Optimal design of step-stress accelerated degradation tests based on genetic algorithm and neural network

A novel optimal accelerated degradation test design method considering multiple decision variables

(2021) Zhihua Wang et al.   MICROELECTRONICS RELIABILITY

Reliability estimation considering multi-stress monotonic degradation test data with non-constant scale parameter

(2020) Shah Limon et al.   Quality Engineering

Bayesian planning of step-stress accelerated degradation tests under various optimality criteria

(2018) Xiujie Zhao et al.   APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY

Correction to: Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm

(2018) Eric Bradford et al.   JOURNAL OF GLOBAL OPTIMIZATION

Integration method for reliability assessment with multi-source incomplete accelerated degradation testing data

(2017) Le Liu et al.   Quality Engineering

A Bayesian optimal design for accelerated degradation testing based on the Inverse Gaussian process

(2017) Xiaoyang Li et al.   IEEE Access

Bayesian optimal design of step stress accelerated degradation testing

(2015) Xiaoyang Li et al.   Journal of Systems Engineering and Electronics

Machine learning: Trends, perspectives, and prospects

(2015) M. I. Jordan et al.   SCIENCE

Real-time Reliability Evaluation with a General Wiener Process-based Degradation Model

(2013) Xiaolin Wang et al.   QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL

A novel training algorithm for RBF neural network using a hybrid fuzzy clustering approach

(2011) Antonios D. Niros et al.   FUZZY SETS AND SYSTEMS

Optimal design of accelerated degradation tests based on Wiener process models

(2010) Heonsang Lim et al.   JOURNAL OF APPLIED STATISTICS

Modeling of dependent competing risks by first passage times of Wiener processes

(2008) Bo Henry Lindqvist et al.   IIE TRANSACTIONS

Modelling and Pareto optimization of heat transfer and flow coefficients in microchannels using GMDH type neural networks and genetic algorithms

(2007) N. Amanifard et al.   ENERGY CONVERSION AND MANAGEMENT