The Experts below are selected from a list of 30258 Experts worldwide ranked by ideXlab platform
Francisco Herrera - One of the best experts on this subject based on the ideXlab platform.
-
a fast and scalable multiobjective genetic Fuzzy system for linguistic Fuzzy Modeling in high dimensional regression problems
IEEE Transactions on Fuzzy Systems, 2011Co-Authors: Rafael Alcala, Maria Jose Gacto, Francisco HerreraAbstract:Linguistic Fuzzy Modeling in high-dimensional regression problems poses the challenge of exponential-rule explosion when the number of variables and/or instances becomes high. One way to address this problem is by determining the used variables, the linguistic partitioning and the rule set together, in order to only evolve very simple, but still accurate models. However, evolving these components together is a difficult task, which involves a complex search space. In this study, we propose an effective multiobjective evolutionary algorithm that, based on embedded genetic database (DB) learning (involved variables, granularities, and slight Fuzzy-partition displacements), allows the fast learning of simple and quite-accurate linguistic models. Some efficient mechanisms have been designed to ensure a very fast, but not premature, convergence in problems with a high number of variables. Further, since additional problems could arise for datasets with a large number of instances, we also propose a general mechanism for the estimation of the model error when using evolutionary algorithms, by only considering a reduced subset of the examples. By doing so, we can also apply a fast postprocessing stage for further refining the learned solutions. We tested our approach on 17 real-world datasets with different numbers of variables and instances. Three well-known methods based on embedded genetic DB learning have been executed as references. We compared the different approaches by applying nonparametric statistical tests for multiple comparisons. The results confirm the effectiveness of the proposed method not only in terms of scalability but in terms of the simplicity and generalizability of the obtained models as well.
-
a proposal for the genetic lateral tuning of linguistic Fuzzy systems and its interaction with rule selection
IEEE Transactions on Fuzzy Systems, 2007Co-Authors: Rafael Alcala, Jesus Alcalafdez, Francisco HerreraAbstract:Linguistic Fuzzy Modeling allows us to deal with the Modeling of systems by building a linguistic model which is clearly interpretable by human beings. However, since the accuracy and the interpretability of the obtained model are contradictory properties, the necessity of improving the accuracy of the linguistic model arises when complex systems are modeled. To solve this problem, one of the research lines in recent years has led to the objective of giving more accuracy to linguistic Fuzzy Modeling without losing the interpretability to a high level. In this paper, a new postprocessing approach is proposed to perform an evolutionary lateral tuning of membership functions, with the main aim of obtaining linguistic models with higher levels of accuracy while maintaining good interpretability. To do so, we consider a new rule representation scheme base on the linguistic 2-tuples representation model which allows the lateral variation of the involved labels. Furthermore, the cooperation of the lateral tuning together with Fuzzy rule reduction mechanisms is studied in this paper, presenting results on different real applications. The obtained results show the good performance of the proposed approach in high-dimensional problems and its ability to cooperate with methods to remove unnecessary rules.
-
Genetic tuning on Fuzzy systems based on the linguistic 2-tuples representation
2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), 2004Co-Authors: Rafael Alcala, Francisco HerreraAbstract:Linguistic Fuzzy Modeling allows us to deal with the Modeling of systems building a linguistic model clearly interpretable by human beings. However, in this kind of Modeling the accuracy and the interpretability of the obtained model are contradictory properties directly depending on the learning process and/or the model structure. Thus, the necessity of improving the linguistic model accuracy arises when complex systems are modeled. To solve this problem, one of the research lines of this framework in the last years has leaded up to the objective of giving more accuracy to the linguistic Fuzzy Modeling, without losing the associated interpretability to a high level. In this work, a new post-processing method of Fuzzy rule-based systems is proposed by means of an evolutionary lateral tuning of the linguistic variables, with the main aim of obtaining Fuzzy rule-based systems with a better accuracy and maintaining a good interpretability. To do so, this tuning considers a new rule representation scheme by using the linguistic 2-tuples representation model which allows the lateral variation of the involved labels. As an example of application of these kinds of systems, we analyze this approach considering a real-world problem.
