Question

Implement the following perceptron learning algorithm using C++. Test your algorithm with AND and OR operations. With calculate the rate value. Learning Algorithm steps: 1. Initialise weights and threshold. Set $w_i(t)$, ($1 \leq i \leq m$) to be the weight $i$ at time $t$, and $\phi$ to be the threshold value in the output node. Set $w_i$ to small random values, thus initialising the weights and threshold. 2. Present input and desired output Present input $x_0$ and $x_1, x_2,..., x_m$ and desired output $d(t)$ 3. Calculate the actual output $y(t) = f[w_0(t)x_0(t) + w_1(t)x_1(t) + w_2(t)x_2(t) + .... + w_m(t)x_m(t)]$ 4. Adapts weights $w_i(t+1) = w_i(t) + \mu[d(t) - y(t)]x_i(t)$, for. Steps 3 and 4 are repeated until the iteration error is less than a user-specified error threshold or a predetermined number of iterations have been completed. $\mu$ is the learning rate $d(t)$ is the desired output $y(t)$ is the actual output Error = d(t)-y(t) $\mu$ is between 0 and 1:

          Implement the following perceptron learning algorithm using C++. Test your algorithm with AND
and OR operations. With calculate the rate value.
Learning Algorithm steps:
1. Initialise weights and threshold.
Set $w_i(t)$, ($1 \leq i \leq m$) to be the weight $i$ at time $t$, and $\phi$ to be the threshold value in the output node.
Set $w_i$ to small random values, thus initialising the weights and threshold.
2. Present input and desired output
Present input $x_0$ and $x_1, x_2,..., x_m$ and desired output $d(t)$
3. Calculate the actual output
$y(t) = f[w_0(t)x_0(t) + w_1(t)x_1(t) + w_2(t)x_2(t) + .... + w_m(t)x_m(t)]$
4. Adapts weights
$w_i(t+1) = w_i(t) + \mu[d(t) - y(t)]x_i(t)$, for.
Steps 3 and 4 are repeated until the iteration error is less than a user-specified error threshold or a
predetermined number of iterations have been completed.
$\mu$ is the learning rate
$d(t)$ is the desired output
$y(t)$ is the actual output
Error = d(t)-y(t)
$\mu$ is between 0 and 1:

Implement the following perceptron learning algorithm using C++. Test your algorithm with AND
and OR operations. With calculate the rate value.
Learning Algorithm steps:
1. Initialise weights and threshold.
Set wi(t), (1 ≤ i ≤ m) to be the weight i at time t, and ϕ to be the threshold value in the output node.
Set wi to small random values, thus initialising the weights and threshold.
2. Present input and desired output
Present input x0 and x1, x2,..., xm and desired output d(t)
3. Calculate the actual output
y(t) = f[w0(t)x0(t) + w1(t)x1(t) + w2(t)x2(t) + .... + wm(t)xm(t)]
4. Adapts weights
wi(t+1) = wi(t) + μ[d(t) - y(t)]xi(t), for.
Steps 3 and 4 are repeated until the iteration error is less than a user-specified error threshold or a
predetermined number of iterations have been completed.
μ is the learning rate
d(t) is the desired output
y(t) is the actual output
Error = d(t)-y(t)
μ is between 0 and 1:

Added by Samuel R.

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