1997, Vol. IV, Núm. 3
http://hdl.handle.net/2099/2065
3rd. Hispano-Polish Symposium Systems Analysis & Computer Science2019-12-06T04:12:37ZEditorial [3rd. Hispano-Polish Symposium on Systems Analysis and Computer Science: selection of papers]
http://hdl.handle.net/2099/3677
Editorial [3rd. Hispano-Polish Symposium on Systems Analysis and Computer Science: selection of papers]
Hryniewicz, Olgierd; Esteva Massaguer, Francesc
2007-10-15T11:59:25ZHryniewicz, OlgierdEsteva Massaguer, FrancescNeural networks learning as a multiobjective optimal control problem
http://hdl.handle.net/2099/3498
Neural networks learning as a multiobjective optimal control problem
Krawczak, Maciej
The supervised learning process of multilayer feedforward neural networks can be considered as a class of multi-objective, multi-stage optimal control problem. An iterative parametric minimax method is proposed in which the original optimization problem is embedded into a weighted minimax formulation. The resulting auxiliary parametric optimization problems at the lower level have simple structures that are readily tackled by efficient solution methods, such as the dynamic programming or the error backpropagation algorithm. The analytical expression of the partial derivatives of systems performance indices with respect to the weighting vector in the parametric minimax formulation is derived.
2007-09-17T12:41:14ZKrawczak, MaciejThe supervised learning process of multilayer feedforward neural networks can be considered as a class of multi-objective, multi-stage optimal control problem. An iterative parametric minimax method is proposed in which the original optimization problem is embedded into a weighted minimax formulation. The resulting auxiliary parametric optimization problems at the lower level have simple structures that are readily tackled by efficient solution methods, such as the dynamic programming or the error backpropagation algorithm. The analytical expression of the partial derivatives of systems performance indices with respect to the weighting vector in the parametric minimax formulation is derived.Testing satistical hipotheses in fuzzy environment
http://hdl.handle.net/2099/3497
Testing satistical hipotheses in fuzzy environment
Gregorzewski, Przemyslaw; Hryniewicz, Olgierd
In traditional statistics all parameters of the mathematical model and
possible observations should be well defined. Sometimes such assumption
appears too rigid for the real-life problems, especially while dealing with
linguistic data or imprecise requirements. To relax this rigidity fuzzy
methods are incorporated into statistics. We review hitherto existing
achievements in testing statistical hypotheses in fuzzy environment, point
out their advantages or disadvantages and practical problems. We propose
also a formalization of that decision problem and indicate the directions of
further investigations in order to construct a more general theory.
2007-09-17T12:26:36ZGregorzewski, PrzemyslawHryniewicz, OlgierdIn traditional statistics all parameters of the mathematical model and
possible observations should be well defined. Sometimes such assumption
appears too rigid for the real-life problems, especially while dealing with
linguistic data or imprecise requirements. To relax this rigidity fuzzy
methods are incorporated into statistics. We review hitherto existing
achievements in testing statistical hypotheses in fuzzy environment, point
out their advantages or disadvantages and practical problems. We propose
also a formalization of that decision problem and indicate the directions of
further investigations in order to construct a more general theory.A genetic algorithm for the multistage control of a fuzzy system in a fuzzy environment
http://hdl.handle.net/2099/3496
A genetic algorithm for the multistage control of a fuzzy system in a fuzzy environment
Kacprzyk, Janusz
We discuss a prescriptive approach to multistage optimal fuzzy control of a
fuzzy system, given by a fuzzy state transition equation. Fuzzy constraints
and fuzzy goals at consecutive control stages are given, and their
confluence, Bellman and Zadeh's fuzzy decision, is an explicit performance
function to be optimized. First, we briefly survey previous basic solution
methods of dynamic programming (Baldwin and Pilsworth, 1982) and
branch-and-bound (Kacprzyk, 1979), which are plagued by low numerical
efficiency, and then sketch Kacprzyk's (1993a--e, 1994a) approach based on
possibilistic interpolative reasoning aimed at enhancing the numerical
efficiency but requiring a solution of a simplified auxiliary problem, and
then some "readjustment" of the solution obtained.