-
can linguistic Modeling be as accurate as Fuzzy Modeling without losing its description to a high degree
2000Co-Authors: Jorge Casillas, Oscar Cordon, Francisco HerreraAbstract:In system Modeling with Fuzzy Rule-Based Systems (FRBSs), we may usually find two contradictory requirements, the interpretability and the accuracy of the model obtained. As known, Linguistic Modeling (LM)—where the main requirement is the interpretability—is developed by linguistic FRBSs, while Fuzzy Modeling (FM)—where the main requirement is the accuracy—is developed, among others, by approximate FRBSs. Whilst the fact of making linguistic FRBSs be highly interpretable involves establishing hard restrictions to the rule structure (due to the use of a global semantic) thus losing flexibility, relaxing such restrictions, as approximate FRBSs do (using a local semantic), can make more flexible models to be obtained but losing their interpretability. The main objective of this contribution it to carry out a comparative analysis between LM and FM beyond the classical approach resigned to simply consider LM with a good interpretability but a bad accuracy. Some possibilities to significantly improve the precision of the linguistic models keeping good legibility will be introduced under the assumption that better accuracy could be obtained looking for a good balance between model flexibility and Modeling simplicity. The good performance of such improvements opposite to FM will be shown by means of a wide experimental study applying fourteen different learning methods, carefully selected, to four Modeling problems with different nature.
Soichiro Takahashi - One of the best experts on this subject based on the ideXlab platform.
-
state space approach to adaptive Fuzzy Modeling for financial investment
Applied Soft Computing, 2019Co-Authors: Masafumi Nakano, Akihiko Takahashi, Soichiro TakahashiAbstract:Abstract This paper proposes a new adaptive learning framework for Fuzzy system under dynamically changing environment. Especially, a state–space model with filtering algorithm, traditionally used for the estimation of unobservable state variables, is applied to online non-linear optimization problems by reinterpreting control variables and objective function as state variables and observation model, respectively. Our proposed methodology substantially improves the flexibility of the objective function, which enables to construct the adaptive Fuzzy system achieving arbitrarily designed user’s objective. In addition, time-series structure is actively introduced into the parameter transition, whose proper Modeling is expected to enhance the performance. Particularly, the introduction of mean-reversion process makes it possible to adaptively learn model parameters around specific predetermined levels obtained by existing learning methodologies. As an application of adaptive learning Fuzzy system for financial investment, the current work focuses on the construction of the target return replication portfolio. Concretely, the target return is specified as zero floored market index considering investor’s great demand to construct a portfolio with restricted downside risk. The validity of our framework is shown by out-of-sample numerical experiments with the data of well-known high liquid instruments such as S&P500 and TOPIX, which indicates the robustness and reliability of our proposed method in practice.
-
state space approach to adaptive Fuzzy Modeling for financial investment
Social Science Research Network, 2017Co-Authors: Masafumi Nakano, Akihiko Takahashi, Soichiro TakahashiAbstract:This paper proposes a new state space approach to adaptive Fuzzy Modeling under the dynamically changing environment, where Bayesian filtering sequentially learns parameters including model structures as state variables. Moreover with a particle filtering algorithm, our approach is widely applicable to the machine learning for real-time observation data flows. To show the effectiveness of our framework, a Takagi-Sugeno-Kang Fuzzy model is concretely designed for financial portfolio construction based on a benchmark return, that is stock market index (e.g. S\&P 500 index) return with non-negative lower bound, and successfully attains fine risk-return profiles. An out-of-sample simulation with our proposed portfolio construction demonstrates the validity of our framework.
Takeshi Furuhashi - One of the best experts on this subject based on the ideXlab platform.
