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Questions and Answers
Adaline consists of multiple output units with bipolar values (+1, -1).
Adaline consists of multiple output units with bipolar values (+1, -1).
False
The learning rule in Adaline aims to maximize the mean square error between activation and target values.
The learning rule in Adaline aims to maximize the mean square error between activation and target values.
False
In Adaline, the weights between the input unit and output unit are fixed and not adjustable.
In Adaline, the weights between the input unit and output unit are fixed and not adjustable.
False
Adaline network applies the activation function first and then calculates the net input.
Adaline network applies the activation function first and then calculates the net input.
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Adaline has a bias of activation function 0 instead of 1.
Adaline has a bias of activation function 0 instead of 1.
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The delta learning rule is used in Adaline to update weights based on the comparison of actual output with calculated output.
The delta learning rule is used in Adaline to update weights based on the comparison of actual output with calculated output.
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