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Which artificial neural network allows loops? 

Porosity
Artificial neural network
Time delay neural network
Moving average
Permeability (earth sciences)

Answers from top 10 papers

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Papers (10)Insight
Open access•Posted Content
Neural network design for J function approximation in dynamic programming
X. Pang, P. Werbos 
03 Jun 1998-arXiv: Adaptation and Self-Organizing Systems
69 Citations
This paper shows that a new type of artificial neural network (ANN) -- the Simultaneous Recurrent Network (SRN) -- can, if properly trained, solve a difficult function approximation problem which conventional ANNs -- either feedforward or Hebbian -- cannot.
Proceedings Article•DOI
Analog neural network building blocks based on current mode subthreshold operation
L. Song, Mohamed I. Elmasry, Anthony Vannelli 
03 May 1993
10 Citations
These building blocks can be applied to artificial neural network (ANN) design in particular and to analog signal processing in general.<>
Journal Article•DOI
Relationships between permeability, porosity and pore throat size in carbonate rocks using regression analysis and neural networks
M R Rezaee, A Jafari, E Kazemzadeh 
01 Dec 2006-Journal of Geophysics and Engineering
136 Citations
An artificial neural network, a biologically inspired computing method which has an ability to learn, self-adjust, and be trained, provides a powerful tool in solving complex problems.
Journal Article•DOI
Artificial neural network model for synthetic streamflow generation
Juran Ali Ahmed, Arup Kumar Sarma 
01 May 2007-Water Resources Management
74 Citations
In such event, artificial neural network (ANN) model can be a potential alternative to the conventional models.
Proceedings Article•DOI
Artificial neural networks model design of Lorenz chaotic system for EEG pattern recognition and prediction
Lei Zhang
01 Dec 2017
21 Citations
Artificial neural network (ANN) resembles brain biological neural network and can be used to simulate chaotic system.
Open access
Estimating the Initial Pressure, Permeability and Skin Factor of Oil Reservoirs Using Artificial Neural Networks
Jeirani, Mohebbi 
01 Jan 2005
44 Citations
Artificial neural network, a biologically inspired computing method which has an ability to learn, self-adjust, and be trained, provides a powerful tool in solving pattern recognition problems.
Journal Article•DOI
A combination of artificial neural network and random walk models for financial time series forecasting
Ratnadip Adhikari, Ramesh Kumar Agrawal 
01 May 2014-Neural Computing and Applications
125 Citations
Artificial neural network (ANN) is another promising alternative with the unique capability of nonlinear self-adaptive modeling.
Journal Article•DOI
Crevice corrosion : a neural network approach
S. P. Trasatti, F. Mazza 
01 Jan 1996-British Corrosion Journal
20 Citations
Among these, the artificial neural network (NN) system appears to be a powerful tool to tackle situations in w...
Journal Article•DOI
Automatic target recognition system for unmanned aerial vehicle via backpropagation artificial neural network
Jiaqi Jia, Haibin Duan 
04 Jan 2017-Aircraft Engineering and Aerospace Technology
12 Citations
This kind of network combines or better fuses the advantages of backpropagation artificial neural algorithm and Hu moment.
Proceedings Article•DOI
Characterization of artificial neural network algorithms
T. Baker, Dan Hammerstrom 
08 May 1989
26 Citations
One popular artificial neural network model, the back-propagation algorithm, promises to be a powerful and flexible learning model.

Related Questions

What is human-in-the-loop ai?5 answersHuman-in-the-loop AI refers to an approach in which humans are actively involved in the AI development process, particularly in the design, training, and evaluation stages. This involvement of humans helps in identifying unproductive layers of the AI architecture, leading to the creation of lightweight AI architectures suitable for embedded applications. By enabling human-machine interaction (HMI) during AI inference, the AI-in-the-loop concept combines the strengths of AI and humans. In this approach, the AI primarily solves the task but can seek human assistance through appropriate HMI when unsure about the correctness of its inference. The use of HMI during AI development reduces the resources needed and makes AI more reliable and explainable. Additionally, the concept of human-in-the-loop is also applied in novelty generation, where humans provide guidance in selecting and filtering novelties generated by abstract models of environments.
What are ctcf mediated loops?3 answersCTCF-mediated loops are chromatin loops that are formed and regulated by the CCCTC-binding factor (CTCF) protein. CTCF is a transcription regulator involved in various cellular processes and plays a crucial role in three-dimensional (3D) genome organization. These loops are formed when CTCF binds to specific DNA sequences called CTCF binding sites (CBSs) and acts as an anchor for the cohesin protein complex. The cohesin complex extrudes DNA loops that are anchored by CTCF in a polar orientation. The orientation of CTCF binding polarity controls cohesin-mediated DNA looping, and the presence of CTCF motifs and sequence conservation are important indicators of chromatin loops. CTCF-mediated loops have been found to be involved in gene regulation, cell development, disease progression, and the spatial organization of the genome.
What is the phenomenon of human-in-the-loop training of neural network?5 answersHuman-in-the-loop training of neural networks refers to a training approach that involves the active participation of humans in the training process. In this approach, humans provide feedback or guidance to the neural network during the training phase, which helps improve the performance and interpretability of the model. The feedback can be in the form of labeled data, annotations, or evaluations of the model's output. By incorporating human feedback, the neural network can learn from the expertise and knowledge of humans, leading to better results. This approach has been applied in various domains such as music generation, polyp detection in colonoscopy, synthetic aperture radar target recognition, and feature aggregation in machine learning.
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