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A hybrid digital neuro-fuzzy board for control of complex systems

Artificial neural networks (NNs) and fuzzy systems are highly parallel structures that consist of a large number of fully interconnected elementary nonlinear units (called neurons). We present a digital hardware implementation of NNs combined with a host computer (DSP systems, PCs, etc.) resulting in a powerful system used in different applications. The NNs process most of the tasks, while the host performs signal pre-processing and learning algorithms. In some control applications the host also tracks some discrete states of the plant by implementing a finite state automata and/or verifying plant safety boundaries operations. The tight link with the host allows the NN hardware to be very simple since several operations related with the NNs (learning, weights initialization, etc.) can be performed by the host computer. The board can be used to implement intelligent control paradigms mixing neuro-fuzzy algorithms with finite state automata and/or digital control algorithms.