Approximation of dynamic systems using recurrent neuro-fuzzy techniques

Fuzzy systems, neural networks and its combination in neuro-fuzzy systems are already well established in data analysis and system control. Especially, neuro-fuzzy systems are well suited for the development of interactive data analysis tools, which enable the creation of rule-based knowledge from data and the introduction of a-priori knowledge into the process of data analysis. In this article an architecture is presented that was designed to learn and optimize a hierarchical fuzzy rule base with feedback connections using a genetic algorithm for rule base structure learning and a gradient descent method to optimize the fuzzy sets of the learned rule base. Since this architecture is able to store information of prior system states, the model is especially suited for the analysis of dynamic systems.

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