34,219 research outputs found
An immune algorithm based fuzzy predictive modeling mechanism using variable length coding and multi-objective optimization allied to engineering materials processing
In this paper, a systematic multi-objective fuzzy
modeling approach is proposed, which can be regarded
as a three-stage modeling procedure. In the first stage, an
evolutionary based clustering algorithm is developed to
extract an initial fuzzy rule base from the data. Based on
this model, a back-propagation algorithm with momentum
terms is used to refine the initial fuzzy model. The refined
model is then used to seed the initial population of an
immune inspired multi-objective optimization algorithm
in the third stage to obtain a set of fuzzy models with
improved transparency. To tackle the problem of
simultaneously optimizing the structure and parameters, a
variable length coding scheme is adopted to improve the
efficiency of the search. The proposed modeling approach
is applied to a real data set from the steel industry.
Results show that the proposed approach is capable of
eliciting not only accurate but also transparent fuzzy
models
Fuzzy investment decision support for brownfield redevelopment
Tato disertační práce se zaměřuje na problematiku investování a podporu rozhodování pomocí moderních metod. Zejména pokud jde o analýzu, hodnocení a výběr tzv. brownfieldů pro jejich redevelopment (revitalizaci). Cílem této práce je navrhnout univerzální metodu, která usnadní rozhodovací proces. Proces rozhodování je v praxi komplikován též velkým počet relevantních parametrů ovlivňujících konečné rozhodnutí. Navržená metoda je založena na využití fuzzy logiky, modelování, statistické analýzy, shlukové analýzy, teorie grafů a na sofistikovaných metodách sběru a zpracování informací. Nová metoda umožňuje zefektivnit proces analýzy a porovnávání alternativních investic a přesněji zpracovat velký objem informací. Ve výsledku tak bude zmenšen počet prvků množiny nejvhodnějších alternativních investic na základě hierarchie parametrů stanovených investorem.This dissertation focuses on decision making, investing and brownfield redevelopment. Especially on the analysis, evaluation and selection of previously used real estates suitable for commercial use. The objective of this dissertation is to design a method that facilitates the decision making process with many possible alternatives and large number of relevant parameters influencing the decision. The proposed method is based on the use of fuzzy logic, modeling, statistic analysis, cluster analysis, graph theory and sophisticated methods of information collection and processing. New method allows decision makers to process much larger amount of information and evaluate possible investment alternatives efficiently.
A hierarchical Mamdani-type fuzzy modelling approach with new training data selection and multi-objective optimisation mechanisms: A special application for the prediction of mechanical properties of alloy steels
In this paper, a systematic data-driven fuzzy modelling methodology is proposed, which allows to construct Mamdani fuzzy models considering both accuracy (precision) and transparency (interpretability) of fuzzy systems. The new methodology employs a fast hierarchical clustering algorithm to generate an initial fuzzy model efficiently; a training data selection mechanism is developed to identify appropriate and efficient data as learning samples; a high-performance Particle Swarm Optimisation (PSO) based multi-objective optimisation mechanism is developed to further improve the fuzzy model in terms of both the structure and the parameters; and a new tolerance analysis method is proposed to derive the confidence bands relating to the final elicited models. This proposed modelling approach is evaluated using two benchmark problems and is shown to outperform other modelling approaches. Furthermore, the proposed approach is successfully applied to complex high-dimensional modelling problems for manufacturing of alloy steels, using ‘real’ industrial data. These problems concern the prediction of the mechanical properties of alloy steels by correlating them with the heat treatment process conditions as well as the weight percentages of the chemical compositions
An artificial immune systems based predictive modelling approach for the multi-objective elicitation of Mamdani fuzzy rules: a special application to modelling alloys
In this paper, a systematic multi-objective Mamdani fuzzy modeling approach is proposed, which can be viewed as an extended version of the previously proposed Singleton fuzzy modeling paradigm. A set of new back-error propagation (BEP) updating formulas are derived so that they can replace the old set developed in the singleton version. With the substitution, the extension to the multi-objective Mamdani Fuzzy Rule-Based Systems (FRBS) is almost endemic. Due to the carefully chosen output membership functions, the inference and the defuzzification methods, a closed form integral can be deducted for the defuzzification method, which ensures the efficiency of the developed Mamdani FRBS. Some important factors, such as the variable length coding scheme and the rule alignment, are also discussed. Experimental results for a real data set from the steel industry suggest that the proposed approach is capable of eliciting not only accurate but also transparent FRBS with good generalization ability
Adaptive neurofuzzy ANFIS modeling of laser surface treatments
This paper introduces a new ANFIS adaptive
neurofuzzy inference model for laser surface heat treatments
based on the Green’s function. Due to its high
versatility, efficiency and low simulation time, this model
is suitable not only for the analysis and design of control
systems, but also for the development of an expert real time
supervision system that would allow detecting and preventing
any failure during the treatment
Optimization of fuzzy analogy in software cost estimation using linguistic variables
One of the most important objectives of software engineering community has
been the increase of useful models that beneficially explain the development of
life cycle and precisely calculate the effort of software cost estimation. In
analogy concept, there is deficiency in handling the datasets containing
categorical variables though there are innumerable methods to estimate the
cost. Due to the nature of software engineering domain, generally project
attributes are often measured in terms of linguistic values such as very low,
low, high and very high. The imprecise nature of such value represents the
uncertainty and vagueness in their elucidation. However, there is no efficient
method that can directly deal with the categorical variables and tolerate such
imprecision and uncertainty without taking the classical intervals and numeric
value approaches. In this paper, a new approach for optimization based on fuzzy
logic, linguistic quantifiers and analogy based reasoning is proposed to
improve the performance of the effort in software project when they are
described in either numerical or categorical data. The performance of this
proposed method exemplifies a pragmatic validation based on the historical NASA
dataset. The results were analyzed using the prediction criterion and indicates
that the proposed method can produce more explainable results than other
machine learning methods.Comment: 14 pages, 8 figures; Journal of Systems and Software, 2011. arXiv
admin note: text overlap with arXiv:1112.3877 by other author
- …