Titlebook: Artificial Intelligence and Soft Computing; 19th International C Leszek Rutkowski,Rafa? Scherer,Jacek M. Zurada Conference proceedings 2020

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A New Algorithm with a Line Search for Feedforward Neural Networks Trainingplication of a line search method. Similar algorithms based on the QR decomposition utilize a runtime fixed size of the training step. In some situations that might result in inaccurate weight corrections in a given step. The proposed algorithm solves this issue by finding the exact spot of the opti

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Fast Conjugate Gradient Algorithm for Feedforward Neural Networksion. In this paper a new modification of the conjugate gradient algorithm is presented. The proposed solution speeds up the directional minimization, which result in a significant reduction of the calculation time. This modification of the CG algorithm was tested on selected examples. The performanc

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光滑

Are Direct Links Necessary in Random Vector Functional Link Networks for Regression?e of RVFL is fast training without backpropagation. This is because the weights and biases of hidden nodes are selected randomly and stay untrained. Recently, alternative architectures with randomized learning are developed which differ from RVFL in that they have no direct links and a bias term in

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