By Ben Krose, Patrick van der Smagt

This manuscript makes an attempt to supply the reader with an perception in synthetic neural networks.

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Create inputs x , x 0 , x ", : : :. Besides only inputting x (t), we also input its rst, second, etc. 47 48 CHAPTER 5. RECURRENT NETWORKS derivatives. Naturally, computation of these derivatives is not a trivial task for higher-order derivatives. The disadvantage is, of course, that the input dimensionality of the feed-forward network is multiplied with n, leading to a very large network, which is slow and di cult to train. The Jordan and Elman networks provide a solution to this problem. Due to the recurrent connections, a window of inputs need not be input anymore instead, the network is supposed to learn the in uence of the previous time steps itself.

4) and the networks is trained with these samples. Training is stopped when the error does not decrease anymore. 7A as a dashed line. The learning samples and the approximation of the network are shown in the same gure. We see that in this case E learning is small (the network output goes perfectly through the learning samples) but E test is large: the test error of the network is large. 7B. The E learning is larger than in the case of 5 learning samples, but the E test is smaller. This experiment was carried out with other learning set sizes, where for each learning set size the experiment was repeated 10 times.

Each row in the matrix represents a city, whereas each column represents the position in the tour. When the network is settled, each row and each column should have one and only one active neuron, indicating a speci c city occupying a speci c position in the tour. 2) with a sigmoid activation function between 0 and 1. The activation value yXj = 1 indicates that city X occupies the j th place in the tour. An energy function describing this problem can be set up as follows. 8) where A, B , and C are constants.

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