Abstract: The radial basis function network-based autoregressive with exogenous input (RBF-ARX) model is the nonlinear model based on state dependence with additional input. This brief discusses the ...
Support vector regression can predict numeric values effectively, and this article shows how to implement and train a kernel SVR model in C# using stochastic sub-gradient descent.
Abstract: In this paper, an adaptive neural network (NN) backstepping control method is designed for a class of uncertain fractional order nonlinear systems with external disturbance and input ...
MKLMM is a Python package for predition of complex phenotypes from single nucleotide polymorphism (SNP) data that can model genetic interactions by using multi-kernel-learning techniques. The model is ...
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New computational method combines modern density functional with adaptive algorithm to predict semiconductor properties
Semiconductors are central to modern technology. They are used in computer chips, solar cells, sensors, LEDs and ...
This repository implementates 6 frameworks for hyperspectral image classification based on PyTorch and sklearn. The detailed results can be seen in the Classification of Hyperspectral Image Based on ...
Artificial intelligence is increasingly deployed in time-critical systems that must convert information into action on sub-microsecond timescales. Integrated photonics offers a route to such ...
This research addresses these gaps by proposing a comprehensive multiobjective optimization framework for sustainable flyover design. The framework integrates RBF surrogate modeling with advanced ...
Parameters for the k-GTM algorithm are the square root of the number of grid points (k), the square root of the number of RBF functions (m), the regularization coefficient (l), the RBF width factor (w ...
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