-
Fuzzy Modeling using genetic algorithms with Fuzzy entropy as conciseness measure
Information Sciences, 2001Co-Authors: Tatsuya Suzuki, Takeshi Furuhashi, Tetsuji Kodama, H TsutsuiAbstract:Abstract In this paper, a Fuzzy Modeling method using genetic algorithms (GAs) with a conciseness measure is presented. This paper introduces De Luca and Termini's Fuzzy entropy to evaluate the shape of a membership function, and proposes another measure to evaluate the deviation of a membership function from symmetry. A combined measure is then derived from these two measures, and a new conciseness measure is defined for evaluation of the shape and allocation of the membership functions of a Fuzzy model. Numerical results show that the new conciseness measure is effective for Fuzzy Modeling formulated as a multi-objective optimization problem.
-
generality and conciseness of submodels in hierarchical Fuzzy Modeling
Simulated Evolution and Learning, 1998Co-Authors: Kanta Tachibana, Takeshi FuruhashiAbstract:Hierarchical Fuzzy Modeling is a promising technique to describe input-output relationships of nonlinear systems with multiple inputs. This paper presents a new method of dividing input spaces for hierarchical Fuzzy Modeling using Fuzzy Neural Network (FNN) and Genetic Algorithm (GA). Uneven division of input space for each submodel in the hierarchical Fuzzy model can be achieved with the proposed method. The obtained hierarchical Fuzzy models are probable to be more concise and more precise than those identified with the conventional methods. Studies on effects of the weights on performance indices for the Fuzzy model are also shown in this paper.
-
on Fuzzy Modeling using Fuzzy neural networks with the back propagation algorithm
IEEE Transactions on Neural Networks, 1992Co-Authors: Shinichi Horikawa, Takeshi Furuhashi, Yoshiki UchikawaAbstract:A Fuzzy Modeling method using Fuzzy neural networks with the backpropagation algorithm is presented. The method can identify the Fuzzy model of a nonlinear system automatically. The feasibility of the method is examined using simple numerical data. >
Rafael Alcala - One of the best experts on this subject based on the ideXlab platform.
-
a fast and scalable multiobjective genetic Fuzzy system for linguistic Fuzzy Modeling in high dimensional regression problems
IEEE Transactions on Fuzzy Systems, 2011Co-Authors: Rafael Alcala, Maria Jose Gacto, Francisco HerreraAbstract:Linguistic Fuzzy Modeling in high-dimensional regression problems poses the challenge of exponential-rule explosion when the number of variables and/or instances becomes high. One way to address this problem is by determining the used variables, the linguistic partitioning and the rule set together, in order to only evolve very simple, but still accurate models. However, evolving these components together is a difficult task, which involves a complex search space. In this study, we propose an effective multiobjective evolutionary algorithm that, based on embedded genetic database (DB) learning (involved variables, granularities, and slight Fuzzy-partition displacements), allows the fast learning of simple and quite-accurate linguistic models. Some efficient mechanisms have been designed to ensure a very fast, but not premature, convergence in problems with a high number of variables. Further, since additional problems could arise for datasets with a large number of instances, we also propose a general mechanism for the estimation of the model error when using evolutionary algorithms, by only considering a reduced subset of the examples. By doing so, we can also apply a fast postprocessing stage for further refining the learned solutions. We tested our approach on 17 real-world datasets with different numbers of variables and instances. Three well-known methods based on embedded genetic DB learning have been executed as references. We compared the different approaches by applying nonparametric statistical tests for multiple comparisons. The results confirm the effectiveness of the proposed method not only in terms of scalability but in terms of the simplicity and generalizability of the obtained models as well.
