Basic Network Models

How to describe the difference among various combat networks and measure their effectiveness is an important problem in combat analysis. In this paper three basic network models are developed based on the theory of complex networks and a new method is put forward for measuring the network effect of combat SoS. Numerical comparisons of the three combat network models indicate that though integrated joint operations network has the highest networked effects in networked effectiveness and clustering coefficient, but when considering the Average Path Length index, it has the lowest effectiveness. The results also suggest that the degree distribution of integrated joint operations network is scale-free thus it has the highest survivability.

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Black Box Models and Sociological Explanations: Predicting GPA Using Neural Networks

10.31235/osf.io/7nsrf ◽

2017 ◽

Author(s):

Thomas Davidson

Keyword(s):

Network Models ◽

Predictive Performance ◽

Black Box ◽

Neural Network Models ◽

Grade Point ◽

Predictive Variables ◽

Box Models ◽

Model Finding ◽

Basic Network ◽

Predicting Gpa

The Fragile Families Challenge provided an opportunity to empirically assess the applicability of black box machine learning models to sociological questions and the extent to which interpretable explanations can be extracted from these models. In this paper I use neural network models to predict high school grade-point average and examine how variations of basic network parameters affect predictive performance. Using a recently proposed technique, I identify the most important predictive variables used by the best-performing model, finding that they relate to parenting and the child’s cognitive and behavioral development, consistent with prior work. I conclude by discussing the implications of these findings for the relationship between prediction and explanation in sociological analyses.

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The Simulation Gene Regulatory Boolean Network Based on the Sequential Circuit

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.686.463 ◽

2014 ◽

Vol 686 ◽

pp. 463-469

Author(s):

Zhen Cheng Fang

Keyword(s):

Regulatory Networks ◽

Boolean Network ◽

Network Models ◽

Boolean Model ◽

Genome Project ◽

The Past ◽

Gene Regulatory ◽

Basic Network ◽

The Relationship ◽

Independent Existence

Along with the completion of HGP (human genome project), huge amounts of genetic data constantly emerge. Research suggests that genes are not in independent existence and the expression of a gene will promote or inhibit the expression of another gene; if the expression of a gene makes the biochemical environment of cells changed, the expression of a series of genes will be affected. In order to get a better understanding of the relationship between genes, all sorts of gene regulatory network models have been established by scientists. In this paper, a variety of gene regulatory networks are first introduced according to the process of this subject research, and then the most basic network (i.e. Boolean network) is emphatically analyzed, and then a new method (i.e. Boolean network based on the theory of circuit) to describe Boolean network is drawn forth. After the shortcomings of the Boolean network proposed in the past are analyzed, a simulation circuit Boolean model is established using EDA technology in order to improve the Boolean network.

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Modular Construction of Time-Delay Neural Networks for Speech Recognition

Neural Computation ◽

10.1162/neco.1989.1.1.39 ◽

1989 ◽

Vol 1 (1) ◽

pp. 39-46 ◽

Cited By ~ 196

Author(s):

Alex Waibel

Keyword(s):

Neural Networks ◽

Speech Recognition ◽

Time Delay ◽

Recognition Performance ◽

Network Models ◽

Modular Construction ◽

Problem Of Time ◽

Incremental Design ◽

Basic Network ◽

Time Invariant

Several strategies are described that overcome limitations of basic network models as steps towards the design of large connectionist speech recognition systems. The two major areas of concern are the problem of time and the problem of scaling. Speech signals continuously vary over time and encode and transmit enormous amounts of human knowledge. To decode these signals, neural networks must be able to use appropriate representations of time and it must be possible to extend these nets to almost arbitrary sizes and complexity within finite resources. The problem of time is addressed by the development of a Time-Delay Neural Network; the problem of scaling by Modularity and Incremental Design of large nets based on smaller subcomponent nets. It is shown that small networks trained to perform limited tasks develop time invariant, hidden abstractions that can subsequently be exploited to train larger, more complex nets efficiently. Using these techniques, phoneme recognition networks of increasing complexity can be constructed that all achieve superior recognition performance.

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

Behavioral and Brain Sciences ◽

10.1017/s0140525x18001206 ◽

2019 ◽

Vol 42 ◽

Cited By ~ 2

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.

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Special, radical, failure of reduction in psychiatry

Behavioral and Brain Sciences ◽

10.1017/s0140525x18001164 ◽

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.

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Application of Lattice Imaging Techniques to Amorphous Films

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100113664 ◽

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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