The development of artificial intelligence and machine learning has its roots in the “simple models” of the 1950s and 60s.
Among these, the ADALINE (Adaptive Linear Neuron) is a groundbreaking model that underpins today’s deep learning.
1. What is ADALINE?
1.1 Basic Structure and Operating Principle
ADALINE is a simple “single-layer neural model” with a straight connection from input to output.
It first adjusts multiple input signals with appropriate weight values, sums the results, and passes them to the output layer.
Unlike conventional perceptrons, it doesn’t include a non-linear switch internally. Instead, it calculates the output value itself solely through multiplication and addition of inputs and weights.
The difference between the resulting predicted value and the correct label is evaluated using a metric called “mean squared error”, and learning progresses to minimise this error.
1.2 Learning Algorithm
ADALINE learning is performed using a method called the “Least Mean Squares rule”.
Specifically, the model constantly monitors the error between its predicted value and the actual correct answer. The larger the error, the more significantly the weights corresponding to the input and the bias for output adjustment are changed.
Through a parameter called the learning rate, which determines the strength of the adjustment, the error is gradually reduced while searching for an appropriate combination of weights.
By repeating this update process, a model that can ultimately suppress the error as much as possible is obtained.
1.3 Historical Significance
ADALINE was proposed in the early 1960s by Bernard Widrow and Ted Hoff.
At the time, it was highly regarded as an “adaptive filter” for removing real-time noise from communication lines and was widely used in research labs and the military.
This success provided important insights that later led to learning algorithms for neural networks in general, and forms the foundation for today’s deep learning.
2. Where is ADALINE Used Today?
While there are fewer cases of ADALINE being used directly, its underlying technology continues to be active in the following fields and tasks:
- Adaptive Noise Cancelling
Estimates and cancels stationary noise mixed with microphones and sensors.
- Channel Equalisation (Communication Engineering)
Corrects distortions occurring in wireless and fibre optic communications in real-time.
- Stock Price Prediction / Time Series Analysis
Applied as a simple linear regression model to short-term trends and error minimising filters.
- Lightweight Models for IoT/Embedded Devices
Anomaly detection and simple classification tasks under resource constraints.
3. Benefits of Learning ADALINE
- Foundation of Gradient Descent
Intuitively experience the principles of algorithms that minimise error functions.
- Boundary Between Continuous Value Prediction vs. Classification
Clearly understand why mean squared error is used and the differences between linear regression and classification.
- Visualise the Effect of Parameter Adjustment
Simply track the behaviour of learning rate, initial values, and mini-batch learning.
- Understand the Origin of Backpropagation
- Necessity of Activation Functions and Regularisation
Optimal step to learn problems that cannot be solved with linear models alone.
- Model Selection Judgement
Ability to distinguish between tasks where a simple linear model is sufficient and those where a more complex model is needed.
- Real-time Filtering
Knowing techniques applicable to factories, medical facilities, and communication sites.
- Implementation Exercises
The experience of building a model that works with a few lines of Python/NumPy code builds confidence.
Summary
ADALINE is not an outdated technology, but a textbook for learning the foundations of machine learning.
Let’s start by getting hands-on, deepening our understanding from both theoretical and practical perspectives, and expanding the scope of application.
If you want to learn ADALINE, we recommend this book (access here).
コメント
コメントを投稿