Neural networks thesis
The training algorithm stops when a specified condition is satisfied. Some stopping criteria commonly used are:
- The parameters increment norm is less than a minimum value.
- The performance improvement in one epoch is less than a set value.
- Performance has been minimized to a goal value.
- The norm of the performance function gradient falls below a goal.
- A maximum number of epochs is reached.
- A maximum amount of computing time has been exceeded.
Refer to the figure that illustrates the backpropagation multilayer network with layers. represents the number of neurons in th layer. Here, the network is presented the th pattern of training sample set with -dimensional input and -dimensional known output response . The actual response to the input pattern by the network is represented as . Let be the output from the th neuron in layer for th pattern; be the connection weight from th neuron in layer to th neuron in layer ; and be the error value associated with the th neuron in layer .
Neural networks thesis
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