The particle swarm optimization (PSO) algorithm, which uses the best experience of an individual and its neighborhood to find the optimum solution, has proven useful in solving various optimization problems, including multiobjective optimization (MOO) problems. In MOO problems, existing multi-objective...
In this study, the combination of artificial neural network (ANN) and non-dominated sorting genetic algorithm II (NSGA-II) has been implemented for modelin... S Lotfan,RA Ghiasi,M Fallah,... - 《Applied Energy》 被引量: 10发表: 2016年 Multiobjective Optimization — New Formulation and Ap...
A. An MLP works by passing input data through multiple layers of interconnected neurons, each layer transforming the input, learning features, and contributing to the network’s ability to make accurate predictions or classifications. Q4.What is the architecture of the Multilayer Perceptron (MLP) ne...
Specifically, this is the first study utilizing a multi-objective optimization approach to optimize the process parameters of a grinding robot. Based on the experimental data of the grinding robot ROKAE XB7, the long short-term memory (LSTM) and multilayer perceptron (MLP) ...
卷积神经网络(CNN)作为医学图像分割领域中U-Net基线网络的重要组成部分,其主要作用是处理局部特征信息之间的关系.而Transformer是一种能够有效强化特征信息之间的远距离依赖关系的视觉模型.目前的研究表明,结合Transformer和CNN可以在一定程度上提高医学图像分割的准确性.但是,由于医学图像的标注数据较少,而且训练Transformer模...
implementing them to improve the performance and training of this network. In order to support your experiments, we have acquired Google Cloud Platform credits which allow the use of the Google Compute Engine infrastructure. You will need this to run all the tasks of the coursework (which ...
the parameters of the simple network with one hidden layer, whose computational graph is in :numref:`fig_forward`, are $\mathbf{W}^{(1)}$ and $\mathbf{W}^{(2)}$. The objective of backpropagation is to calculate the gradients $\partial J/\partial \mathbf{W}^{(1)}$ and $\parti...
Sultan Noman Qasem, Siti Mariyam Shamsuddin, “Radial basis function network based on time variant multiobjective particle swarm optimization for medical diseases diagnosis”, Elsevier: Applied Soft Computing, Vol. 11, pp. 1427-1438, 2007. Asha Gowda Karegowda , A.S. Manjunath , M.A. Jaya...
The new optimization structure has the objective to find a neural network structure capable to represent the process quantitatively and qualitatively. The sensitivity factors, when compared with the expert knowledge of the human agent, represented through symbolic rules, can evaluate not only the ...
theoptimizationofthisnon-convexobjectivefunctionisoftenachievedbyback-propagation,whichisnotguar-anteedtofindaglobaloptimum.Similarly,mostoftheexistingsolutionstoMLPadaptationhavethesameobjectiveasMLPlearning,andacommonadaptationstrategyiseitherpartiallyretrainingnetworkparame-ters,oraddingaugmentative,speaker-dependent...