Fuzzy lattice neural network (FLNN): a hybrid model for learning

Petridis, V.; Kaburlasos, Vassilis G.

doi:10.1109/72.712161

Cited by 93 publications

(44 citation statements)

References 33 publications

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“…For the SLMP model we used the same design process and parameters definition suggested by [36]. For the FLNN model we used the same design process and parameters definition suggested by [9,13]. For the FLR model we used the same design process and parameters definition suggested by [9,37].…”

Section: Simulations and Experimental Resultsmentioning

confidence: 99%

“…In order to establish a fair performance comparison, results with the following classification models were examined in the same context and under the same experimental conditions: multilayer perceptrons (MLP) [1,2], morphological-rank-linear neural network (MRLNN) [35], morphological perceptron with competitive learning (MP/CL) [9], single layer morphological perceptron (SLMP) [36], fuzzy lattice neural network (FLNN) [13], fuzzy lattice reasoning (FLR) [37], k-nearest neighbors (KNN) [38], decision tree (DT) [39,40], support vector machine (SVM) [2] and dilation-erosion-linear perceptron with gradient-based learning, that is, the DELP(BP) [27].…”

Section: Simulations and Experimental Resultsmentioning

confidence: 99%

“…It is inspired by the concept of biological neurons and it is able to solve linear classification problems [1,2]. There is a particular class of artificial neurons based on the framework of mathematical morphology (MM) [3,4] and lattice theory [5][6][7], called morphological perceptrons (MPs) [8,9], which have been successfully applied as solution of linear and nonlinear problems [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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A Swarm-based Evolutionary Morphological Approach for Binary Classification Problems

Araújo¹,

Oliveira²,

Meira³

2013

L&NLM

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-In this work we propose a swarm-based evolutionary morphological approach to deal with binary classification problems. It consists of a hybrid neuron based on principles of mathematical morphology and lattice theory, referred to as dilation-erosion-linear perceptron (DELP). We also present a swarm-based evolutionary learning process, called DELP(PSO), using a particle swarm optimizer (PSO) to design the DELP model, due to some drawbacks from gradient estimation of morphological operators in the classical learning process of the DELP. Besides, we conduct an experimental analysis using two relevant binary classification problems and the obtained results are discussed and compared with those obtained by established techniques in the literature.

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Section: Simulations and Experimental Resultsmentioning

confidence: 99%

Section: Simulations and Experimental Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Swarm-based Evolutionary Morphological Approach for Binary Classification Problems

Araújo¹,

Oliveira²,

Meira³

2013

L&NLM

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“…He generalized Zadeh's definition by using a popular concept of fuzzy inclusion which is a fuzzy analog of conditional probability: jA^Bj=jAj. Variation of this measure and its applications appeared in [9,15,22,34]. Zadeh [52], Zhang et al [56] and Zhang and Qiu [58] gave the knowledge processing method for intelligent systems based on inclusion measures and rough sets.…”

Section: Preliminariesmentioning

confidence: 99%

On inclusion measures of intuitionistic and interval-valued intuitionistic fuzzy values and their applications to group decision making

Zhang

Yang

Yue

2015

Int. J. Mach. Learn. & Cyber.

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Ranking intuitionistic fuzzy values (IFVs) and interval-valued intuitionistic fuzzy values (IVIFVs) is an important and necessary work in intuitionistic fuzzy group decision making. Since the set of all IFVs is a poset and inclusion measure indicates the degree to which a given element of a poset is contained in another one. This paper studies hybrid monotonic (HM) inclusion measures of IFVs and IVIFVs respectively and discuss their applications to group decision making. Firstly, HM inclusion measure is defined on the posets of all IFVs and IVIFVs respectively. Then HM inclusion measures are studied by constructive approach. Furthermore, the HM inclusion measures are employed to make intuitionistic and interval-valued intuitionistic fuzzy group decisions. Lastly, practical examples are provided to illustrate the developed approaches respectively.

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“…There are many other approaches to achieve invariant recognition by neural networks, for example proposed in [10], [11], [12], [13], [14], [15], [16]. But each of them is either too complex or specialized for determined any kind of images and transformations.…”

Section: Introductionmentioning

confidence: 99%

An Approach for Invariant Clustering and Recognition in Dynamic Environment

Gavrilov

Lee

Advances and Innovations in Systems, Computing Sciences and Software Engineering

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-An approach for invariant clustering and recognition of objects (situation) in dynamic environment is proposed. This approach is based on the combination of clustering by using unsupervised neural network (in particular ART-2) and preprocessing of sensor information by using forward multilayer perceptron (MLP) with error back propagation (EBP) which supervised by clustering neural network. Using MLP with EBP allows to recognize a pattern with relatively small transformations (shift, rotation, scaling) as a known previous cluster and to reduce producing large number of clusters in dynamic environment, e.g. during movement of robot or recognition of novelty in security system.

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Fuzzy lattice neural network (FLNN): a hybrid model for learning

Cited by 93 publications

References 33 publications

A Swarm-based Evolutionary Morphological Approach for Binary Classification Problems

A Swarm-based Evolutionary Morphological Approach for Binary Classification Problems

On inclusion measures of intuitionistic and interval-valued intuitionistic fuzzy values and their applications to group decision making

An Approach for Invariant Clustering and Recognition in Dynamic Environment

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