Journal ArticleDOI
A neural network learning-based global optimization approach for aero-engine transient control schedule
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In this article , a surrogate-assisted optimization framework is presented by using the learning capability of neural networks, which is achieved by presenting a sequential ensemble radial basis function (RBF) neural network-based optimization algorithm.About:
This article is published in Neurocomputing.The article was published on 2022-01-01. It has received 4 citations till now. The article focuses on the topics: Transient (computer programming) & Schedule.read more
Citations
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Turbo-fan engine acceleration control schedule optimization based on DNN-LPV model
TL;DR: In this article , an online data-driven linear parameter varying model based on a special structure deep neural network (DNN) is proposed to establish the predictive model to ensure prediction accuracy and modeling efficiency.
Journal ArticleDOI
Guest Editorial: Special Issue on Neural Networks-based Reinforcement Learning Control of Autonomous Systems
Hamid Reza Karimi,M. A Duenas-Lopez,Ning Wang,Vinicius Juvino dos Santos,Xu Jin,Ali Zemouche +5 more
TL;DR: In this paper, by combining with neural networks and reinforcement learning, advances in the reinforcement learning technologies of autonomous systems are exclusively pursued in this special issue, by combining reinforcement learning and neural networks.
Journal ArticleDOI
Guest Editorial: Special issue on neural networks-based reinforcement learning control of autonomous systems
TL;DR: In this article , by combining with neural networks and reinforcement learning, advances in the reinforcement learning technologies of autonomous systems are exclusively pursued in this special issue, by combining reinforcement learning and neural networks.
Journal ArticleDOI
A multi-input based full envelope acceleration schedule design method for gas turbine engine based on multilayer perceptron network
TL;DR: In this article , a multi-input based (MIB) method for high-precision acceleration schedule design is proposed, which integrates a combined input selection (CIS) strategy and a multilayer perceptron (MLP) network.
References
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Journal ArticleDOI
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
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Efficient Global Optimization of Expensive Black-Box Functions
TL;DR: This paper introduces the reader to a response surface methodology that is especially good at modeling the nonlinear, multimodal functions that often occur in engineering and shows how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule.
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Ensemble of surrogates
TL;DR: The utility of an ensemble of surrogate models is extended to identify regions of possible high errors at locations where predictions of surrogates widely differ, and provide a more robust approximation approach.
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Genetic Algorithms and Very Fast Simulated Reannealing: A comparison
Lester Ingber,B. Rosen +1 more
TL;DR: This work compares Genetic Algorithms with a functional search method, Very Fast Simulated Reannealing (VFSR), that not only is efficient in its search strategy, but also is statistically guaranteed to find the function optima.
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Multiple surrogates: how cross-validation errors can help us to obtain the best predictor
TL;DR: This paper discussed how PRESS is employed to estimate the RMS error, and whether to use the best PRESS solution or a weighted surrogate when a single surrogate is needed, and found that PRESS as obtained through the k-fold strategy successfully estimates the RMSE.