Recent achievement of the learning-based classification leads to the noticeable performance improvement in automatic polyp detection. Here, building large good datasets is very crucial for learning a reliable detector. However, it is practically chal...
Neural networks : the official journal of the International Neural Network Society
Apr 7, 2015
This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the pro...
Particle size soft sensing in cement mills will be largely helpful in maintaining desired cement fineness or Blaine. Despite the growing use of vertical roller mills (VRM) for clinker grinding, very few research work is available on VRM modeling. Thi...
Neural networks : the official journal of the International Neural Network Society
Nov 28, 2014
This paper presents a novel artificial neural network with the Radial Basis Function (RBF) as an activation function of neurons and clustered neurons in the hidden layer which has a high learning speed, thus it is called Fast Clustered Radial Basis F...
IEEE transactions on neural networks and learning systems
Oct 13, 2014
This paper presents the Chebyshev neural network (ChNN) as an improved artificial intelligence technique for power system protection studies and examines the performances of two ChNN learning algorithms for fault classification of series compensated ...
IEEE transactions on neural networks and learning systems
Oct 8, 2014
An approximate online equilibrium solution is developed for an N -player nonzero-sum game subject to continuous-time nonlinear unknown dynamics and an infinite horizon quadratic cost. A novel actor-critic-identifier structure is used, wherein a robus...
To deal with heterogeneous classification problem efficiently, each heterogeneous object was represented by a set of measurements obtained on different part of it, and the heterogeneous classification problem was reformulated in the framework of mult...
IEEE transactions on neural networks and learning systems
Sep 11, 2014
A number of neural networks can be formulated as the linear-in-the-parameters models. Training such networks can be transformed to a model selection problem where a compact model is selected from all the candidates using subset selection algorithms. ...
IEEE transactions on neural networks and learning systems
Sep 9, 2014
Single-layer feedforward networks (SLFNs) have been proven to be a universal approximator when all the parameters are allowed to be adjustable. It is widely used in classification and regression problems. The SLFN learning involves two tasks: determi...
IEEE transactions on neural networks and learning systems
Jul 17, 2014
In this paper, an optimized multilayer feed-forward network (MLFN) is developed to construct a soft sensor for controlling naphtha dry point. To overcome the two main flaws in the structure and weight of MLFNs, which are trained by a back-propagation...
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