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Feed-forward topology Topology of a neural network refers to the way the Neurons are connected, and it is an important factor in network functioning and learning. A common topology in unsupervised learning is a direct mapping of inputs to a collection of units that represents categories (e.g., Self-organizing maps). The most common topology in supervised learning is the fully connected, three-layer, feed-forward network. Backpropagation of error (henceforth BP) is a method for training feed-forward neural networks. A specific implementation of BP is an iterative procedure that adjusts network weight parameters according to the gradient of an error measure. A feed-forward neural network is a mathematical function that is composed of constituent "semi-linear" functions constrained by a feed-forward network architecture, wherein the constituent functions correspond to nodes in a graph. Prajakta Pandit 03-21-2017 02:29 AM

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