A Proposal for the Genetic Lateral Tuning of Linguistic Fuzzy Systems and Its Interaction With Rule Selection

Alcalá, Rafael; Alcalá‐Fdez, Jesús; Herrera, F.

doi:10.1109/tfuzz.2006.889880

Cited by 170 publications

(150 citation statements)

References 60 publications

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“…The lateral displacement [57] is applied to tune the membership functions. This method allows for the lateral displacement of each membership function partition only by using one parameter.…”

Section: Hybrid Hierarchical Fuzzy Inference System (Hifs)mentioning

confidence: 99%

A Hybrid Fuzzy Inference System Based on Dispersion Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning

2017

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Abstract:Characterizing the spatial variation of traffic-related air pollution has been and is a long-standing challenge in quantitative environmental health impact assessment of urban transportation planning. Advanced approaches are required for modeling complex relationships among traffic, air pollution, and adverse health outcomes by considering uncertainties in the available data. A new hybrid fuzzy model is developed and implemented through hierarchical fuzzy inference system (HFIS). This model is integrated with a dispersion model in order to model the effect of transportation system on the PM 2.5 concentration. An improved health metric is developed as well based on a HFIS to model the impact of traffic-related PM 2.5 on health. Two solutions are applied to improve the performance of both the models: the topologies of HFISs are selected according to the problem and used variables, membership functions, and rule set are determined through learning in a simultaneous manner. The capabilities of this proposed approach is examined by assessing the impacts of three traffic scenarios involved in air pollution in the city of Isfahan, Iran, and the model accuracy compared to the results of available models from literature. The advantages here are modeling the spatial variation of PM 2.5 with high resolution, appropriate processing requirements, and considering the interaction between emissions and meteorological processes. These models are capable of using the available qualitative and uncertain data. These models are of appropriate accuracy, and can provide better understanding of the phenomena in addition to assess the impact of each parameter for the planners.

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Section: Hybrid Hierarchical Fuzzy Inference System (Hifs)mentioning

confidence: 99%

A Hybrid Fuzzy Inference System Based on Dispersion Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning

2017

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“…The different types of neuro-fuzzy systems used in this paper are as follow: ANFIS, WM, DENFIS, HyFIS [22] , genetic for lateral tuning and rule selection of linguistic fuzzy system (GFS.LT.RS) [23] , SBC [24,25] . Here we provide a short description each of them.…”

Section: Neuro-fuzzy Modelmentioning

confidence: 99%

“…GFS.LT.RS is proposed by Alcalá et al [23] that performs an evolutionary lateral tuning of membership functions in constructing FRBS model to obtain higher accurate linguistic models (Algorithm 5).…”

Section: Gfsltrsmentioning

confidence: 99%

Spike discharge prediction based on neuro-fuzzy system

Zarei¹

2017

JUMD

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How to cite this article: Zarei M. Spike discharge prediction based on neuro-fuzzy system. J Unexplored Med Data 2017;2:88-101. This paper presents the development and evaluation of different versions of neuro-fuzzy model for prediction of spike discharge patterns. The author aims to predict the spike discharge variation using first spike latency and frequency-following interval. In order to study the spike discharge dynamics, the author analyzed the cerebral cortex data of the cat. Adaptive neuro-fuzzy inference systems (ANFIS), Wang and Mendel, dynamic evolving neural-fuzzy inference system, hybrid neural fuzzy inference system, genetic for lateral tuning and rule selection of linguistic fuzzy system (GFS.LT.RS) and subtractive clustering and fuzzy c-means algorithms are applied for data. Among these algorithms, ANFIS and GFS.LT.RS models have better performance. On the other hand, ANFIS and GFS.LT.RS algorithms can be used to predict the spike discharge dynamics as a function of first spike latency and frequency with a higher accuracy compared to other algorithms. Key words:Spike discharge, spike latency, spike frequency, neuro-fuzzy system ABSTRACT Article history:

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“…Furthermore, this process of contextualizing the membership functions enables them to achieve a better covering degree while maintaining the original shapes, which results in accuracy improvements without a loss in the interpretability of the fuzzy labels. In the specialised literature, the 2-tuples representation has been used to tackle different problems as regression 4 or classification 1,2,40 .…”

Section: Lateral Tuningmentioning

confidence: 99%

Genetic lateral tuning for subgroup discovery with fuzzy rules using the algorithm NMEEF-SD

Carmona¹,

González²,

Gacto³

et al. 2012

IJCIS

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The main objective of subgroup discovery is to discover interesting and interpretable patterns with respect to a specific property. The use of evolutionary fuzzy systems provides good algorithms to approach this problem. In this sense, NMEEF-SD algorithm -one of the most representative evolutionary fuzzy systems for subgroup discovery-obtains precise and interpretable subgroups. However in the majority of the evolutionary fuzzy systems, the membership functions of the linguistic labels are usually fixed to static values and the partitions are not adapted to the context of each variable. In this paper, a post-processing tuning step to improve the results of the subgroup discovery algorithm NMEEF-SD is proposed, allowing the partitions to be adapted to the context the variables. The application of this tuning step is a novelty in subgroup discovery and consist of a genetic algorithm which allows the lateral displacement of the membership functions of a label considering a unique parameter, using the 2-tuples linguistic representation. The results obtained using different data sets of the KEEL repository show the improvement in the performance of the NMEEF-SD algorithm with lateral displacement. The study is supported by statistical tests to improve the analysis performed.

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A Proposal for the Genetic Lateral Tuning of Linguistic Fuzzy Systems and Its Interaction With Rule Selection

Cited by 170 publications

References 60 publications

A Hybrid Fuzzy Inference System Based on Dispersion Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning

A Hybrid Fuzzy Inference System Based on Dispersion Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning

Spike discharge prediction based on neuro-fuzzy system

Genetic lateral tuning for subgroup discovery with fuzzy rules using the algorithm NMEEF-SD

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