As one of the three pillars in computational intelligence, fuzzy systems are a powerful
mathematical tool widely used for modelling nonlinear problems with uncertainties. Fuzzy …

This paper provides an in-depth review of the optimal design of type-1 and type-2 fuzzy
inference systems (FIS) using five well known computational frameworks: genetic-fuzzy …

Fundamental and advanced developments in neuro-fuzzy synergisms for modeling and
control are reviewed. The essential part of neuro-fuzzy synergisms comes from a common …

Fuzzy logic control was originally introduced and developed as a model free control design
approach. However, it unfortunately suffers from criticism of lacking of systematic stability …

A self-constructing neural fuzzy inference network (SONFIN) with online learning ability is
proposed in this paper. The SONFIN is inherently a modified Takagi-Sugeno-Kang (TSK) …

In today's industrial systems, economic markets, life and health-care sciences fuzzy systems
play an important role in many application scenarios such as system identification, fault …

Many traditional reinforcement-learning algorithms have been designed for problems with
small finite state and action spaces. Learning in such discrete problems can been difficult …

The automatic diagnosis of breast cancer is an important, real-world medical problem. In this
paper we focus on the Wisconsin breast cancer diagnosis (WBCD) problem, combining two …

The stock market, which has been investigated by various researchers, is a rather
complicated environment. Most research only concerned the technical indexes (quantitative …

In this paper, a TSK-type recurrent fuzzy network (TRFN) structure is proposed. The proposal
calls for the design of TRFN by either neural network or genetic algorithms depending on the …