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Easily verifiable conditions are presented for stability and convergence when using such $FD$ estimators for the gradient/Hessian. In addition, our framework ...
This paper focuses on exploring new identification strategies for the feedback nonlinear equation-error moving average systems.
One of its advantage is computational efficiency as it yields a steady error gradient and gives a stable convergence. Sometimes, steady error gradient may ...
Considering a class of gradient-based multi- agent learning algorithms in non-cooperative settings, we provide convergence guarantees to.
Considering a class of gradient-based multi-agent learning algorithms in non-cooperative settings, we provide convergence guarantees to a neighborhood of a ...
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In this paper, we study the convergence of gradient method with batch updating rule for ridge polynomial neural network, and a monotonicity theorem and two ...
Dec 18, 2023 · In this paper, we aim at studying quadratic convex problems by using average stability. Without relying on the bounded gradient assumption, we ...
The SG-ELM algorithm not only guarantees a stable learning but also reduces the computational demand compared to the recursive least squares based OS-ELM ...
Oct 19, 2021 · Stability & Convergence. Gradient descent is heavily studied with nice convergence if the objective function is convex. However, many RL methods ...
Considering a class of gradient-based multi- agent learning algorithms in non-cooperative settings, we provide convergence guarantees to.