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Complex dynamics and the structure of small neural networks

F. Pasemann
2002 Network  
doi:10.1080/net. pmid:12061420 fatcat:xvkmqyoezjd5dljyuarvzjsvim

Structure and Performance of Fully Connected Neural Networks: Emerging Complex Network Properties [article]

Leonardo F. S. Scabini, Odemir M. Bruno
2021 arXiv   pre-print
Therefore, we propose Complex Network (CN) techniques to analyze the structure and performance of fully connected neural networks.  ...  Each neural network is approached as a weighted and undirected graph of neurons and synapses, and centrality measures are computed after training.  ...  Conclusion In this work, we explore the correlations between the structure and performance of fully connected neural networks on large vision tasks.  ... 
arXiv:2107.14062v1 fatcat:kcjdj77pxrh5xgs3yfaysun2v4

Learning Dynamics and Structure of Complex Systems Using Graph Neural Networks [article]

Zhe Li, Andreas S. Tolias, Xaq Pitkow
2022 arXiv   pre-print
Our results demonstrated a path towards understanding both dynamics and structure of a complex system and how such understanding can be used for generalization.  ...  Many complex systems are composed of interacting parts, and the underlying laws are usually simple and universal.  ...  Background 2.1 Graph neural network A graph neural network [2, 1, 3] is a message-passing algorithm defined over a graph G = (V, E) with vertices V and edges E.  ... 
arXiv:2202.10996v1 fatcat:whg7lu6krvfqzpoeay6dxvlntu

Economic Structure Analysis Based on Neural Network and Bionic Algorithm

Yanjun Dai, Lin Su, Zhihan Lv
2021 Complexity  
space, and establishes a neuroevolutionary method with shallow neural network and deep neural network as the research objects.  ...  In this article, an in-depth study and analysis of economic structure are carried out using a neural network fusion release algorithm.  ...  Each individual includes all the weights and thresholds of a neural network, and if the network structure is known, a neural network with determined structure, weights, and thresholds can be formed; after  ... 
doi:10.1155/2021/9925823 fatcat:c23wpnoluveoljwe5ukug7cqta

A Convolutional Neural Network for Impact Detection and Characterization of Complex Composite Structures

Iuliana Tabian, Hailing Fu, Zahra Sharif Khodaei
2019 Sensors  
This paper reports on a novel metamodel for impact detection, localization and characterization of complex composite structures based on Convolutional Neural Networks (CNN) and passive sensing.  ...  Methods to generate appropriate input datasets and network architectures for impact localization and characterization were proposed, investigated and optimized.  ...  applications and complex structures.  ... 
doi:10.3390/s19224933 pmid:31726762 pmcid:PMC6891538 fatcat:w73eue62xrgjnbcilift4ztqui

Structure Optimization of a Vibration Suppression Device for Underwater Moored Platforms Using CFD and Neural Network

Zhaoyong Mao, Fuliang Zhao
2017 Complexity  
It is shown that BP neural network and generic algorithm are effective.  ...  The optimal value of the vibration suppression rate is obtained by backpropagation (BP) neural network and genetic algorithm.  ...  Acknowledgments This research was supported by the National Science Foundation of China (Grants nos. 51179159 and 61572404) and the Shaanxi Province Youth Science and Technology New Star Project (Grant  ... 
doi:10.1155/2017/5392539 fatcat:yx6hq2h5uvdx5frnekn3kok6bu

Complexity of neural networks on Fibonacci-Cayley tree

Jung-Chao Ban, Chih-Hung Chang
2019 Journal of Algebra Combinatorics Discrete Structures and Applications  
We apply the result to neural networks defined on Fibonacci-Cayley tree, which reflect those neural systems with neuronal dysfunction.  ...  Aside from demonstrating a surprising phenomenon that there are only two possibilities of entropy for neural networks on Fibonacci-Cayley tree, we address the formula of the boundary in the parameter space  ...  Since neural networks with tree structure come to researcher's attention, it is natural to ask how the complexity of a tree structure neural network can be measured.  ... 
doi:10.13069/jacodesmath.560410 fatcat:wufqypwoznbrrm7vs3bzecqhnu

Mobile Neural Networking Hypothesis for Complex Concept and Its Logical Structure (Digital Linguistics)

Kumon Tokumaru, KOKU, Japan
2019 Journal of clean energy technologies  
Digital Linguistics (DL) proposes that (1) mobile neural networks among immune cells inside ventricle system (VS) are in charge of sign reflex and linguistic processing, and (2) mobile neurons, currently  ...  DL identifies that there are several different complexity orders in concepts, and that Forward Error Correction (FEC) should be implemented against incoming linguistic information.  ...  Mobile Neural Networking Hypothesis for Complex Concept and Its Logical Structure (Digital Linguistics) Kumon Tokumaru International Journal of Computer Theory and Engineering, Vol. 11, No. 3, June 2019  ... 
doi:10.7763/ijcte.2019.v11.1241 fatcat:pykzu4zrmfbhfoiywy3ofux4ue

