Approximative fuzzy rules approaches for classification with hybrid-GA techniques

Gómez‐Skarmeta, Antonio F.; Valdés-Vela, Mercedes; Jiménez, Fernando; Marín-Blázquez, Javier G.

doi:10.1016/s0020-0255(01)00148-7

Cited by 32 publications

(19 citation statements)

References 22 publications

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“…This model is used because it has a lower number of rules and is therefore a more compact model, and, using Equation~7!, these solutions are dominated and therefore are not shown. Gómez-Skarmeta et al 24 obtain different fuzzy models to classify the Iris data set. Approximative and descriptive models are obtained.…”

Section: Classification Of the Iris Data Setmentioning

confidence: 99%

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Improving interpretability in approximative fuzzy models via multiobjective evolutionary algorithms

Gómez‐Skarmeta

Jiménez

Sánchez

2007

Int. J. Intell. Syst.

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Current research lines in fuzzy modeling mostly tackle improving the accuracy in descriptive models and improving of the interpretability in approximative models. This article deals with the second issue, approaching the problem by means of multiobjective optimization in which accuracy and interpretability criteria are simultaneously considered. Evolutionary algorithms are especially appropriated for multiobjective optimization because they can capture multiple Pareto solutions in a single run of the algorithm. We propose a multiobjective evolutionary algorithm to find multiple Pareto solutions~fuzzy models! showing a trade-off between accuracy and interpretability. Additionally, neural-network-based techniques in combination with ad hoc techniques for improving interpretability are incorporated into the multiobjective evolutionary algorithm to improve the efficiency of the algorithm.

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Section: Classification Of the Iris Data Setmentioning

confidence: 99%

“…MONEA obtains the same classification rate using only three rules, but is also a transparent model. Another model obtained by Gómez-Skarmeta et al 24 is a descriptive model that uses linguistic labels. In this case, an interpretable model is obtained with four rules and MC/NC ϭ 3/0.…”

Section: Classification Of the Iris Data Setmentioning

confidence: 99%

Improving interpretability in approximative fuzzy models via multiobjective evolutionary algorithms

Gómez‐Skarmeta

Jiménez

Sánchez

2007

Int. J. Intell. Syst.

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“…Gomez-Skarmeta et al [3] evaluated the use of different methods from the fuzzy modeling field for classification tasks and the potential of their integration in producing better classification results. The methods considered, approximate in nature, study the integration of techniques with an initial rule generation step and a following rule tuning approach using different evolutionary algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Genetic Algorithm and Bacterial Foraging Approach for Global Optimization and Robust Tuning of PID Controller with Disturbance Rejection

Kim¹,

Abraham

2007

Hybrid Evolutionary Algorithms

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Summary.The social foraging behavior of Escherichia coli (E. Coli) bacteria has been used to solve optimization problems. This chapter proposes a hybrid approach involving genetic algorithm (GA) and bacterial foraging (BF) algorithm for function optimization problems. We first illustrate the proposed method using four test functions and the performance of the algorithm is studied with an emphasis on mutation, crossover, variation of step sizes, chemotactic steps, and the lifetime of the bacteria. The proposed algorithm is then used to tune a PID controller of an Automatic Voltage Regulator (AVR). To design disturbance rejection tuning, disturbance rejection conditions based on H ∞ are illustrated and the performance of response is computed for the designed PID controller as the integral of time weighted squared error. Simulation results clearly illustrate that the proposed approach is very efficient and could easily be extended for other global optimization problems. IntroductionIn the last decade, approaches based on genetic algorithms (GA) have received increased attention from the academic and industrial communities for dealing with optimization problems that have been shown to be intractable using conventional problem solving techniques.In the past, some researchers have focused on using hybrid genetic algorithm approaches for optimization problems. Buczak and Uhrig [1] proposed a novel hierarchical fuzzy-genetic information fusion technique. The combined reasoning takes place by means of fuzzy aggregation functions, capable of combining information by compensatory connectives that better mimic the human reasoning process than union and intersection, employed in traditional set theories. The parameters of the connectives are found by genetic algorithms. evaluated the use of different methods from the fuzzy modeling field for classification tasks and the potential of their integration in producing better classification results. The methods considered, approximate in nature, study the integration of techniques with an initial rule generation step and a following rule tuning approach using different evolutionary algorithms.

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“…On the other hand, they can be regarded as flexible mathematical structures, capable to perform nonlinear mappings between input and output data. Besides, FISs have been widely used to achieve classification tasks [10], given that a fuzzy classifier is only a FIS with crisp and discrete outputs. Rules in a FIS are usually obtained from expert knowledge.…”

Section: Introductionmentioning

confidence: 99%

Neuro-fuzzy Based Maneuver Detection for Collision Avoidance in Road Vehicles

Zamora-Izquierdo

Toledo-Moreo

Valdés-Vela

et al. 2007

Lecture Notes in Computer Science

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Abstract. The issue of collision avoidance in road vehicles has been investigated from many different points of view. An interesting approach for Road Vehicle Collision Assistance Support Systems (RVCASS) is based on the creation of a scene of the vehicles involved in a potentially conflictive traffic situation. This paper proposes a neuro-fuzzy approach for dynamic classification of the vehicles roles in a scene. For that purpose, different maneuver state models for longitudinal movements of road vehicles have been defined, and a prototype has been equipped with INS (Inertial Navigation Systems) and GPS (Global Positioning System) sensors. Trials with real data show the suitability of the proposed neurofuzzy approach for solving support to the problem under consideration.

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Approximative fuzzy rules approaches for classification with hybrid-GA techniques

Cited by 32 publications

References 22 publications

Improving interpretability in approximative fuzzy models via multiobjective evolutionary algorithms

Improving interpretability in approximative fuzzy models via multiobjective evolutionary algorithms

A Hybrid Genetic Algorithm and Bacterial Foraging Approach for Global Optimization and Robust Tuning of PID Controller with Disturbance Rejection

Neuro-fuzzy Based Maneuver Detection for Collision Avoidance in Road Vehicles

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