6,499 research outputs found
Learning of Type-2 Fuzzy Logic Systems using Simulated Annealing.
This thesis reports the work of using simulated annealing to design more efficient fuzzy logic systems to model problems with associated uncertainties. Simulated annealing is
used within this work as a method for learning the best configurations of type-1 and
type-2 fuzzy logic systems to maximise their modelling ability. Therefore, it presents
the combination of simulated annealing with three models, type-1 fuzzy logic systems,
interval type-2 fuzzy logic systems and general type-2 fuzzy logic systems to model
four bench-mark problems including real-world problems. These problems are: noise-free
Mackey-Glass time series forecasting, noisy Mackey-Glass time series forecasting
and two real world problems which are: the estimation of the low voltage electrical
line length in rural towns and the estimation of the medium voltage electrical line
maintenance cost. The type-1 and type-2 fuzzy logic systems models are compared in
their abilities to model uncertainties associated with these problems. Also, issues related
to this combination between simulated annealing and fuzzy logic systems including
type-2 fuzzy logic systems are discussed.
The thesis contributes to knowledge by presenting novel contributions. The first is
a novel approach to design interval type-2 fuzzy logic systems using the simulated
annealing algorithm. Another novelty is related to the first automatic design of general
type-2 fuzzy logic system using the vertical slice representation and a novel method
to overcome some parametrisation difficulties when learning general type-2 fuzzy logic
systems. The work shows that interval type-2 fuzzy logic systems added more abilities
to modelling information and handling uncertainties than type-1 fuzzy logic systems but
with a cost of more computations and time. For general type-2 fuzzy logic systems, the
clear conclusion that learning the third dimension can add more abilities to modelling
is an important advance in type-2 fuzzy logic systems research and should open the
doors for more promising research and practical works on using general type-2 fuzzy
logic systems to modelling applications despite the more computations associated with
it
A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems
This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).Peer ReviewedPostprint (published version
Learning of interval and general type-2 fuzzy logic systems using simulated annealing: theory and practice
This paper reports the use of simulated annealing to design more efficient fuzzy logic systems to model problems with associated uncertainties. Simulated annealing is used within this work as a method for learning the best configurations of interval and gen- eral type-2 fuzzy logic systems to maximize their modeling ability. The combination of simulated annealing with these models is presented in the modeling of four bench- mark problems including real-world problems. The type-2 fuzzy logic system models are compared in their ability to model uncertainties associated with these problems. Issues related to this combination between simulated annealing and fuzzy logic sys- tems, including type-2 fuzzy logic systems, are discussed. The results demonstrate that learning the third dimension in type-2 fuzzy sets with a deterministic defuzzifier can add more capability to modeling than interval type-2 fuzzy logic systems. This finding can be seen as an important advance in type-2 fuzzy logic systems research and should increase the level of interest in the modeling applications of general type-2 fuzzy logic systems, despite their greater computational load
Laplacian Mixture Modeling for Network Analysis and Unsupervised Learning on Graphs
Laplacian mixture models identify overlapping regions of influence in
unlabeled graph and network data in a scalable and computationally efficient
way, yielding useful low-dimensional representations. By combining Laplacian
eigenspace and finite mixture modeling methods, they provide probabilistic or
fuzzy dimensionality reductions or domain decompositions for a variety of input
data types, including mixture distributions, feature vectors, and graphs or
networks. Provable optimal recovery using the algorithm is analytically shown
for a nontrivial class of cluster graphs. Heuristic approximations for scalable
high-performance implementations are described and empirically tested.
Connections to PageRank and community detection in network analysis demonstrate
the wide applicability of this approach. The origins of fuzzy spectral methods,
beginning with generalized heat or diffusion equations in physics, are reviewed
and summarized. Comparisons to other dimensionality reduction and clustering
methods for challenging unsupervised machine learning problems are also
discussed.Comment: 13 figures, 35 reference
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