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The answer is no. What you want to do is to predict randomness. The perceptron network takes randomboolean(true/false) and it outputs outputboolean(not random!!). The random generation of targetboolean is independent from the generation of outputboolean.

Perceptrons generally learn functions. If you have $f(A)=B$ and $f(A)=C$ and $B\neq C$, then $f$ is not a function.

EDIT: To predict temporal behavior you should add some time dependent variable in the input of the network.

The answer is no. What you want to do is to predict randomness. The perceptron network takes randomboolean(true/false) and it outputs outputboolean(not random!!). The random generation of targetboolean is independent from the generation of outputboolean.

Perceptrons generally learn functions. If you have $f(A)=B$ and $f(A)=C$, then $f$ is not a function.

EDIT: To predict temporal behavior you should add some time dependent variable in the input of the network.

The answer is no. What you want to do is to predict randomness. The perceptron network takes randomboolean(true/false) and it outputs outputboolean(not random!!). The random generation of targetboolean is independent from the generation of outputboolean.

Perceptrons generally learn functions. If you have $f(A)=B$ and $f(A)=C$ and $B\neq C$, then $f$ is not a function.

EDIT: To predict temporal behavior you should add some time dependent variable in the input of the network.

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source | link

The answer is no. What you want to do is to predict randomness. The perceptron network takes randomboolean(true/false) and it outputs outputboolean(not random!!). The random generation of targetboolean is independent from the generation of outputboolean.

Perceptrons generally learn functions. If you have $f(A)=B$ and $f(A)=C$, then $f$ is not a function.

EDIT: To predict temporal behavior you should add some time dependent variable in the input of the network.