Heuristic design of fuzzy inference systems: A review of three decades of research
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 …
inference systems (FIS) using five well known computational frameworks: genetic-fuzzy …
Autonomous learning for fuzzy systems: a review
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 …
mathematical tool widely used for modelling nonlinear problems with uncertainties. Fuzzy …
Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A survey
Major assumptions in computational intelligence and machine learning consist of the
availability of a historical dataset for model development, and that the resulting model will, to …
availability of a historical dataset for model development, and that the resulting model will, to …
Adaptive fuzzy control of a class of nonlinear systems by fuzzy approximation approach
Controlling nonstrict-feedback nonlinear systems is a challenging problem in control theory.
In this paper, we consider adaptive fuzzy control for a class of nonlinear systems with …
In this paper, we consider adaptive fuzzy control for a class of nonlinear systems with …
Observer-based adaptive fuzzy backstep** output feedback control of uncertain MIMO pure-feedback nonlinear systems
S Tong, Y Li, P Shi - IEEE Transactions on Fuzzy Systems, 2012 - ieeexplore.ieee.org
This paper is concerned with the problem of adaptive fuzzy tracking control for a class of
uncertain multiple-input-multiple-output (MIMO) pure-feedback nonlinear systems with …
uncertain multiple-input-multiple-output (MIMO) pure-feedback nonlinear systems with …
PANFIS: A novel incremental learning machine
Most of the dynamics in real-world systems are compiled by shifts and drifts, which are
uneasy to be overcome by omnipresent neuro-fuzzy systems. Nonetheless, learning in …
uneasy to be overcome by omnipresent neuro-fuzzy systems. Nonetheless, learning in …
Fuzzy regression transfer learning in Takagi–Sugeno fuzzy models
Data science is a research field concerned with processes and systems that extract
knowledge from massive amounts of data. In some situations, however, data shortage …
knowledge from massive amounts of data. In some situations, however, data shortage …
A combined backstep** and stochastic small-gain approach to robust adaptive fuzzy output feedback control
S Tong, T Wang, Y Li, B Chen - IEEE Transactions on Fuzzy …, 2012 - ieeexplore.ieee.org
In this paper, an adaptive fuzzy output feedback control approach is investigated for a class
of stochastic nonlinear strict-feedback systems without the requirement of states …
of stochastic nonlinear strict-feedback systems without the requirement of states …
Generalized smart evolving fuzzy systems
In this paper, we propose a new methodology for learning evolving fuzzy systems (EFS) from
data streams in terms of on-line regression/system identification problems. It comes with …
data streams in terms of on-line regression/system identification problems. It comes with …
Ensemble of evolving data clouds and fuzzy models for weather time series prediction
This paper describes a variation of data cloud-based intelligent method known as typicality-
and-eccentricity-based method for data analysis (TEDA). The objective is to develop data …
and-eccentricity-based method for data analysis (TEDA). The objective is to develop data …