https://en.wikipedia.org/wiki/ADALINE

I was confused about this because, for example, the XOR problem is not linearly separable, and a simple Perceptron obviously cannot solve it, so we would need a network like the multilayer perceptron. Furthermore, some sources on the internet say that Adaline's learning algorithm, the LMS (Least Mean Squares) algorithm, converges to the minimum error assumption, regardless of whether the training data is linearly separable or not. However, this refers to minimizing MSE, not correctly classifying all training samples. But this leaves me confused. What does that mean?

An Adaline neuron can solve problems that are not linearly separable ?

If so, what is the difference between Adaline's ability to solve problems that are not linearly separable and other neural networks such as Madaline and multilayer perceptron?