We propose a genetic algorithm for solving the problem considered. Real
coding and specially defined operations of crossover, mutation, etc. are
employed. The approach yields good results, and is quite efficient
numerically
2007-09-17T12:05:58ZKacprzyk, JanuszWe discuss a prescriptive approach to multistage optimal fuzzy control of a
fuzzy system, given by a fuzzy state transition equation. Fuzzy constraints
and fuzzy goals at consecutive control stages are given, and their
confluence, Bellman and Zadeh's fuzzy decision, is an explicit performance
function to be optimized. First, we briefly survey previous basic solution
methods of dynamic programming (Baldwin and Pilsworth, 1982) and
branch-and-bound (Kacprzyk, 1979), which are plagued by low numerical
efficiency, and then sketch Kacprzyk's (1993a--e, 1994a) approach based on
possibilistic interpolative reasoning aimed at enhancing the numerical
efficiency but requiring a solution of a simplified auxiliary problem, and
then some "readjustment" of the solution obtained.
We propose a genetic algorithm for solving the problem considered. Real
coding and specially defined operations of crossover, mutation, etc. are
employed. The approach yields good results, and is quite efficient
numericallyMulti-stage genetic fuzzy systems based on the iterative rule learning approach
http://hdl.handle.net/2099/3495
Multi-stage genetic fuzzy systems based on the iterative rule learning approach
González Muñoz, Antonio; Herrera Triguero, Francisco
Genetic algorithms (GAs) represent a class of adaptive search techniques inspired by natural evolution mechanisms. The search properties of GAs make
them suitable to be used in machine learning processes and for developing
fuzzy systems, the so-called genetic
fuzzy systems (GFSs).
In this contribution, we discuss genetics-based machine learning processes presenting the iterative rule learning approach, and a special kind of GFS, a multi-stage GFS based on the iterative rule learning approach, by learning from examples.
2007-09-17T11:53:22ZGonzález Muñoz, AntonioHerrera Triguero, FranciscoGenetic algorithms (GAs) represent a class of adaptive search techniques inspired by natural evolution mechanisms. The search properties of GAs make
them suitable to be used in machine learning processes and for developing
fuzzy systems, the so-called genetic
fuzzy systems (GFSs).
In this contribution, we discuss genetics-based machine learning processes presenting the iterative rule learning approach, and a special kind of GFS, a multi-stage GFS based on the iterative rule learning approach, by learning from examples.Weighting quantitative and qualitative variables in clustering methods
http://hdl.handle.net/2099/3494
Weighting quantitative and qualitative variables in clustering methods
Gibert, Karina; Cortés García, Claudio Ulises
Description of individuals in ill-structured domains produces messy data matrices.
In this context, automated classification requires the management of those kind of matrices.
One of the features involved in clustering is the evaluation of distances between individuals. Then, a special function to calculate distances between individuals partially simultaneously described by qualitative and quantitative variables is required.
In this paper properties and details of the metrics used by Klass in this situation is presented --- Klass is a clustering system oriented to the classification of
ill-structured domains which implements an adapted version of the reciprocal neighbors algorithm; it also takes advantage of any
additional information that an expert can provide about the target concepts.
2007-09-17T11:35:09ZGibert, KarinaCortés García, Claudio UlisesDescription of individuals in ill-structured domains produces messy data matrices.
In this context, automated classification requires the management of those kind of matrices.
One of the features involved in clustering is the evaluation of distances between individuals. Then, a special function to calculate distances between individuals partially simultaneously described by qualitative and quantitative variables is required.
In this paper properties and details of the metrics used by Klass in this situation is presented --- Klass is a clustering system oriented to the classification of
ill-structured domains which implements an adapted version of the reciprocal neighbors algorithm; it also takes advantage of any
additional information that an expert can provide about the target concepts.Sedàs: a semantic based general classifier system
http://hdl.handle.net/2099/3493
Sedàs: a semantic based general classifier system
Valls Mateu, Aïda; Riaño Ramos, David; Torra Ferré, Vicenç
In this work we present the general classifier system Sedàs.
