Results 161 to 170 of about 171,847 (214)
Efficiency of Kolmogorov-Arnold Networks in Small Medical Samples (Case Study of 2D Brain MRI Image Segmentation). [PDF]
Sovrem Tekhnologii MedManzhos GY +3 more
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Multivariate control based on recurrent wavelet neural network for wastewater treatment process. [PDF]
PLoS OneFang Y, Su Y, Li Y.
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Multilayer pyramid pooling self-attention for landslide detection using vision transformers. [PDF]
Sci RepSreelakshmi S +3 more
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Federated learning's uncomfortable truth: why human networks matter more than neural networks. [PDF]
J Am Med Inform AssocPeltonen LM, Chomutare T.
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Neural networks for shortest path computation and routing in computer networks
IEEE Transactions on Neural Networks, 1993The application of neural networks to the optimum routing problem in packet-switched computer networks, where the goal is to minimize the network-wide average time delay, is addressed. Under appropriate assumptions, the optimum routing algorithm relies heavily on shortest path computations that have to be carried out in real time.
Mustafa K. Mehmet Ali, Faouzi Kamoun
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Proceedings of ICNN'95 - International Conference on Neural Networks, 2002
In this paper, we discuss an approach for designing the computational neural network, which is mainly composed of a hardlimiter neuron, a updated neuron, and a search function neuron, to solve some computational problems. The computation-by-search scheme can effectively solve some complicated problems in the condition that their search functions can be
Jar-Ferr Yang, Chi-Ming Chen
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In this paper, we discuss an approach for designing the computational neural network, which is mainly composed of a hardlimiter neuron, a updated neuron, and a search function neuron, to solve some computational problems. The computation-by-search scheme can effectively solve some complicated problems in the condition that their search functions can be
Jar-Ferr Yang, Chi-Ming Chen
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Computing with structured neural networks
Computer, 1988The authors are concerned with how one can design, realize, and analyze networks that embody the specific computational structures needed to solve hard problems. They focus on the design and use of massively parallel connectionist computational models, particularly in artificial intelligence.
Jerome A. Feldman +2 more
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Neural computations by networks of oscillators
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000We describe here how a network of oscillators can perform neural computations. In particular, it shown how the connectivity within the network can be created to memorize data in terms of phase relations between synchronized states. The memorized states are extracted through correlation calculations. The influence of noise on the system is discussed.
Frank C. Hoppensteadt +1 more
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Layered Neural Networks Computations
Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks (SNPD/SAWN'05), 2005Among prominent features of the visual networks, movement detections are carried out in the visual cortex. The visual cortex for the movement detection, consist of two layered networks, called the primary visual cortex (VI), followed by the middle temporal area (MT), in which nonlinear functions play important roles in the visual systems. In this paper,
Naohiro Ishii +2 more
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Future Generation Computer Systems, 1991
Abstract In this paper, we give a general presentation of neural networks, showing their links and differences with Artificial Intelligence and neurosciences. We provide the general formalism of neural networks and describe two neural networks learning algorithms: gradient backpropagation and learning vector quantization.
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Abstract In this paper, we give a general presentation of neural networks, showing their links and differences with Artificial Intelligence and neurosciences. We provide the general formalism of neural networks and describe two neural networks learning algorithms: gradient backpropagation and learning vector quantization.
openaire +1 more source