Transfer learning with long term artificial neural network memory (LTANN-MEM) and neural symbolization algorithm (NSA) for solving high dimensional multi-objective symbolic regression problems

2017 ◽  
Author(s):  
Amr K. Deklel ◽  
Mohamed A. Saleh ◽  
Alaa M. Hamdy ◽  
Elsayed M. Saad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Learning ◽  
Symbolic Regression ◽  
High Dimensional ◽  
Multi Objective ◽  
Regression Problems ◽  
Artificial Neural

Inverse Artificial Neural Network for Multi-Objective Antenna Design

2021 ◽  
pp. 1-1
Author(s):  
Li-Ye Xiao ◽  
Wei Shao ◽  
Fu-Long Jin ◽  
Bing-Zhong Wang ◽  
Qing Huo Liu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Antenna Design ◽  
Multi Objective ◽  
Artificial Neural

scholarly journals A Surrogate Model Based on Artificial Neural Network for RF Radiation Modelling with High-Dimensional Data

2020 ◽  
Vol 17 (7) ◽  
pp. 2586 ◽  
Author(s):  
Xi Cheng ◽  
Clément Henry ◽  
Francesco P. Andriulli ◽  
Christian Person ◽  
Joe Wiart
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Uncertainty Quantification ◽  
Network Architecture ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Attractive Alternative ◽  
Neural Network Architecture ◽  
Radiation Modelling ◽  
Artificial Neural

This paper focuses on quantifying the uncertainty in the specific absorption rate values of the brain induced by the uncertain positions of the electroencephalography electrodes placed on the patient’s scalp. To avoid running a large number of simulations, an artificial neural network architecture for uncertainty quantification involving high-dimensional data is proposed in this paper. The proposed method is demonstrated to be an attractive alternative to conventional uncertainty quantification methods because of its considerable advantage in the computational expense and speed.

Aerodynamic Optimization Design of Compressor Cascade Based on Parallel Multi-Objective Genetic Algorithm and Artificial Neural Network

2011 ◽  
Vol 138-139 ◽  
pp. 534-539
Author(s):  
Li Hai Chen ◽  
Qing Zhen Yang ◽  
Jin Hui Cui
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Optimization Design ◽  
Compressor Cascade ◽  
Aerodynamic Optimization ◽  
Navier Stokes Equation ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Artificial Neural

Genetic algorithm (GA) is improved with fast non-dominated sort approach and crowded comparison operator. A new algorithm called parallel multi-objective genetic algorithm (PMGA) is developed with the support of Massage Passing Interface (MPI). Then, PMGA is combined with Artificial Neural Network (ANN) to improve the optimization efficiency. Training samples of the ANN are evaluated based on the two-dimensional Navier-Stokes equation solver of cascade. To demonstrate the feasibility of the hybrid algorithm, an optimization of a controllable diffusion cascade is performed. The optimization results show that the present method is efficient and trustiness.

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