A generalized learning paradigm exploiting the structure of feedforward neural networks

Parisi, Raffaele; Claudio, Elio D. Di; Orlandi, G.; Rao, Bhaskar D.

doi:10.1109/72.548172

Cited by 77 publications

(32 citation statements)

References 20 publications

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“…Except for the classical VICOM [17], linearization was performed by a five parameter monotonic logistic function [30], [31], trained by the BRLS modified Newton algorithm [53] over several runs, to minimize the risk of trapping into local minima.…”

Section: Experiments and Resultsmentioning

confidence: 99%

A Detail-Based Method for Linear Full Reference Image Quality Prediction

Claudio

Jacovitti

2018

IEEE Trans. on Image Process.

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Abstract-In this paper, a novel Full Reference method is proposed for image quality assessment, using the combination of two separate metrics to measure the perceptually distinct impact of detail losses and of spurious details. To this purpose, the gradient of the impaired image is locally decomposed as a predicted version of the original gradient, plus a gradient residual. It is assumed that the detail attenuation identifies the detail loss, whereas the gradient residuals describe the spurious details. It turns out that the perceptual impact of detail losses is roughly linear with the loss of the positional Fisher information, while the perceptual impact of the spurious details is roughly proportional to a logarithmic measure of the signal to residual ratio. The affine combination of these two metrics forms a new index strongly correlated with the empirical Differential Mean Opinion Score (DMOS) for a significant class of image impairments, as verified for three independent popular databases. The method allowed alignment and merging of DMOS data coming from these different databases to a common DMOS scale by affine transformations. Unexpectedly, the DMOS scale setting is possible by the analysis of a single image affected by additive noise.

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Section: Experiments and Resultsmentioning

confidence: 99%

A Detail-Based Method for Linear Full Reference Image Quality Prediction

Claudio

Jacovitti

2018

IEEE Trans. on Image Process.

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“…In addition, to speed up the convergence the Levenberg-Marquardt algorithm is used with backpropagation algorithm. LevenbergMarquardt algorithm is the most powerful and a popular second derivative based algorithm that have been proposed for the training of feed-forward networks which combines the local convergence properties of Gauss-Newton method near a minimum with the consistent error decrease provided by gradient descent far away from the solution [15][16][17][18]. Sigmoid function is used as neuron transfer function.…”

Section: Introductionmentioning

confidence: 99%

Ability to forecast unsteady aerodynamic forces of flapping airfoils by artificial neural network

Kurtuluş

2008

Neural Comput & Applic

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The ability of artificial neural networks (ANN) to model the unsteady aerodynamic force coefficients of flapping motion kinematics has been studied. A neural networks model was developed based on multi-layer perception (MLP) networks and the Levenberg-Marquardt optimization algorithm. The flapping kinematics data were divided into two groups for the training and the prediction test of the ANN model. The training phase led to a very satisfactory calibration of the ANN model. The attempt to predict aerodynamic forces both the lift coefficient and drag coefficient showed that the ANN model is able to simulate the unsteady flapping motion kinematics and its corresponding aerodynamic forces. The shape of the simulated force coefficients was found to be similar to that of the numerical results. These encouraging results make it possible to consider interesting and new prospects for the modelling of flapping motion systems, which are highly non-linear systems.

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“…But, until now, the most widely used NNs, FNs, and WNs systems are algebraic systems, despite the immense popularity of the algebraic neural, fuzzy, and wavelet systems (or feedforward networks) that are usually implemented for the approximation of a non-linear function [13,23,50,52,69,73,75].…”

Section: Introductionmentioning

confidence: 99%

On three intelligent systems: dynamic neural, fuzzy, and wavelet networks for training trajectory

Becerikli

2004

Neural Comput & Applic

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Intelligent systems cover a wide range of technologies related to hard sciences, such as modeling and control theory, and soft sciences, such as the artificial intelligence (AI). Intelligent systems, including neural networks (NNs), fuzzy logic (FL), and wavelet techniques, utilize the concepts of biological systems and human cognitive capabilities. These three systems have been recognized as a robust and attractive alternative to the some of the classical modeling and control methods. The application of classical NNs, FL, and wavelet technology to dynamic system modeling and control has been constrained by the non-dynamic nature of their popular architectures. The major drawbacks of these architectures are the curse of dimensionality, such as the requirement of too many parameters in NNs, the use of large rule bases in FL, the large number of wavelets, and the long training times, etc. These problems can be overcome with dynamic network structures, referred to as dynamic neural networks (DNNs), dynamic fuzzy networks (DFNs), and dynamic wavelet networks (DWNs), which have unconstrained connectivity and dynamic neural, fuzzy, and wavelet processing units, called ''neurons'', ''feurons'', and ''wavelons'', respectively. The structure of dynamic networks are based on Hopfield networks. Here, we present a comparative study of DNNs, DFNs, and DWNs for non-linear dynamical system modeling. All three dynamic networks have a lag dynamic, an activation function, and interconnection weights. The network weights are adjusted using fast training (optimization) algorithms (quasiNewton methods). Also, it has been shown that all dynamic networks can be effectively used in non-linear system modeling, and that DWNs result in the best capacity. But all networks have non-linearity properties in non-linear systems. In this study, all dynamic networks are considered as a non-linear optimization with dynamic equality constraints for non-linear system modeling. They encapsulate and generalize the target trajectories. The adjoint theory, whose computational complexity is significantly less than the direct method, has been used in the training of the networks. The updating of weights (identification of network parameters) is based on Broyden-Fletcher-Goldfarb-Shanno method. First, phase portrait examples are given. From this, it has been shown that they have oscillatory and chaotic properties. A dynamical system with discrete events is modeled using the above network structure. There is a localization property at discrete event instants for time and frequency in this example.

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A generalized learning paradigm exploiting the structure of feedforward neural networks

Cited by 77 publications

References 20 publications

A Detail-Based Method for Linear Full Reference Image Quality Prediction

A Detail-Based Method for Linear Full Reference Image Quality Prediction

Ability to forecast unsteady aerodynamic forces of flapping airfoils by artificial neural network

On three intelligent systems: dynamic neural, fuzzy, and wavelet networks for training trajectory

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