Titlebook: Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003; Joint International Okyay Kaynak,Ethem Alpaydin,Lei Xu C

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amyloid

Loading Temporal Associative Memory Using the Neuronic EquationWe discuss the loading capacity of the neuronic equation for temporal associative memory. We show explicitly how to synthesize a perfect temporal associative memory using a network of such neurons, where all non-linear aspects can be linearized in tensorial space.

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Untersuchungen an Kettenlaschen,tion mechanism” to guide the neural search process towards high-quality solutions for large-scale static optimization problems. Specifically, a novel methodology that employs gradient-descent in the error space to adapt weights and constraint weight parameters in order to guide the network dynamics

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Gingivitis

https://doi.org/10.1007/978-3-662-33001-2this information measure is minimized to derive new ICA algorithms. Since the convex divergence includes logarithmic information measures as special cases, the presented method comprises faster algorithms than existing logarithmic ones. Another important feature of this paper’s ICA algorithm is to a

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柳樹；枯黃

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Dawdle

https://doi.org/10.1007/978-3-663-10065-2ation [2]. In this paper we present an ICA algorithm which employs differential learning, thus named as .. We derive a differential ICA algorithm in the framework of maximum likelihood estimation and random walk model. Algorithm derivation using the natural gradient and local stability analysis are