- Adaline neural networks are single-layer neural networks that use an adaptive linear neuron as the processing unit. They can be trained using the Delta rule or least mean square rule to minimize errors. - The architecture of an Adaline network consists of an input layer, weighted sum function, activation function, bias node, error function, and gradient descent training algorithm. - Adaline networks were applied to problems like adaptive filtering, system modeling, statistical prediction, noise cancellation, and pattern recognition. - A Python implementation of Adaline for breast cancer classification is presented, including methods for model training, prediction, and evaluating accuracy.

1. Adaline Neural Networks
Roll No: 711 to 720

2. Introduction
● In supervised learning, output is compared with the target output.
● On the basis of the error signal, the weights are adjusted until the actual
output is matched with the desired output.
● Adaptive Linear Neuron is a single-layer operational unit, which uses
bipolar activation function.
● Adaline neurons can be trained using the Delta or Least Mean Square
rules.
Soft Computing - Adaline Neural Networks Savri - 713

3. Adaline versus Perceptron
The Adaline learns all the time and the perceptron only after
errors, the Adaline will find a solution faster than the perceptron
for the same problem.
Soft Computing - Adaline Neural Networks Savri - 713

4. ADALINE NEURAL NETWORK ARCHITECTURE
Soft Computing - Adaline Neural Networks Amar - 718

5. ADALINE NEURAL NETWORK ARCHITECTURE
COMPONENTS
Soft Computing - Adaline Neural Networks
● Input layer:
○ receives the input data and passes it to the next layer.
○ Each input feature is represented by a separate node.
● Weighted Sum Function:
○ Calculates the dot product of the input features and their corresponding
weights.
○ Represented mathematically as ∑(x_i * w_i)
where,
x_i is the i-th input feature,
w_i is the i-th weight,
∑ is the summation function.
Amar - 718

6. Soft Computing - Adaline Neural Networks Niraj - 712
● Activation Function:
○ Takes the output of the weighted sum function and
produces the output.
○ Uses a linear activation function.
● Bias Node:
○ An additional input node that always has a value of 1.
○ It is multiplied by a weight(bias weight) and added to the
weighted sum of input features.
○ Weighted sum allows to adjust the output vertically,
which is useful for classification tasks.

7. Soft Computing - Adaline Neural Networks
● Error Function :
○ Used to evaluate how well the network is performing.
○ The goal of training the network is to minimize the error
function.
● Gradient Descent:
○ It is an optimization algorithm used to train the Adaline
network.
○ Adjusts the weights and bias weight based on the derivative
of the error function w.r.t the weights.
Niraj - 712

8. Delta Rule (A learning rule)
● The learning rule is a mathematical logic that encourages a neural network to
gain from the existing condition and uplift its performance.
● It is an iterative procedure.
● The Delta rule is derived from the Gradient – Descent Method, which can be
generalized to more than one layer.
● Gradient descent approach continues forever converging asymptotically to the
solution, as compared to the perceptron learning rule which stops after a finite
number of learning steps.
● The major aim is to minimize the error over all training patterns.
Soft Computing - Adaline Neural Networks Nikhil - 715

9. Delta Rule
Nikhil - 715
Soft Computing - Adaline Neural Networks

10. Training Algorithm
Step 0: Weights and bias are set to random values but not
zero.Set the learning rate parameter alpha.
Step1: Perform steps 2-6 when stopping condition is false.
Step2: Perform steps 3-5 for each bipolar training pair
Steps3: Set activation for input unit i=! to n.
xi=si
Soft Computing - Adaline Neural Networks Gautham - 716

11. Steps4: Calculate the net input to output unit
Yin=b+∑xiwi
Steps5: Update the weights and bias for i=1 to n:
Wi(new)=wi(old)+ α(t-yin)xi
b(new)=b(old)+ α(t-yin)
Step6: Stopping criteria: The training can stop when the error is small enough or
does not change much with further training, or when a maximum number of
epochs has been reached.
Soft Computing - Adaline Neural Networks Gautham - 716

12. ● Initialize the weights
● Input data
● Activation function
● Prediction
● Error calculation
● Weight update
● Repeat steps 2-6 for multiple epochs or until the error is
below a certain threshold.
● Evaluate performance
● Deploy
Soft Computing - Adaline Neural Networks Jayesh - 720

13. Learning rate
● When we train neural networks we usually use weight updation rule.
● At each iteration we use back-propagation to calculate the derivative of the loss
function with respect to each weight and subtract it from that weight.
● Learning rate determines how quickly or how slowly you want to update your
weight(parameter) values.
● Learning rate should be high enough so that it won’t take ages to converge, and it
should be low enough so that it finds the local minima.
Soft Computing - Adaline Neural Networks Shivam - 717

14. Testing Algorithm
● Used to classify input patterns.
● Step function is used to test the performance of the
network
Soft Computing - Adaline Neural Networks Shivam - 717
Implement AND NOT function using Adaline Network

15. ● Step 1 : Initialize the weights (Obtained from training algorithm)
● Step 2 : Perform Steps 3-5 for each bipolar input vector x.
● Step 3 : Set the activations of the input units to x.
● Step 4 : Calculate the net input to the output unit.
● Step 5 : Apply the activation function over the net input calculated:
Testing Procedure for Adaline network
Soft Computing - Adaline Neural Networks Shivam - 717
Bipolar Activation Function

16. Application :
● Adaptive filters and adaptive signal processing:
○ The adaptive filter of the given figure has an input signal and produces an
output signal. The desired response is supplied during training.
○ The filtered output is a linear combination of the current and past input signal
samples.
The main application of the ADALINE was in adaptive filtering and adaptive
signal processing.
Soft Computing - Adaline Neural Networks Sakshi Yadav - 711

17. ● Adaptive signal processing examples
○ System Modelling
○ Statistical prediction
○ Noise Cancelling
○ Inverse Modelling
○ Adaptive echo cancellation
○ Channel equalization
● Adaptive pattern recognition
○ It would be useful to devise a neural net configuration that could be trained to
classify an important set of training patterns as required.
○ The adaptive threshold element can be used for pattern recognition and as a
trainable logic device.
Sakshi Yadav - 711
Soft Computing - Adaline Neural Networks

18. Practical Implementation using Python - Overview
● Implementation of Adaline Neural Network to the Breast Cancer dataset
● We will be:
○ Creating fit() and predict() methods for training and testing the Adaline NN model
○ Training the model using label "0" as the negative class and the label "1" as the positive class
○ Then calculate the accuracy of the model
● This will be a binary classification example, where we are trying to distinguish between malignant
and benign tumors
Soft Computing - Adaline Neural Networks Sameer Bidi - 719

19. Practical Implementation using Python
Soft Computing - Adaline Neural Networks Sameer Bidi - 719
Adaline Class - fit() method
Adaline Class - predict() method

20. Practical Implementation using Python
Soft Computing - Adaline Neural Networks Nitin Upadhyay - 714
Load the dataset and split into training and testing sets
Import necessary libraries

21. Practical Implementation using Python
Soft Computing - Adaline Neural Networks Nitin Upadhyay - 714
Testing the model - Accuracy: 0.9649
Scaling the data Training the model

22. That’s all folks!