Future networks are expected to provide improved support for several different kinds of applications and services. All these services will have diverse characteristics and requirements to be satisfied. A potential technology to upgrade efficiently and effectively current generation networks is virtualisation via network ’softwarization’. This approach requires the combination of software-defined networking and network function virtualisation. Nevertheless, such a new complex network structure will raise further issues and challenges to be solved both reactively and proactively, without human intervention. In order to achieve that, academia and industry have identified the solution in the implementation and deployment of machine learning. Hence, very likely, 5G (and especially beyond 5G) networks will be cognitive virtualised networks. In that context, this article proposes a cognitive software-defined networking architecture based on Fuzzy Cognitive Maps. First, specific design modifications of Fuzzy Cognitive Maps are proposed to overcome some well-known issues of this learning paradigm. Second, the efficient integration with a software-defined networking architecture is presented and analysed. Finally, the emulation of a sample network scenario via Mininet is provided to validate the effectiveness and the potential of the new cognitive system and its capability to act and to adapt independently of human intervention.