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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Extra resources for An Introduction to Neural Networks (8th Edition)
4: Training a feed-forward network to control an object. The solid line depicts the desired trajectory x d the dashed line the realised trajectory. The third line is the error. the results are actually better with the ordinary feed-forward network, which has the same complexity as the Elman network. 3 Back-propagation in fully recurrent networks More complex schemes than the above are possible. For instance, independently of each other Pineda (Pineda, 1987) and Almeida (Almeida, 1987) discovered that error back-propagation is in fact a special case of a more general gradient learning method which can be used for training attractor networks.
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. 8. Note that the learning error increases with an increasing learning set size, and the test error decreases with increasing learning set size.
Consequently, weight vectors are rotated towards those areas where many inputs appear: the clusters in the input. 2. w1 w1x w2 a. x w2 b. 3: Determining the winner in a competitive learning network. a. Three normalised vectors. b. , but with di erent lengths. , vectors x and w1 are nearest to each other, and their dot product xT w1 = jxjjw1 j cos is larger than the dot product of x and w2 . , however, the pattern and weight vectors are not normalised, and in this case w2 should be considered the `winner' when x is applied.