We show how this system implements the description of the domain
and how it builds similarity matrices and classification trees. The system uses a new semantics, introduced in [Torra96], to define a distance between qualitative values.
2007-09-17T11:23:47ZValls Mateu, AïdaRiaño Ramos, DavidTorra Ferré, VicençIn this work we present the general classifier system Sedàs.
We show how this system implements the description of the domain
and how it builds similarity matrices and classification trees. The system uses a new semantics, introduced in [Torra96], to define a distance between qualitative values.Towards Specifying with Inclusions
http://hdl.handle.net/2099/3492
Towards Specifying with Inclusions
Agustí Cullell, Jaume; Puigsegur i Figueras, Jordi; Schorlemmer, Wernher Marco
In this article we present a functional specification language based on inclusions between set expressions.
Instead of computing with data individuals we deal with their classification into sets.
The specification of functions and relations by means of inclusions can be considered as a generalization of the conventional algebraic specification by means of equations.
The main aim of this generalization is to facilitate the incremental refinement of specifications.
Furthermore, inclusional specifications admit a natural visual syntax which can also be used to visualize the reasoning process.
We show that reasoning with inclusions is well captured by bi-rewriting, a rewriting technique introduced by Levy and Agustí [15].
However, there are still key problems to be solved in order to have executable inclusional specifications, necessary for rapid prototyping purposes.
The article mainly points to the potentialities and difficulties of specifying with inclusions.
2007-09-17T10:56:59ZAgustí Cullell, JaumePuigsegur i Figueras, JordiSchorlemmer, Wernher MarcoIn this article we present a functional specification language based on inclusions between set expressions.
Instead of computing with data individuals we deal with their classification into sets.
The specification of functions and relations by means of inclusions can be considered as a generalization of the conventional algebraic specification by means of equations.
The main aim of this generalization is to facilitate the incremental refinement of specifications.
Furthermore, inclusional specifications admit a natural visual syntax which can also be used to visualize the reasoning process.
We show that reasoning with inclusions is well captured by bi-rewriting, a rewriting technique introduced by Levy and Agustí [15].
However, there are still key problems to be solved in order to have executable inclusional specifications, necessary for rapid prototyping purposes.
The article mainly points to the potentialities and difficulties of specifying with inclusions.Milord II: language description
http://hdl.handle.net/2099/3491
Milord II: language description
Puyol-Gruart, Josep; Sierra Garriga, Carlos
In this paper we describe the language Milord. The description is made in terms of computer language concepts and not in terms of the logical semantics underlying it. In this sense the paper complements others in
which the focus of the description has been either the object level multi-valued language description, or the reflective component of the architecture, or even the several applications built using it. All the necessary elements to understand how a system
programmed in Milord executes have room in this full description: types, facts, rules, modules, local logics, control rategies, ... Although the description is guided by the Milord language syntax, this is by no means
a user's manual, which would deserve a much longer document, but a language summary description that places all the components of the language in their correct place.
2007-09-17T10:25:23ZPuyol-Gruart, JosepSierra Garriga, CarlosIn this paper we describe the language Milord. The description is made in terms of computer language concepts and not in terms of the logical semantics underlying it. In this sense the paper complements others in
which the focus of the description has been either the object level multi-valued language description, or the reflective component of the architecture, or even the several applications built using it. All the necessary elements to understand how a system
programmed in Milord executes have room in this full description: types, facts, rules, modules, local logics, control rategies, ... Although the description is guided by the Milord language syntax, this is by no means
a user's manual, which would deserve a much longer document, but a language summary description that places all the components of the language in their correct place.Valuation based systems for multistage control
http://hdl.handle.net/2099/3162
Valuation based systems for multistage control
Wierzchon, S.T.
In this paper a graphical technique for solving multistage control problems is introduced. Its applicability and generality is demonstrated on examples concerning deterministic systems with deterministic or fuzzy constraints and stochastic systems with fuzzy constraints.
2007-07-04T09:45:27ZWierzchon, S.T.In this paper a graphical technique for solving multistage control problems is introduced. Its applicability and generality is demonstrated on examples concerning deterministic systems with deterministic or fuzzy constraints and stochastic systems with fuzzy constraints.