Titlebook: Competitively Inhibited Neural Networks for Adaptive Parameter Estimation; Michael Lemmon Book 1991 Springer Science+Business Media New Yo

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Summary,This chapter summarizes the principal accomplishments of this research. The significance of these contributions is examined in the context of current neural network research. Finally, a number of important research questions are posed for future study.

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https://doi.org/10.1007/978-1-4615-4044-1algorithms; artificial neural network; cognitive psychology; electrical engineering; learning; network; ne

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https://doi.org/10.1007/978-3-642-99333-6ity and steady states of the system. These theorems provide a parametric characterization of the CINN’s steady states. The characterization allows us to predict the network’s STM and LTM states given the input vector. We can fashion these results into an algorithm which we call the CINN Algorithm. T

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,Metastatische Knochengeschwülste,f the source’s pdf. In this respect, the LTM state vectors can be interpreted as estimates of the source density’s modes. Parameter estimation problems are often solved by finding the maximum mode of a cost function or suitable probability density function [53]. It is therefore logical to see if a C