-
a proposal for the genetic lateral tuning of linguistic Fuzzy systems and its interaction with rule selection
IEEE Transactions on Fuzzy Systems, 2007Co-Authors: Rafael Alcala, Jesus Alcalafdez, Francisco HerreraAbstract:Linguistic Fuzzy Modeling allows us to deal with the Modeling of systems by building a linguistic model which is clearly interpretable by human beings. However, since the accuracy and the interpretability of the obtained model are contradictory properties, the necessity of improving the accuracy of the linguistic model arises when complex systems are modeled. To solve this problem, one of the research lines in recent years has led to the objective of giving more accuracy to linguistic Fuzzy Modeling without losing the interpretability to a high level. In this paper, a new postprocessing approach is proposed to perform an evolutionary lateral tuning of membership functions, with the main aim of obtaining linguistic models with higher levels of accuracy while maintaining good interpretability. To do so, we consider a new rule representation scheme base on the linguistic 2-tuples representation model which allows the lateral variation of the involved labels. Furthermore, the cooperation of the lateral tuning together with Fuzzy rule reduction mechanisms is studied in this paper, presenting results on different real applications. The obtained results show the good performance of the proposed approach in high-dimensional problems and its ability to cooperate with methods to remove unnecessary rules.
-
Genetic tuning on Fuzzy systems based on the linguistic 2-tuples representation
2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), 2004Co-Authors: Rafael Alcala, Francisco HerreraAbstract:Linguistic Fuzzy Modeling allows us to deal with the Modeling of systems building a linguistic model clearly interpretable by human beings. However, in this kind of Modeling the accuracy and the interpretability of the obtained model are contradictory properties directly depending on the learning process and/or the model structure. Thus, the necessity of improving the linguistic model accuracy arises when complex systems are modeled. To solve this problem, one of the research lines of this framework in the last years has leaded up to the objective of giving more accuracy to the linguistic Fuzzy Modeling, without losing the associated interpretability to a high level. In this work, a new post-processing method of Fuzzy rule-based systems is proposed by means of an evolutionary lateral tuning of the linguistic variables, with the main aim of obtaining Fuzzy rule-based systems with a better accuracy and maintaining a good interpretability. To do so, this tuning considers a new rule representation scheme by using the linguistic 2-tuples representation model which allows the lateral variation of the involved labels. As an example of application of these kinds of systems, we analyze this approach considering a real-world problem.
Robert Babuska - One of the best experts on this subject based on the ideXlab platform.
-
neuro Fuzzy methods for nonlinear system identification
Annual Reviews in Control, 2003Co-Authors: Robert Babuska, H B VerbruggenAbstract:Most processes in industry are characterized by nonlinear and time-varying behavior. Nonlinear system identification is becoming an important tool which can be used to improve control performance and achieve robust fault-tolerant behavior. Among the different nonlinear identification techniques, methods based on neuro-Fuzzy models are gradually becoming established not only in the academia but also in industrial applications. Neuro-Fuzzy Modeling can be regarded as a gray-box technique on the boundary between neural networks and qualitative Fuzzy models. The tools for building neuro-Fuzzy models are based on combinations of algorithms from the fields of neural networks, pattern recognition and regression analysis. In this paper, an overview of neuro-Fuzzy Modeling methods for nonlinear system identification is given, with an emphasis on the tradeoff between accuracy and interpretability. © 2003 Published by Elsevier Science Ltd.
-
Fuzzy Modeling with multivariate membership functions gray box identification and control design
Systems Man and Cybernetics, 2001Co-Authors: Janos Abonyi, Robert Babuska, Ferenc SzeifertAbstract:A novel framework for Fuzzy Modeling and model-based control design is described. The Fuzzy model is of the Takagi-Sugeno (TS) type with constant consequents. It uses multivariate antecedent membership functions obtained by Delaunay triangulation of their characteristic points. The number and position of these points are determined by an iterative insertion algorithm. Constrained optimization is used to estimate the consequent parameters, where the constraints are based on control-relevant a priori knowledge about the modeled process. Finally, methods for control design through linearization and inversion of this model are developed. The proposed techniques are demonstrated by means of two benchmark examples: identification of the well-known Box-Jenkins gas furnace and inverse model-based control of a pH process. The obtained results are compared with results from the literature.
