In this study, an online learning algorithm for feedforward neural networks (FNN) based on the optimized learning rate and adaptive forgetting factor is proposed for online financial time series prediction. The new learning algorithm is developed for online predictions in terms of the gradient descent technique, and can speed up the FNN learning process substantially relative to the standard FN...

This paper describes and evaluates how confidence values can be used to improve the quality of exploration in Q-Routing for adaptive packet routing in communication networks. In Q-Routing each node in the network has a routing decision maker that adapts, on-line, to learn routing policies that can sustain high network loads and have low average packet delivery time. These decision makers mainta...

background: increasing frame rate is a challenging issue for better interpretation of medical images and diagnosis based on tracking the small transient motions of myocardium and valves in real time visualization. methods: in this paper, manifold learning algorithm is applied to extract the nonlinear embedded information about echocardiography images from the consecutive images in two dimension...

In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...

Certainty equivalence control with forcing has been shown to be optimal for several stochastic adaptive control problems with the average cost per unit time criterion. Recently researchers have started looking at stochastic adaptive control problems with a view to minimizing the rate of increase of the learning loss. This criterion is stronger than the average cost per unit time criterion. Cert...

This work is concerned with the problem of the real-time identification and control of dynamic non-linear systems using a neural network approach. The chosen model was the Gaussian Radial Basis Function Neural Network (RBFNN) type, due to its universal approximation property and also to the fact that the parameters are linearly related to the outputs allowing linear learning algorithms. A hybri...

Matrix Factorization (MF) has become the predominant technique in recommender systems. The model parameters are usually learned by means of numerical methods, such as gradient descent. The learning rate of gradient descent is typically set to lower values in order to ensure that the algorithm will not miss a local optimum. As a consequence, the algorithm may take several iterations to converge....