Which characteristic is NOT true about a multilayer perceptron in neural networks?

A multilayer perceptron (MLP) is a type of artificial neural network that consists of an input layer, one or more hidden layers, and an output layer. One of its key characteristics is that it can indeed have any number of hidden layers. This flexibility allows MLPs to model complex relationships in data and represent intricate patterns.

While some simpler neural networks may only use a single hidden layer, the architecture of MLPs is such that they can be designed with multiple hidden layers, which enables them to learn more complex functions. The ability to increase the number of hidden layers contributes to the power of MLPs in various applications, including classification and regression tasks.

In contrast, while it is true that MLPs can also accommodate any number of inputs and utilize nonlinear activation functions, stating that they utilize only a single hidden layer is incorrect because this limits the architecture unnecessarily. Multilayer perceptrons are specifically designed to function effectively with multiple layers to enhance their learning capability.