Draw Delta Learning Rule (LMS-Widrow Hoff) model and explain it with a training process flowchart.

Team Answered question May 27, 2024

The Delta Learning Rule, also known as the LMS (Least Mean Squares) algorithm or the Widrow-Hoff rule, is a method for adjusting the weights of a linear neuron in a neural network during training. Below is a graphical representation of the Delta Learning Rule model:

Components of the Delta Learning Rule Model:

**Input Features**:- Represents the input data features (e.g., pixel values in an image, numerical attributes in a dataset).

**Weighted Summation**:- Each input feature is multiplied by its corresponding weight.
- The weighted inputs are summed up to produce a weighted sum.

**Activation Function**:- The weighted sum is passed through an activation function.
- Common activation functions include the step function, sigmoid function, or rectified linear unit (ReLU).

**Output**:- Represents the output of the neuron after applying the activation function.
- The output can be binary (0 or 1) for binary classification tasks or continuous for regression tasks.

Operation of the Delta Learning Rule:

**Initialization**:- Initialize the weights of the neuron with random or predefined values.

**Forward Propagation**:- Pass the input features through the neuron.
- Calculate the weighted sum of inputs.
- Apply the activation function to obtain the output.

**Error Calculation**:- Compare the predicted output to the true output (target).
- Calculate the error as the difference between the predicted output and the target.

**Weight Adjustment (Learning)**:- Update the weights based on the error using the Delta Rule formula: Δwij=η⋅(di−yi)⋅xij Where:
- Δwij is the change in weight.
- η is the learning rate.
- di is the target output.
- yi is the predicted output.
- xij is the input feature.

- Adjust the weights to minimize the error and improve the accuracy of the model.

- Update the weights based on the error using the Delta Rule formula: Δwij=η⋅(di−yi)⋅xij Where:
**Repeat**:- Repeat steps 2-4 for each training example in the dataset.
- Iterate through the entire dataset multiple times (epochs) until the model converges or until a stopping criterion is met.

Team Answered question May 27, 2024