1999, Vol. VI, Núm. 1
http://hdl.handle.net/2099/2068
2018-08-15T06:51:02ZEditorial [ New editorial line and format]
http://hdl.handle.net/2099/3679
Editorial [ New editorial line and format]
Castro Peña, Juan Luis; Jacas Moral, Juan
2007-10-15T12:27:17ZCastro Peña, Juan LuisJacas Moral, JuanAn example of the knowledge based controller-design and evaluation
http://hdl.handle.net/2099/3548
An example of the knowledge based controller-design and evaluation
Tezak, Oto
Knowledge based controller for a balance control model is presented in this paper. The design of the controller was based
on the human control of the same process. Developed controller is tested by means of simulation and operation on the laboratory
balance control model. The simulation results of the controller as well as a statistical description of the experiments with developed controller and human control is presented in the paper.
Verification is based on experiments with an intelligent controller and a human control of the same plant. The results obtained with the developed intelligent controller (set up time, control error, speed, speed range) are satisfied and similar to the results of human control.
2007-09-25T10:33:35ZTezak, OtoKnowledge based controller for a balance control model is presented in this paper. The design of the controller was based
on the human control of the same process. Developed controller is tested by means of simulation and operation on the laboratory
balance control model. The simulation results of the controller as well as a statistical description of the experiments with developed controller and human control is presented in the paper.
Verification is based on experiments with an intelligent controller and a human control of the same plant. The results obtained with the developed intelligent controller (set up time, control error, speed, speed range) are satisfied and similar to the results of human control.Evolutionary design of fuzzy logic controllers using strongly-typed GP
http://hdl.handle.net/2099/3547
Evolutionary design of fuzzy logic controllers using strongly-typed GP
Alba Torres, Enrique; Cotta Porras, Carlos; Troya Linero, José María
An evolutionary approach to the design of fuzzy logic controllers is presented in this paper. We propose the use of the genetic programming paradigm to evolve fuzzy rule-bases (internally represented as type-constrained syntactic trees). This model has been applied to the cart-centering problem, although it can be readily extended to other problems. The obtained results show that a good parameterization of the algorithm, and an appropriate evaluation function, can lead to near-optimal solutions.
2007-09-25T10:02:05ZAlba Torres, EnriqueCotta Porras, CarlosTroya Linero, José MaríaAn evolutionary approach to the design of fuzzy logic controllers is presented in this paper. We propose the use of the genetic programming paradigm to evolve fuzzy rule-bases (internally represented as type-constrained syntactic trees). This model has been applied to the cart-centering problem, although it can be readily extended to other problems. The obtained results show that a good parameterization of the algorithm, and an appropriate evaluation function, can lead to near-optimal solutions.Fuzzy max-min classifiers decide locally on the basis of two attributes
http://hdl.handle.net/2099/3546
Fuzzy max-min classifiers decide locally on the basis of two attributes
Von Schmidt, Birka; Klawonn, Frank
Fuzzy classification systems differ from fuzzy controllers in the form of their outputs. For classification problems a decision between a finite number of discrete classes has to be made, whereas in fuzzy control the output domain is usually continuous, i.e.\ a real interval. In this paper we consider fuzzy classification systems using the max-min inference scheme and classifying an unknown datum on the basis of maximum matching, i.e.\ assigning it to the class appearing in the consequent of the rule whose premise fits best. We basically show that this inference scheme locally takes only two attributes (variables) into account for the classification decision.
2007-09-25T09:54:26ZVon Schmidt, BirkaKlawonn, FrankFuzzy classification systems differ from fuzzy controllers in the form of their outputs. For classification problems a decision between a finite number of discrete classes has to be made, whereas in fuzzy control the output domain is usually continuous, i.e.\ a real interval. In this paper we consider fuzzy classification systems using the max-min inference scheme and classifying an unknown datum on the basis of maximum matching, i.e.\ assigning it to the class appearing in the consequent of the rule whose premise fits best. We basically show that this inference scheme locally takes only two attributes (variables) into account for the classification decision.Capital budgeting problems with fuzzy cashflows
http://hdl.handle.net/2099/3545
Capital budgeting problems with fuzzy cashflows
Carlsson, Christer; Fuller, Robert
We consider the {\em internal rate of return} (IRR) decision rule in capital budgeting problems with fuzzy cash flows.
The possibility distribution of the IRR at any $r\geq0$,is defined to be the degree of possibility that the (fuzzy) net present value of the project with discount factor $r$ equals to zero.
Generalizing our earlier results on fuzzy capital budegeting problems \cite{Car99} we
show that the possibility distribution of the {IRR} is a highly nonlinear function which is getting more and more unbalanced
by increasing imprecision in the future cash flow.
However, it is stable under small changes in the membership functions of fuzzy numbers
representing the lingusitic values of future cash flows.
2007-09-25T09:46:38ZCarlsson, ChristerFuller, RobertWe consider the {\em internal rate of return} (IRR) decision rule in capital budgeting problems with fuzzy cash flows.
The possibility distribution of the IRR at any $r\geq0$,is defined to be the degree of possibility that the (fuzzy) net present value of the project with discount factor $r$ equals to zero.
