Advances in Nonlinear Network Models and Algorithms

1989 ◽  
pp. 45-72
Author(s):  
John M. Mulvey
Keyword(s):  
Network Models ◽  
Nonlinear Network

Facial Expression Recognition Method Based on Improved VGG Convolutional Neural Network

2019 ◽  
Vol 34 (07) ◽  
pp. 2056003
Author(s):  
Shuo Cheng ◽  
Guohui Zhou
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Network Structure ◽  
Facial Expression Recognition ◽  
Network Models ◽  
Experimental Results ◽  
Expression Recognition ◽  
Classification Problems ◽  
Nonlinear Network

Because the shallow neural network has limited ability to represent complex functions with limited samples and calculation units, its generalization ability will be limited when it comes to complex classification problems. The essence of deep learning is to learn a nonlinear network structure, to represent input data distributed representation and demonstrate a powerful ability to learn deeper features of data from a small set of samples. In order to realize the accurate classification of expression images under normal conditions, this paper proposes an expression recognition model of improved Visual Geometry Group (VGG) deep convolutional neural network (CNN). Based on the VGG-19, the model optimizes network structure and network parameters. Most expression databases are unable to train the entire network from the start due to lack of sufficient data. This paper uses migration learning techniques to overcome the shortage of image training samples. Shallow CNN, Alex-Net and improved VGG-19 deep CNN are used to train and analyze the facial expression data on the Extended Cohn–Kanade expression database, and compare the experimental results obtained. The experimental results indicate that the improved VGG-19 network model can achieve 96% accuracy in facial expression recognition, which is obviously superior to the results of other network models.

OR Practice—Large-Scale Nonlinear Network Models and Their Application

1989 ◽  
Vol 37 (3) ◽  
pp. 353-372 ◽  
Author(s):  
Ron S. Dembo ◽  
John M. Mulvey ◽  
Stavros A. Zenios
Keyword(s):  
Large Scale ◽  
Network Models ◽  
Nonlinear Network ◽  
Or Practice

scholarly journals Input-to-State Stability for Dynamical Neural Networks with Time-Varying Delays

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Weisong Zhou ◽  
Zhichun Yang
Keyword(s):  
Sufficient Conditions ◽  
Network Models ◽  
Functional Method ◽  
Linear Matrix ◽  
Time Varying ◽  
Neural Network Models ◽  
Network Systems ◽  
Nonlinear Network ◽  
Dynamical Neural Network ◽  
Dynamical Neural Networks

A class of dynamical neural network models with time-varying delays is considered. By employing the Lyapunov-Krasovskii functional method and linear matrix inequalities (LMIs) technique, some new sufficient conditions ensuring the input-to-state stability (ISS) property of the nonlinear network systems are obtained. Finally, numerical examples are provided to illustrate the efficiency of the derived results.

scholarly journals Energy transfer in nonlinear network models of proteins

2009 ◽  
Vol 88 (6) ◽  
pp. 68001 ◽  
Author(s):  
F. Piazza ◽  
Y.-H. Sanejouand
Keyword(s):  
Energy Transfer ◽  
Network Models ◽  
Nonlinear Network

scholarly journals Discrete Breathers in Nonlinear Network Models of Proteins

2007 ◽  
Vol 99 (23) ◽  
Author(s):  
B. Juanico ◽  
Y.-H. Sanejouand ◽  
F. Piazza ◽  
P. De Los Rios
Keyword(s):  
Network Models ◽  
Nonlinear Network ◽  
Discrete Breathers

Nonlinear Network Models of the Oculomotor Integrator

1997 ◽  
pp. 371-377 ◽  
Author(s):  
D. D. Lee ◽  
B. Y. Reis ◽  
H. S. Seung ◽  
D. W. Tank
Keyword(s):  
Network Models ◽  
Nonlinear Network

What's in a model? Network models as tools instead of representations of what psychiatric disorders really are

2019 ◽  
Vol 42 ◽  
Author(s):  
Hanna M. van Loo ◽  
Jan-Willem Romeijn
Keyword(s):  
Psychiatric Disorders ◽  
Network Models ◽  
Model Network

AbstractNetwork models block reductionism about psychiatric disorders only if models are interpreted in a realist manner – that is, taken to represent “what psychiatric disorders really are.” A flexible and more instrumentalist view of models is needed to improve our understanding of the heterogeneity and multifactorial character of psychiatric disorders.

Special, radical, failure of reduction in psychiatry

2019 ◽  
Vol 42 ◽  
Author(s):  
Don Ross
Keyword(s):  
Mental Disorder ◽  
Brain Structure ◽  
Causal Structure ◽  
Network Models ◽  
Gambling Addiction ◽  
Brain Disorder ◽  
Mental Processes ◽  
Special Radical ◽  
Intentional Relationships

AbstractUse of network models to identify causal structure typically blocks reduction across the sciences. Entanglement of mental processes with environmental and intentional relationships, as Borsboom et al. argue, makes reduction of psychology to neuroscience particularly implausible. However, in psychiatry, a mental disorder can involve no brain disorder at all, even when the former crucially depends on aspects of brain structure. Gambling addiction constitutes an example.

Application of Lattice Imaging Techniques to Amorphous Films

1975 ◽  
Vol 33 ◽  
pp. 8-9
Author(s):  
S. R. Herd ◽  
P. Chaudhari
Keyword(s):  
Nearest Neighbor ◽  
Random Network ◽  
Imaging Techniques ◽  
Network Models ◽  
Accurate Method ◽  
Direct Transmission ◽  
Lattice Imaging ◽  
Transmission Electron ◽  
Lattice Planes ◽  
Nearest Neighbor Distances

Electron diffraction and direct transmission have been used extensively to study the local atomic arrangement in amorphous solids and in particular Ge. Nearest neighbor distances had been calculated from E.D. profiles and the results have been interpreted in terms of the microcrystalline or the random network models. Direct transmission electron microscopy appears the most direct and accurate method to resolve this issue since the spacial resolution of the better instruments are of the order of 3Å. In particular the tilted beam interference method is used regularly to show fringes corresponding to 1.5 to 3Å lattice planes in crystals as resolution tests.

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