Advances in fuzzy integration for pattern recognition

Keller, James M.; Gader, Paul D.; Tahani, Hossein; Chiang, Jung-Hsien; Mohamed, Magdi

doi:10.1016/0165-0114(94)90024-8

Cited by 133 publications

(51 citation statements)

References 7 publications

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“…The fuzzy integral, a non-linear fusion operator, which was first introduced with this name by Sugeno, [3], has showed its suitability for the integration of information. The use of the fuzzy integral in problems Multicriteria Decision Making [4], and Pattern Recognition [5], supports this affirmation. The favorable properties of the fuzzy integral regarding the qualitative aspect mentioned in the introduction are achieved through the fuzzy measures.…”

Section: Operationmentioning

confidence: 70%

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Lower motor control modeled by neuron with fuzzy synapses

Bigan

Strungaru

Ungureanu

et al. 2001

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-The paper presents a model of information flow through the sensors to muscle system in regard to the control of output. The model takes into account both the logical and emotional action components. The emotional components depend on the current needs of the organism. Actions resulting in a positive change in the emotional analysis of the world activate the reward component that enables the action of logical analysis of the situation. The principal component of the system, which provides input to most muscle fibers of human body, is called lower motor neuron (LMN). The control of LMN is modeled via a set of fuzzy rules. The principles of processing seem applicable to artificial systems. Keywords -Lower motor control, fuzzy control, fuzzy synapses I. INTRODUCTIONNatural communication modeling -information coding and transmission in human body is useful for technical implementations as well in data communications as in other related fields as robotics. II. MUSCULAR CONTROL BY LOWER MOTOR NEURONSThe discharge activity of a motor unit is regulated by a complex interaction between excitatory and inhibitory inputs to the motor neuron. A major portion of the input comes from supraspinal motor centers, directly or through interneurons. Consequently, disorders of these centers can alter the motor unit discharge pattern as seen in parkinsonism, chorea, cerebellar disorders, and spasticity. In most cases, muscles work in opposing pairs: one muscle opens or extends a joint and the other closes or flexes it. This configuration is necessitated by the fact that muscles exert force in one direction only (i.e., contraction). Figure 1 demonstrates this arrangement for a typical joint. This diagram also shows some of the neural elements, which control the contraction of these muscles. The principal neuron of this system, which provides input to most muscle fibers, is called a lower motor neuron and is labeled L in figure 1. This type of neuron and the other neurons associated with it are located in the spinal cord, where they function as the final processing stage before output to the muscle. We shall refer to the lower motor neuron and its associated elements as a LMN system. This system is a good place to observe some of the principles of the brain's motor organization. There are a great many LMN systems in the spinal cord. Every muscle is composed of thousands to millions of fibers and in the case of muscles used for precise operations there may be an LMN system for each individual fiber. In other cases, a single LMN system may control many fibers of a muscle. Basically, an LMN system must accept and reconcile commands from a multitude of other systems, which desire control of the muscle in question. It must attend to these commands according to their priority, modify them on the basis of inputs from both the kinesthetic and vestibular systems and on the basis of status information from related LMN systems, provide an appropriate output to the muscle, and make its own status information available to other systems. In a...

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Section: Operationmentioning

confidence: 70%

“…There are basically two types of fuzzy integral, respectively known as Sugeno's integral (S µ ): (5) and Choquet's integral (C µ ):…”

Section: Operationmentioning

confidence: 99%

Lower motor control modeled by neuron with fuzzy synapses

Bigan

Strungaru

Ungureanu

et al. 2001

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…Where we interpret the classifier outputs as the support for the classes, fuzzy aggregation methods can be applied, such as simple connectives between fuzzy sets or the fuzzy integral [23,22,66,128]; if the classifier outputs are possibilistic, Dempster-Schafer combination rules can be applied [108]. Statistical methods and similarity measures to estimate classifier correlation have also been used to evaluate expert system combination for a proper design of multi-expert systems [58].…”

Section: Non-generative Ensemblesmentioning

confidence: 99%

Ensembles of Learning Machines

2002

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Abstract. Ensembles of learning machines constitute one of the main current directions in machine learning research, and have been applied to a wide range of real problems. Despite of the absence of an unified theory on ensembles, there are many theoretical reasons for combining multiple learners, and an empirical evidence of the effectiveness of this approach. In this paper we present a brief overview of ensemble methods, explaining the main reasons why they are able to outperform any single classifier within the ensemble, and proposing a taxonomy based on the main ways base classifiers can be generated or combined together.

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“…Out of all fuzzy integrals [40][41][42][43][44], the Choquet fuzzy integral [39,41,45] and Sugeno integral [46] are widely used. A handful of applications of fuzzy integrals include: handwriting recognition [47], landmine detection [48], pattern recognition [49], and decision making [37]. In the present study, the Choquet fuzzy integral is favored for it employs the nonadditive property of fuzzy measures.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Fuzzy Measures Using Covariance Matrices in Gaussian Mixtures

Verma

2012

Applied Computational Intelligence and Soft Computing

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This paper presents a novel computational approach for estimating fuzzy measures directly from Gaussian mixtures model (GMM). The mixture components of GMM provide the membership functions for the input-output fuzzy sets. By treating consequent part as a function of fuzzy measures, we derived its coefficients from the covariance matrices found directly from GMM and the defuzzified output constructed from both the premise and consequent parts of the nonadditive fuzzy rules that takes the form of Choquet integral. The computational burden involved with the solution of λ-measure is minimized using Q-measure. The fuzzy model whose fuzzy measures were computed using covariance matrices found in GMM has been successfully applied on two benchmark problems and one real-time electric load data of Indian utility. The performance of the resulting model for many experimental studies including the above-mentioned application is found to be better and comparable to recent available fuzzy models. The main contribution of this paper is the estimation of fuzzy measures efficiently and directly from covariance matrices found in GMM, avoiding the computational burden greatly while learning them iteratively and solving polynomial equations of order of the number of input-output variables.

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Advances in fuzzy integration for pattern recognition

Cited by 133 publications

References 7 publications

Lower motor control modeled by neuron with fuzzy synapses

Lower motor control modeled by neuron with fuzzy synapses

Ensembles of Learning Machines

Estimation of Fuzzy Measures Using Covariance Matrices in Gaussian Mixtures

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