Self-organizing neuro-fuzzy system for control of unknown plants

A cluster-based self-organizing neuro-fuzzy system (SO-NFS) is proposed for control of unknown plants. The neuro-fuzzy system can learn its knowledge base from input-output training data. A plant model is not required for training, that is, the plant is unknown to the SO-NFS. Using new data types, the vectors and matrices, a construction theory is developed for the organization process and the inference activities of the cluster-based SO-NFS. With the construction theory, a compact equation for describing the relation between the input base variables and inference results is established. This equation not only gives the inference relation between inputs and outputs but also specifies the linguistic meanings in the process. New pseudo-error learning control is proposed for closed-loop control applications. Using a cluster-based algorithm, the neuro-fuzzy system in its genesis can be generated by the stimulation of input/output training data to have its initial control policy (IF-THEN rules) for application. With the well-known random optimization method, the generated neuro-fuzzy system can learn its data base for specific applications. The proposed approach can be applied on control of unknown plants, and can levitate the curse of dimensionality in traditional fuzzy systems. Two examples are demonstrated.