Method and software for extracting fuzzy classification rules by subtractive clustering

Chiu, S.

doi:10.1109/nafips.1996.534778

Cited by 85 publications

(54 citation statements)

References 9 publications

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“…This model is composed of significant features expedient for fast computation time [25]. An in-depth discussion regarding this algorithm can be procured from [48].…”

Section: Anfis Modelmentioning

confidence: 99%

The Amalgamation of SVR and ANFIS Models with Synchronized Phasor Measurements for On-Line Voltage Stability Assessment

et al. 2017

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This paper presents the application of support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS) models that are amalgamated with synchronized phasor measurements for on-line voltage stability assessment. As the performance of SVR model extremely depends on the good selection of its parameters, the recently developed ant lion optimizer (ALO) is adapted to seek for the SVR's optimal parameters. In particular, the input vector of ALO-SVR and ANFIS soft computing models is provided in the form of voltage magnitudes provided by the phasor measurement units (PMUs). In order to investigate the effectiveness of ALO-SVR and ANFIS models towards performing the on-line voltage stability assessment, in-depth analyses on the results have been carried out on the IEEE 30-bus and IEEE 118-bus test systems considering different topologies and operating conditions. Two statistical performance criteria of root mean square error (RMSE) and correlation coefficient (R) were considered as metrics to further assess both of the modeling performances in contrast with the power flow equations. The results have demonstrated that the ALO-SVR model is able to predict the voltage stability margin with greater accuracy compared to the ANFIS model.

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“…This model is composed of significant features expedient for fast computation time [25]. An in-depth discussion regarding this algorithm can be procured from [48].…”

Section: Anfis Modelmentioning

confidence: 99%

The Amalgamation of SVR and ANFIS Models with Synchronized Phasor Measurements for On-Line Voltage Stability Assessment

et al. 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%

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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“…2. Subtractive Clustering: Subtractive clustering [8] per subset identifies the number of rules and the membership functions of each rule's antecedent without having to declare how many clusters there are. 3.…”

Section: Data Separationmentioning

confidence: 99%

Increased Robustness in Context Detection and Reasoning Using Uncertainty Measures: Concept and Application

Berchtold

Beigl

2009

Lecture Notes in Computer Science

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Abstract. This paper reports on a novel recurrent fuzzy classification method for robust detection of context activities in an environment using either single or distributed sensors. It also introduces a classification of system architectures for uncertainty calculation in general. Our proposed novel method utilizes uncertainty measures for improvement of detection, fusion and aggregation of context knowledge. Uncertainty measurement calculations are based on our novel recurrent fuzzy system. We applied the method in a real application to recognize various applause (and non applause) situations, e.g. during a conference. Measurements were taken from mobile phone sensors (microphone, accel. if available) and acceleration sensory attached to a board marker. We show that we are able to improve robustness of detection using our novel recurrent fuzzy classifier in combination with uncertainty measures by ∼30% on average. We also show that the use of multiple phones and distributed recognition in most cases allows to achieve a recognition rate between 90% and 100%.

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Method and software for extracting fuzzy classification rules by subtractive clustering

Cited by 85 publications

References 9 publications

The Amalgamation of SVR and ANFIS Models with Synchronized Phasor Measurements for On-Line Voltage Stability Assessment

The Amalgamation of SVR and ANFIS Models with Synchronized Phasor Measurements for On-Line Voltage Stability Assessment

Spike discharge prediction based on neuro-fuzzy system

Increased Robustness in Context Detection and Reasoning Using Uncertainty Measures: Concept and Application

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