 
Optimal Parameter for the Training of Multilayer Perceptron Neural Networks by Using Hierarchical Genetic Algorithm
This paper deals with the controversial topic of the selection of the parameters of a genetic algorithm, in this case hierarchical, used for training of multilayer perceptron neural networks for the binary classification. The parameters to select are the crossover and mutation probabilities of the control and parametric genes and the permanency percent. The results can be considered as a guide for using this kind of algorithm. 
Classification of fuels using multilayer perceptron neural networks
Electrical impedance data obtained with an array of conducting polymer chemical sensors was used by a neural network (ANN) to classify fuel adulteration. Real samples were classified with accuracy greater than 90% in two groups: approved and adulterated. 
Multilayer perceptron for nonlinear programming.
A new method for solving nonlinear programming problems within the framework of a multilayer neural network perceptron is proposed. The method employs the Penalty Function method to transform a constrained optimization problem into a sequence of unconstrained optimization problems and then solves the sequence of unconstrained optimizations of the transformed problem by training a series of multilayer perceptrons. The neural network formulation is represented in such a way that the multilayer perceptron prediction error to be minimized mimics the objective function of the unconstrained problem, and therefore, the minimization of the objective function for each unconstrained optimization is attained bymore »