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There are two basic categories: There are two basic categories: 1. Feed-forward Neural Networks These are the nets in which the signals flow from the input units to the output units, in a forward direction. These are the nets in which the signals flow from the input units to the output units, in a forward direction. They are further classified as: They are further classified as: 1. Single Layer Nets 2. Multi-layer Nets 2. Recurrent Neural Networks These are the nets in which the signals can flow in both directions from the input to the output or vice versa. These are the nets in which the signals can flow in both directions from the input to the output or vice versa.

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X1X1X1X1 X2X2X2X2 llllll XnXnXnXn Input Units Y1Y1Y1Y1 Y2Y2Y2Y2 YmYmYmYm llllll Output Units w 11 w 21 w 31 w 12 w 32 w 13 w 2m w nm w 22

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Biological Neurons In Action llllll Hidden Units llllll Z1Z1Z1Z1 ZjZjZjZj ZpZpZpZp Output Units Y1Y1Y1Y1 llllll YkYkYkYk llllll YmYmYmYm Input Units llllll llllll XiXiXiXi X1X1X1X1 XnXnXnXn w 11 w i1 w n1 w 1j w ij w nj w 1p w ip w np v 11 v j1 v p1 v 1k v jk v pk v 1m v jm v pm

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Supervised Training Training is accomplished by presenting a sequence of training vectors or patterns, each with an associated target output vector. Training is accomplished by presenting a sequence of training vectors or patterns, each with an associated target output vector. The weights are then adjusted according to a learning algorithm. The weights are then adjusted according to a learning algorithm. During training, the network develops an associative memory. It can then recall a stored pattern when it is given an input vector that is sufficiently similar to a vector it has learned. During training, the network develops an associative memory. It can then recall a stored pattern when it is given an input vector that is sufficiently similar to a vector it has learned. Unsupervised Training A sequence of input vectors is provided, but no traget vectors are specified in this case. A sequence of input vectors is provided, but no traget vectors are specified in this case. The net modifies its weights and biases, so that the most similar input vectors are assigned to the same output unit. The net modifies its weights and biases, so that the most similar input vectors are assigned to the same output unit.

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Computational Intelligence Winter Term 2011/12 Prof. Dr. Günter Rudolph Lehrstuhl für Algorithm Engineering (LS 11) Fakultät für Informatik TU Dortmund.

Computational Intelligence Winter Term 2011/12 Prof. Dr. Günter Rudolph Lehrstuhl für Algorithm Engineering (LS 11) Fakultät für Informatik TU Dortmund.

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