The research community has considered in the past the application of Artificial Intelligence (AI) techniques to control and operate networks. A notable example is the Knowledge Plane proposed by D.Clark et al. However, such techniques have not been extensively prototyped or deployed in the field yet. In this paper, we explore the reasons for the lack of adoption and posit that the rise of two recent paradigms: Software-Defined Networking (SDN) and Network Analytics (NA), will facilitate the adoption of AI techniques in the context of network operation and control. We describe a new paradigm that accommodates and exploits SDN, NA and AI, and provide use cases that illustrate its applicability and benefits. We also present simple experimental results that support its feasibility. We refer to this new paradigm as Knowledge-Defined Networking (KDN).Comment: 8 pages, 22 references, 6 figures and 1 tabl

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Neural network techniques are investigated applied to the modelling and control of non-linear processes. The development of process models and predictive controllers using two feed-forward neural networks -the multi-layer perceptron and the radial basis function network -is described. The capabilities of these neural networks are demonstrated in two practical applications to modelling and control of a liquid level rig and a multi-variable in-line pH process. On-line results illustrate the performance of neural network predictive control schemes for set-point tracking over a wide non-linear operating range and regulation in the presence of significant disturbances.