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- W2479698056 abstract "In this chapter, an alternative to the MFFN is presented which is called a chained network. If all the links are feedforward, this would be a chained feedforward network or CFFN. The computational nodes are the vertices and the links are the edges in a directed graph. Each node has a set of nodes that connect into it the backward set) and a set of nodes that it connects out to (the forward set). The CFFN can be used to map an input set to an output set and the corresponding least squares error function can be minimized using gradient descent. The partial derivatives required are calculated a bit differently from the usual backpropagation algorithm used in the MFFN, but the differences arise mainly from the fact that the CFFN is organized as a graph and the MFFN as a set of matrices. Hence, the mathematical details differ somewhat and the CFFN gradient descent derivation is phrased in terms of direct and total dependence of functions on parameters. A simple set of MatLab codes is developed for the CFFN and used for some examples." @default.
- W2479698056 created "2016-08-23" @default.
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- W2479698056 date "2016-01-01" @default.
- W2479698056 modified "2023-09-27" @default.
- W2479698056 title "Chained Feed Forward Architectures" @default.
- W2479698056 cites W1969166509 @default.
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- W2479698056 doi "https://doi.org/10.1007/978-981-287-871-7_17" @default.
- W2479698056 hasPublicationYear "2016" @default.
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