Learning rates

Typical variation of the training rate with epoch number. 

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The Fuzzy-ARTMAP network reaehes the best learning rate of the training data set. This is recognised perfectly with 100% correctness. The exact results are presented in the next table ... 

Calculate the output jy and hence the new values for the weights and biases. Assume a learning rate of 0.5. 

The seleeted network had a 3-6-6-6-3 strueture, i.e. input and output layers eomprising 3 neurons in eaeh, separated by three hidden layers of 6 neurons. During learning, 4 million epoehs were trained. The learning rate and momentum were initially set at 0.3 and 0.8, but were redueed in three steps to final values of 0.05 and 0.4 respeetively. 

In Example 10.9(b), if the target values for the outputs are d 2 = 0 and 1 22 = 1, ealeulate new values for the weights and biases using the baek-propagation algorithm. Assume a learning rate of 0.5 with no momentum term. 

Fig. 44.15. (a) Performance curve of a MLF network for a training set. (b) Performance curve of a MLF network with too high a learning rate. 

Usually the learning rate, T), is chosen between 0 and 1. In this step the network incorporates the new information present in the input object by moving the centroid of the class a little towards the new input pattern x,. This step is intended to make the network flexible enough when clusters are changing in time. 

Set the learning rate, T, to a small positive value 0 < r < 1. Fill the weights vector at each node with random numbers. 

If the learning rate was 0.05, the node weights after updating would be... 

It is a common feature of most AI methods that flexibility exists in the way that we can run the algorithm. In the SOM, we can choose the shape and dimensionality of the lattice, the number of nodes, the initial learning rate and how quickly the rate diminishes with cycle number, the size of the initial neighborhood and how it too varies with the number of cycles, the type of function to be used to determine how the updating of weights varies with distance from the winning node, and the stopping criterion. 

Figure 18.6. Net employment effects for the hydrogen high penetration, and medium penetration scenarios with optimistic learning rates for hydrogen passenger cars, as well as hydrogen low penetration scenarios with moderate learning rates for hydrogen passenger cars for the years 2020 and 2030. The overall net employment effects for the ten HyWays countries in three import and export scenarios are shown.

The index t in the above equation corresponds to the training instance, X is the vector of input variables, Y is the vedor of target variables, q is a learning rate,... 

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