By Ben Krose, Patrick van der Smagt

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

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**Additional info for An Introduction to Neural Networks (8th Edition)**

**Example text**

8) are zero if and only if there is a maximum of one active neuron in each row and column, respectively. The last term is zero if and only if there are exactly n active neurons. 9) is added to the energy, where dXY is the distance between cities X and Y and D is a constant. For convenience, the subscripts are de ned modulo n. 10) ;C global inhibition ;DdXY ( k j+1 + k j;1) data term where jk = 1 if j = k and 0 otherwise. Finally, each neuron has an external bias input Cn. Discussion Although this application is interesting from a theoretical point of view, the applicability is limited.

2, which can be used for the representation of binary patterns subsequently we touch upon Boltzmann machines, therewith introducing stochasticity in neural computation. 1 The generalised delta-rule in recurrent networks The back-propagation learning rule, introduced in chapter 4, can be easily used for training patterns in recurrent networks. Before we will consider this general case, however, we will rst describe networks where some of the hidden unit activation values are fed back to an extra set of input units (the Elman network), or where output values are fed back into hidden units (the Jordan network).

With increasing number of learning samples the two error rates converge to the same value. This value depends on the representational power of the network: given the optimal weights, how good is the approximation. This error depends on the number of hidden units and the activation function. If the learning error rate does not converge to the test error rate the learning procedure has not found a global minimum. 8: E ect of the learning set size on the error rate. The average error rate and the average test error rate as a function of the number of learning samples.

### An Introduction to Neural Networks (8th Edition) by Ben Krose, Patrick van der Smagt

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