A single-layer neural network is a type of feedforward network that has a single layer of artificial neurons, each connected to the input layer with a set of weights. The output of the single layer network is a weighted sum of the inputs passed through a transfer function. The most commonly used transfer function is the sigmoid function, which maps any real-valued input to a value between 0 and 1.
A single-layer neural network can be trained using the supervised learning technique called the perceptron learning rule. The objective of the perceptron algorithm is to find the set of weights that produces the correct output for a given input. The perceptron learning rule adjusts the weights of the connections between the input and output layers based on the error between the predicted output and the actual output.
Single-layer neural networks are limited in their ability to model complex functions and relationships because they can only produce linear decision boundaries. They are often used in simple classification problems where the data is linearly separable.
