Industrial IOT is favoring RBF-type classification over Deep Learning for several essential reasons. First is the convenience to run tasks locally, without dependency to a remote server. Indeed an RBF classifier is a lifelong learner which can be taught incrementally and in real-time. Adapting to changes of production, new materials and other environmental conditions can be done immediately. This eliminates the need to send the new annotated examples to the cloud and wait for the generation of a new inference engine. Secondly, an RBF classifier does not report probabilities but rather prefer to enumerate multiple categories when the neurons do not reach a consensus. This behavior is much preferable when the application carries a cost of the mistake, whether it is for processing, quality control or predictive maintenance.

You can read more on this topic in an article by written Philippe Lambinet and published in the IIOT World magazine. Mr. Lambinet is the president of Cogito Instruments  and has chosen to integrate NeuroMem RBF neural network chips in Cogito’s CompactRIO® cartridge compatible with the National Instruments® platform.