Adaptive filtering based on recurrent neural networks

Adaptive Filtering based on Recurrent Neural Networks

Kalman filter is an optimal filtering solution in certain cases, however, it is more often than not, regarded as a non-robust filter. The slight mismatch in noise statistics or process model may lead to large performance deterioration and the loss of optimality. This research paper proposes an alternative method for robust adaptive filtering concerning lack of information of noise statistics. T...

Adaptive Control Schemes Based on Recurrent Trainable Neural Networks

Abstract: The aim of the present paper is to integrate a recurrent neural network in two schemes of real-time soft computing neural control. There are applied the following control schemes: an indirect and a direct trajectory tracking control, using the state and parameter information, given by an identification recurrent neural network. The applicability of the proposed control schemes is conf...

Quantum recurrent neural networks for filtering

Collaborative Filtering with Recurrent Neural Networks

We show that collaborative filtering can be viewed as a sequence prediction problem, and that given this interpretation, recurrent neural networks offer very competitive approach. In particular we study how the long short-term memory (LSTM) can be applied to collaborative filtering, and how it compares to standard nearest neighbors and matrix factorization methods on movie recommendation. We sh...

Image Backlight Compensation Using Recurrent Functional Neural Fuzzy Networks Based on Modified Differential Evolution

In this study, an image backlight compensation method using adaptive luminance modification is proposed for efficiently obtaining clear images.The proposed method combines the fuzzy C-means clustering method, a recurrent functional neural fuzzy network (RFNFN), and a modified differential evolution.The proposed RFNFN is based on the two backlight factors that can accurately detect the compensat...