Generalizing our earlier results on fuzzy capital budegeting problems \cite{Car99} we
show that the possibility distribution of the {IRR} is a highly nonlinear function which is getting more and more unbalanced
by increasing imprecision in the future cash flow.
However, it is stable under small changes in the membership functions of fuzzy numbers
representing the lingusitic values of future cash flows.Boolean structure of triangular forms
http://hdl.handle.net/2099/3544
Boolean structure of triangular forms
Ziaie, Seyed Abbas; Ray, Suryansu; Mashinchi, Mashallah; Kamran, Rasool
In this note results obtained by S.Ray (1997) on representation of a Boolean
algebra by its triangular norms are generalized.
2007-09-25T09:37:16ZZiaie, Seyed AbbasRay, SuryansuMashinchi, MashallahKamran, RasoolIn this note results obtained by S.Ray (1997) on representation of a Boolean
algebra by its triangular norms are generalized.BL-algebras of basic fuzzy logic
http://hdl.handle.net/2099/3543
BL-algebras of basic fuzzy logic
Turunen, Esko
BL-algebras [7] rise as Lindenbaum algebras from certain logical axioms familiar in fuzzy logic framework.
BL-algebras are studied by means of deductive systems and co-annihilators.
Duals of many theorems known to hold in MV-algebra theory remain valid for BL-algebras, too.
2007-09-25T09:24:44ZTurunen, EskoBL-algebras [7] rise as Lindenbaum algebras from certain logical axioms familiar in fuzzy logic framework.
BL-algebras are studied by means of deductive systems and co-annihilators.
Duals of many theorems known to hold in MV-algebra theory remain valid for BL-algebras, too.Weighted sums of aggregation operators
http://hdl.handle.net/2099/3542
Weighted sums of aggregation operators
Calvo Sánchez, Tomasa; Baets, Bernard de; Mesiar, Radko
he aim of this work is to investigate when a
weighted sum, or in other words, a linear combination, of two or more aggregation operators leads to a new aggregation operator.
For weights belonging to the real unit interval, we obtain a convex combination and the answer is known to be always positive. However, we will show that also other weights can be used, depending upon the aggregation operators involved. A first set
of suitable weights is obtained by a general method based on the variation of the partial derivatives of the aggregation operators. When considering the combination of OWA operators only, all suitable weights can be determined. These results are described explicitly for the case of two aggregation operators, and also for the case of two and three OWA operators.
2007-09-25T09:13:07ZCalvo Sánchez, TomasaBaets, Bernard deMesiar, Radkohe aim of this work is to investigate when a
weighted sum, or in other words, a linear combination, of two or more aggregation operators leads to a new aggregation operator.
For weights belonging to the real unit interval, we obtain a convex combination and the answer is known to be always positive. However, we will show that also other weights can be used, depending upon the aggregation operators involved. A first set
of suitable weights is obtained by a general method based on the variation of the partial derivatives of the aggregation operators. When considering the combination of OWA operators only, all suitable weights can be determined. These results are described explicitly for the case of two aggregation operators, and also for the case of two and three OWA operators.A hybrid evolutionary approach to intelligent system design
http://hdl.handle.net/2099/3541
A hybrid evolutionary approach to intelligent system design
Badr, Amr; Farag, Ibrahim; Eid, Saad
The problem of developing a general methodology for system design has always been demanding. For this purpose, an evolutionary algorithm, adapted with design-specific representation data structures is devised. The representation modeling the system to be designed, is composed of three levels of abstraction: the first, is an 'abstract brain' layer- mainly a number of competing finite state machines, which in turn control the second level composed of fuzzy Petri nets; the third level constitutes the component automata of the Petri nets. Several mutation operators have been developed acting on that representation. The function of these operators is controlled by a 'stochastic L-system'- representing the chronological application of design tasks. The Petri nets' layer receives tokens from the abstract brain layer and serves in modeling synchronous and asynchronous behaviour. The framework is specifically suited to what is called 'distributed design'- design of communicating systems. For that purpose, a methodology has been developed and implemented/ tested in the system GIGANTEC: Genetic Induction for General Analytical Non-numeric Task Evolution Compiler.
2007-09-25T09:03:12ZBadr, AmrFarag, IbrahimEid, SaadThe problem of developing a general methodology for system design has always been demanding. For this purpose, an evolutionary algorithm, adapted with design-specific representation data structures is devised. The representation modeling the system to be designed, is composed of three levels of abstraction: the first, is an 'abstract brain' layer- mainly a number of competing finite state machines, which in turn control the second level composed of fuzzy Petri nets; the third level constitutes the component automata of the Petri nets. Several mutation operators have been developed acting on that representation. The function of these operators is controlled by a 'stochastic L-system'- representing the chronological application of design tasks. The Petri nets' layer receives tokens from the abstract brain layer and serves in modeling synchronous and asynchronous behaviour. The framework is specifically suited to what is called 'distributed design'- design of communicating systems. For that purpose, a methodology has been developed and implemented/ tested in the system GIGANTEC: Genetic Induction for General Analytical Non-numeric Task Evolution Compiler.