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First, a network can be equipped with a feedback mechanism, known as a back-propagation algorithm, that enables it to adjust the connection weights back through the network, training it in response to representative examples.
Adjust the connection weight vector.
(5) Adjust the connection weight vector.
If the output values do not agree with the excepted values, the BP neural network can gradually adjust the connection weights and thresholds between neurons by back propagation of errors between the real output and excepted output.
The result is compared to the corresponding desired values and the error is back-propagated through the network to adjust the connection weights according to the learning rule.
Supervised ANNs calculate an error function measuring the distance between the desired fixed output (target) and their own output, and adjust the connection strengths during the training process to minimize the result of the error function.
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In each set of simulations, we fix the numbers of nodes and edges in a network but adjust the connections to increase the modularity.
During training with the BP algorithm, information about the errors of the network on known data is propagated backwards from the output layer to the input layer and it is used to adjust the connections between the layers and their neurons (the weights and biases of the network) in order to minimize the error and thus improve performance.
Christopher A. Williams, a Clark University geographer who was one of the Op-ed authors and an author of the Nature Geosciences paper on recent western drought, replied (shorthand adjusted): The connection between elevated greenhouse gases and drought in the American West is that greenhouse-induced warming is very likely to increase the frequency and severity of drought conditions in the region.
The final result will be acquired by adjusting the connection weights and thresholds.
The desired output can be achieved by adjusting the connection weights v βα of all pairs of neurons.
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