3D Façade Labeling over Complex Scenarios: A Case Study Using Convolutional Neural Network and Structure-From-Motion

Rodolfo Lotte, Norbert Haala, Mateusz Karpina, Luiz Aragão, Yosio Shimabukuro
2018 Remote Sensing  
Convolutional Neural Networks (CNN) have been a trend for pixel-wise image segmentation, showing flexibility when detecting and classifying any kind of object, even in situations where humans failed to  ...  However, newer neural network architectures have brought back the interest in their application for autonomous classifiers, especially for image classification purposes.  ...  Different models have emerged and contributed to the evolved state of neural networks in the present day.  ... 
doi:10.3390/rs10091435 fatcat:ubagdkkcmbcg5i6it2qy277b5u

Prediction of interface residues in protein-protein complexes by a consensus neural network method: Test against NMR data

Huiling Chen, Huan-Xiang Zhou
2005 Proteins: Structure, Function, and Bioinformatics  
In PPISP, sequence profiles and solvent accessibility of spatially neighboring surface residues were used as input to a neural network.  ...  These structures embed important information for predicting structures of new protein complexes.  ...  The neural network approach has been shown to be quite successful in predicting protein secondary structure and solvent accessibility.  ... 
doi:10.1002/prot.20514 pmid:16080151 fatcat:eettt6nv2nd57lvbchqvgnpkyq

Neural network optimization of complex microwave structures with a reduced number of full-wave analyses

Ethan K. Murphy, Vadim V. Yakovlev
2011 International Journal of RF and Microwave Computer-Aided Engineering  
An innovative technique of neural network optimization of substantially less computational cost is presented as a procedure suitable for viable computer-aided design of complex microwave systems.  ...  in the dynamic training of the decomposed radial-basis-function network.  ...  His research interests include neural-network-based optimization, microwave imaging, multiphysics modeling, and microwave power engineering.  ... 
doi:10.1002/mmce.20514 fatcat:cnjxxr2febhlflpvhbimdijse4


D. A. Akimov, A. D. Kleymenov, S. O. Kozelskaya, O. N. Budadin
2020 Kontrol Diagnostika  
The article proposes a new approach to assessing the operational safety of materials and parts of complex structures based on artificial intelligence methods based on artificial neural networks and multi-criteria  ...  methods and algorithms for assessment of structures and resource forecasting their operational reliability was carried out.  ...  Neural Networks. San Jose, California. 2011.  ... 
doi:10.14489/td.2020.07.pp.018-027 fatcat:ybuabkm2ancobc2svauxcg7upa

Predictive structure and the learnability of inflectional paradigms: investigating whether low i-complexity benefits human learners and neural networks [article]

Tamar Johnson, University Of Edinburgh, Kenny Smith, Hugh Rabagliati, Jennifer Culbertson
Results show weak evidence for an effect of i-complexity on learning, with evidence for greater effects of e-complexity in both human and neural network learners.  ...  In a series of artificial language learning tasks both with human learners and LSTM neural networks, I evaluate the hypothesis that learners are sensitive to i-complexity by testing how well paradigms  ...  Experiment 1: neural networks Network Structure We trained and tested LSTM networks of the same structure as in Chapters 2 and 3.  ... 
doi:10.7488/era/1850 fatcat:hn6ijiu7ofaija332f3milu4nq

An Improved Complex System Optimization Method Hybridized Structure-based Neural Networks with Orthogonal Genetic Algorithm

Si-Jun Tao
2015 Proceedings of the 2nd International Conference on Civil, Materials and Environmental Sciences   unpublished
For this reason, an improved complex system optimization method is proposed which hybridized the structure-based neural networks with the orthogonal genetic algorithm.  ...  The research on optimization methods to complex systems is an important issue in both theoretical and practical significance.  ...  Figure 1 . 1 Structure-based neural network modelB.  ... 
doi:10.2991/cmes-15.2015.138 fatcat:kgu3te7zsvbd7jq7vhi5gqcwla

A Novel High-Speed and High-Accuracy Mathematical Modeling Method of Complex MEMS Resonator Structures Based on the Multilayer Perceptron Neural Network

Qingsong Li, Kuo Lu, Kai Wu, Hao Zhang, Xiaopeng Sun, Xuezhong Wu, Dingbang Xiao
This paper demonstrates a high-speed and high-accuracy simulation tool based on the artificial neural network, where a multilayer perceptron (MLP) neural network model is constructed.  ...  After iteratively trained with 4000 samples, the cumulative error of the neural network decreases to 0.0017 and a prediction network model is obtained.  ...  Acknowledgments: The authors would like to thank the Laboratory of Microsystem, National University of Defense Technology, China, for equipment access and technical support.  ... 
doi:10.3390/mi12111313 pmid:34832725 pmcid:PMC8625225 fatcat:q2ucswkbhfagvpoieamk7e2azq
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