Parameters Of Neural Network Model Research Articles

Utilizing Genetic Algorithms To Enhance Student Graduation Prediction With Neural Networks

The prediction of student graduation plays a crucial role in improving higher education efficiency and as-sisting students in graduating on time. Neural networks have been used for predicting student graduation; however, the performance of neural network models can still be enhanced to make predictions more accurate. Genetic algorithms are optimization methods used to improve the performance of neural network models by optimizing their parameters. The problem at hand is the suboptimal performance of neural networks in predict-ing student graduation. Thus, the objective is to leverage genetic algorithms to improve the accuracy of stu-dent graduation predictions, measure the improvements obtained, and compare the accuracy results between the genetic algorithm-optimized neural network model and the neural network model without optimization. The training process of the neural network model is conducted using training data obtained through experiments, and the accuracy results of the neural network model with and without genetic algorithm optimization are compared. The research findings indicate that by harnessing genetic algorithms to optimize the parameters of the neural network model, the accuracy of student graduation predictions increased by 2.78%. Furthermore, the Area Under the Curve (AUC) also improved by 0.037%. These results demonstrate that integrating genetic algorithms into the neural network model can significantly enhance prediction performance. Thus, this study successfully utilized genetic algorithms to improve student graduation predictions using a neural network model. Experimental results show that prediction accuracy and AUC values significantly increased after opti-mizing the neural network model's parameters with genetic algorithms. Therefore, the use of genetic algorithms can be considered an effective approach to improving student graduation predictions, thereby assisting educa-tional institutions in improving efficiency and helping students graduate on tim.

DETERMINATION OF THE FORCE IMPACT OF AN ION THRUSTER PLUME ON AN ORBITAL OBJECT VIA DEEP LEARNING

The subject of research is the process of creating a neural network model (NNM) for determining the force impact of an ion thruster (IT) plume on an orbital object during non-contact space debris removal. The work aims to develop NNMs and study the influence of various factors on the accuracy of determining the force transmitted by the ion plume of the thruster to a space debris object (SDO). The tasks to resolve are to choose the structures of the NNMs, form a data set and use this data to train and validate the NNMs, and to explore the influence of the model structure and optimizer parameters on the accuracy of force determination. The methods used are plasma physics, computer simulation, deep learning, and optimization using an improved version of stochastic gradient descent. As a result of research, three NNMs have been developed, which differ in the number of hidden layers and neurons in hidden layers. For training and validation of the NNMs, a data set was generated for an SDO approximated by a cylinder using an autosimilar description of the ion plasma propagation. The data set was obtained for various relative positions and orientations of the object in the process of its removal from an orbit. Using this data set, the NNM parameters were optimized with the supervised learning method. The optimizer and its parameters are selected, providing a small error at the stage of validating learning outcomes. It was found that the accuracy of determining the force depends on the relative position and orientation of the SDO, as well as the architecture of the NNM, and the features of this influence were identified. The approach applied allows us to obtain the possibility of using methods of deep learning to determine the force impact of the IT plume on the SDO. The proposed models provide the accuracy of the force impact determination, which is sufficient for solving the considered class of problems. At the same time, NNM makes it possible to obtain results much faster in comparison with the methods used previously. This fact makes the NNMs promising to use both on-board and in mathematical modeling of missions to remove space debris.

뉴럴네트워크 모델 기반의 IPMC 셀프 센싱 액추에이터

이 논문에서는 뉴럴네트워크 모델에 기초하여 셀프 센싱이 가능한 IPMC 액추에이터를 개발하고자 한다. IPMC의 양면에 있는 두 개의 지정된 점에서 측정된 입력 전압과 입력 신호들을 뉴럴네트워크 모델의 입력 신호로 사용한다. CCD 레이저 변위 센서는 제시된 뉴럴네트워크 모델의 학습된 출력값으로 사용되는 IPMC 끝의 변위를 정확히 측정하기 위해 설치된다. 결과적으로 뉴럴네트워크 모델은 수집된 입력/출력 학습데이터에 의해 최대한으로 활용된 IPMC의 끝의 변위를 평가하기 위해 만들어진다. IPMC 액추에이터를 위해 설계된 모델의 효율성은 결과들을 모델링함으로서 증명되어진다.

Fast response distributed parameter fluidized bed reactor model for propylene partial oxidation using feed-forward neural network methods

The use of a neural network model (NNM) to simulate the performance of a fluidized-bed reactor for the partial oxidation of propylene to acrolein is investigated. The training set needed to generate the NNM is obtained from a two-phase cell model of the fluidized-bed where the flow patterns for the bubble and emulsion phases in each cell are assumed to be plug-flow and perfectly mixed, respectively. The intrinsic kinetics, which are taken from the literature, are based upon a single site redox type model that exhibits a nonlinear dependence on both molecular oxygen and propylene. The formation of acrolein, acetaldehyde, and total combustion products is described by a series-parallel reaction network. The fluidized bed model accounts for variable gas velocity as well as finite transport resistance between the bubble ane emulsion phases. To perform the required NNM training, output responses predicted from the cell model are first generated by using all possible combinations of eleven key input parameters varied over practical ranges of interest. The axial variation of the nine output responses is represented by a recurrent NNM. The NNM parameters are then identified using a special-purpose computer software package that implements both training and analysis of the input data and corresponding output responses. To simulate the behavior of a real reactor, the output responses are corrupted with random noise. Comparisons between the output responses obtained from the NNM trained to noisy data to those from the cell model with no noise indicate that the NNM is capable of providing filtering. Furthermore, a sensitivity analysis indicates that the NNM captures the dependence of the output variables on the input ones.