Conventional and Fuzzy Regression: Theory and Applications aims to
present both conventional and fuzzy regression analyses from
theoretical aspects followed by application examples. The present
book contains eight chapters originating from different scientific
fields: River Engineering, Ecohydraulics, Telecommunications, Urban
Planning, Transportation Planning, Hydrology, Soil Mechanics and
Ecology. The first chapter deals with both crisp (conventional)
linear or nonlinear regression and fuzzy linear or nonlinear
regression. The application example refers to the relationship
between sediment transport rates on the one hand and stream
discharge and rainfall intensity on the other hand. In the examined
case, the data of both categories are insufficient, and
furthermore, the phenomenon is characterized by high complexity and
uncertainties. The second chapter refers to the crisp linear or
nonlinear regression of six heavy metals between different soft
tissues and shells of Telescopium telescopium and its habitat
surface sediments. The third chapter describes the crisp linear,
multiple linear, nonlinear and Gaussian process regressions. The
main application paradigms include the prediction in wireless
systems, the predictive analytics in Internet of Things (IoT) based
systems, and coding theory focused on extrinsic information scaling
in turbo codes. The fourth chapter is confronted with a classic
regression model, named Geographically Weighted Regression (GWR),
which constitutes a spatial statistics method. The application
example of this chapter concerns the housing value, i.e., a spatial
phenomenon that is expressed as a function of housing
characteristics. The fifth chapter regards fuzzy linear regression
based on symmetric triangular fuzzy numbers. The main application
of this regression consists of the analysis and forecast of rail
passenger demand between two nearby cities. The dependent variable
concerns the rail passengers and the independent variables are the
Gross Domestic Product (GDP) per capita, the cost of transport by
rail and the road transport fuel prices. The sixth chapter treats
fuzzy linear regression based on trapezoidal membership functions.
In concrete terms, three possible models with trapezoidal fuzzy
parameters are described. The main application of this chapter
concerns the dependence of rainfall records between neighboring
rainfall stations for a small sample of data. The seventh chapter
refers to the multivariable crisp and fuzzy linear regression. In
the application paradigm, the dependent variable is the strength of
fiber reinforced soils, while the independent variables are
pertinent to soil, fiber and laboratory tests. The eighth chapter
deals with the fuzzy linear regression, with crisp input data and
fuzzy output data. In the application example, a relation between
the levels of chlorophyll-a in an artificial lake and water
temperature, nitrate, total phosphorus and Secchi depth is
established. All the above chapters offer a proper foundation of
either widely used or new techniques upon regression. Among the new
techniques, several innovated fuzzy regression based methodologies
are developed for real problems, and useful conclusions are drawn.
General
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