can be formulated as an inverse problem, i.e., starting from the design requirements the optimal values of the design parameters have to be obtained, the input of the neural network will correspond to the design parameters while the output is the objective function of the optimization problem....
Qi, M. Li, et al., "An Automatic Cutting Plane Planning Method Based on Multi‐Objective Optimization for Robot‐Assisted Laminectomy Surgery," IEEE Robotics and Automation Letters 10, no. 3 (2025): 2343–2350, https://doi.org/10.1109/lra.2025.3529318. 16. Y. He, P. Zhang, X. Qi, ...
“broken” neural network and quantitatively analyzing the problem behind this broken network. The second part involves exploring two solutions in literature for fixing this “broken” neural network, and then subsequently implementing them to improve the performance and training of this network. In ord...
We have provided a comparative analysis of the performance of a one-dimensional convolutional neural network (1D-CNN) model on two different training regimes: with undersampling (Table 4) and without undersampling (Table 5). Both tables include combinations of four datasets: WISDM, DALIAC, Motion...
The gating signal, which controls how information moves across the network, can either be learnt or fixed. For example, if the gating signal is close to one, then the output of the MLP is passed through to the next layer unaltered. If it is closer to zero, the output is attenuated, ...
First, we transform the users’ information into vectors and use SVD method to reduce dimensions and then learn the preferences and interests of all users based on the improved kernel function and map them to the network; finally, we predict the user’s rating for the items through the ...
theoptimizationofthisnon-convexobjectivefunctionis oftenachievedbyback-propagation,whichisnotguar- anteedtofindaglobaloptimum.Similarly,mostof theexistingsolutionstoMLPadaptationhavethesame objectiveasMLPlearning,andacommonadaptation strategyiseitherpartiallyretrainingnetworkparame- ters,oraddingaugmentative,speaker-dep...
Furthermore, based on FEM results the multilayers perceptron artificial neural network for the approximation of relation between design variables and objectives is implemented. Finally, using the nondominated sorting genetic algorithm銑 , the optimization procedure of the FSPM motor is done. The ...
Therefore, a lightweight medical diagnosis network CTMLP based on convolutions and multi-layer perceptrons (MLPs) is proposed for the diagnosis of COVID-19. The previous self-supervised algorithms are based on CNNs and VITs, and the effectiveness of such algorithms for MLPs is not yet known....
Then, the initial samples data of the optimization procedure is obtained by 2D finite element method (FEM) model of the FSPM motor, which is validated by the prototype of the motor. Furthermore, based on FEM results the multilayers perceptron artificial neural network for the approximation of ...