Results 181 to 190 of about 171,847 (214)

Cooperative computing and neural networks

2006
Artificial Neural Networks (ANNs) are large collections of interacting entities. Certain conditions of behavior and of nonlinear coupling between the entities enable self-organization of the system with emergent properties of associative memory, abstraction and generalization.
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Neural Networks and Computing

2007
This book covers neural networks with special emphasis on advanced learning methodologies and applications. It includes practical issues of weight initializations, stalling of learning, and escape from a local minima, which have not been covered by many existing books in this area.
Tommy W. S. Chow, Siu-Yeung Cho
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CELLULAR NEURAL NETWORKS AND VISUAL COMPUTING

International Journal of Bifurcation and Chaos, 2003
Brain-like information processing has become a challenge to modern computer science and chip technology. The CNN (Cellular Neural Network) Universal Chip is the first fully programmable industrial-sized brain-like stored-program dynamic array computer which dates back to an invention of Leon O. Chua and Lin Yang in Berkeley in 1988.
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Synchronization and computation in a chaotic neural network

Physical Review Letters, 1992
Chaos generated by the internal dynamics of a large neural network can be correlated over large spatial scales. Modulating the spatial coherence of the chaotic fluctuations by the spatial pattern of the external input provides a robust mechanism for feature segmentation and binding, which cannot be accomplished by networks of oscillators with local ...
, Hansel, , Sompolinsky
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Computation, Cognition, and Neural Networks

1995
Computation and Cognition. To understand computing with neural networks and how cognitive processes can be implemented in this way, it is imperative to distinguish between two kinds of machines and the forms of computation they produce: Finite-state automata (FA’s), and machines such as the Turing machine (TM) or the pushdown automaton (PA).
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Compute-Efficient Neural-Network Acceleration

Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2019
To enhance the performance of FPGA-based neural-network accelerators, maximizing both operating clock rates and compute efficiency is paramount. Streamlining data movement between memory and compute holds the key to boosting these metrics. To unleash latent performance in FPGA-based inference processors, we outline a convolutional neural network ...
Ephrem Wu, Xiaoqian Zhang, David Berman, Inkeun Cho, John Thendean   +4 more
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Neural networks for convex hull computation

IEEE Transactions on Neural Networks, 1997
Computing convex hull is one of the central problems in various applications of computational geometry. In this paper, a convex hull computing neural network (CHCNN) is developed to solve the related problems in the N-dimensional spaces. The algorithm is based on a two-layered neural network, topologically similar to ART, with a newly developed ...
Yee Leung, Jiang-She Zhang 0001, Zongben Xu   +2 more
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Computational Neural Networks

1992
Research on neural network modeling has a long history. Neurobiologists have discovered individual nerve cells existing in the brain and learned how neurons carry information, transmit information, and respond to various stimuli. Based on the understanding of the nervous system, many neural networks have been proposed by researchers.
Yi-Tong Zhou, Rama Chellappa
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Neural Network and Neural Computing

Deep learning, a subset of AI, has gained popularity in various fields, including computer vision and NLP. It is based on artificial neural networks, which process multiple layers of data and extract high-level features automatically. Unlike traditional ML algorithms, deep learning can process large unstructured data and complex algorithms better than ...
Partha Ghosh, Suradhuni Ghosh
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Neural networks for computing eigenvalues and eigenvectors

Biological Cybernetics, 1992
The authors consider the problem of computing an eigendecomposition of a square matrix. They formulate the problem as a constrained optimization problem and construct a penalty function to be minimized. They solve the resulting unconstrained optimization problem by designing neural networks and applying a back-propagation learning scheme, which is ...
Andrzej Cichocki, Rolf Unbehauen
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