| Summary: | In many complex real-world engineering problems, it is well known that the optimal number of rules is unknown. Hence, the fuzzy systems need to acquire knowledge by continuous self-organizing its rules to adapt to changing patterns in evolving data streams. Much-efforts have been devoted by several scientists to develop self-organizing fuzzy systems. Yet, the majority of conventional techniques are mainly concerned with independent issue on structural complexity, prediction accuracy, and structural complexity without taking into account a balance among them. Highly accurate methods usually suffer high computational burden thereby limiting scalability in practical applications. In this dissertation, two novel self-organizing fuzzy systems, which attempts to strike a balance among structural complexity, prediction accuracy, and training speed, are proposed.
|
|---|