Adaptive fuzzy clustering and fuzzy prediction models

Ryoke, Mina; Nakamori, Yoshiteru; Suzuki, Kazuyuki

doi:10.1109/fuzzy.1995.409987

Cited by 12 publications

(5 citation statements)

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“…Fuzzy clustering analysis is a branch of fuzzy mathematics, and its range of applications involves time series prediction (Ryoke et al 1995), neural networks training (Karayiannies and Mi 1997), nonlinear system identification (Runkler et al 1996), parameter estimation (Gath and Geva 1989), medical diagnosis (Bezdek and Fordon 1979), weather forecast (Newton 1992), food classification (Windham 1985), and water quality analysis (Mukherjee 1995).…”

Section: Introductionmentioning

confidence: 99%

The classification in metamorphic rocks using modified fuzzy cluster analysis from geophysical log data: evidence from Chinese Continental Scientific Drilling Main Hole

Yang

Pan

Luo

et al. 2015

J Petrol Explor Prod Technol

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Lithology is one of the most important data in evaluating reservoir, and is mainly carried out by cores recovery in laboratory which is very expensive, and its interpretation is time consuming. Accurate identification of lithology is fundamentally crucial to evaluate reservoir from geophysical log data. Pattern recognition and statistical analysis have been proved to be the most powerful methods for constructing optimal model in lithology recognition. To address this issue, a fast and practical K-means clustering algorithm is proposed in order to better deal with lithology recognition from geophysical log data. Based on the traditional K-means clustering algorithm, Euclidean distance is replaced by Mahalanobis distance; the initial cluster centers are acquired from the average of characteristic values but not selected randomly, in addition, adding weight value in each characteristic value of the objective function, and thus a lithology recognition model named modified K-means clustering is established. The method is applied to identify the Chinese Continental Scientific Drilling Main Hole (CCSD-MH) metamorphic rocks. Compared with the traditional K-means clustering, the accurate rate of the modified K-means clustering in lithologic identification has improved for the same 45 samples, raised 11.11 %. According to the modified K-means cluster algorithm, nine kinds of lithology cluster centers are acquired from 45 samples. The classes of the samples can be determined by analyzing the hamming approach degree curves, which is calculated by the undetermined samples and 9 cluster centers. The predicted results and the core recovery are exactly the same by comparison. The hamming approach degree can identify the whole well of CCSD-MH lithology effectively and accurately. This model may be made applications to other areas.

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Section: Introductionmentioning

confidence: 99%

The classification in metamorphic rocks using modified fuzzy cluster analysis from geophysical log data: evidence from Chinese Continental Scientific Drilling Main Hole

Yang

Pan

Luo

et al. 2015

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

show abstract

“…Thus, if the iterative procedure applied in [114] is followed for this case, then equation (4.10) must be differentiated with regard to each of these independent variables. Derivatives with respect to the centers p i and variances σ 2 i for the unidimensional case result in:…”

Section: Iterative Index Optimizationmentioning

confidence: 99%

“…In many applications [114], [61] the procedure to find the minimum of the cost index is to randomly select an initial point and iteratively find the optimum value for the different unknown variables. The search is often divided into the different variables that have to be found and each is independently computed, keeping the others in the optimum value taken from the previous iteration.…”

Section: Iterative Index Optimizationmentioning

confidence: 99%

“…In some cases [114], the clusters are placed and then the parameters of the models are found by least squares taking into account the objects belonging to each cluster. In this way, some parameters (β vectors) are not optimized, but computed.…”

Section: Iterative Index Optimizationmentioning

confidence: 99%

“…The minimization of J T SP for m = 2 can be done in a similar way to [114]. The steps are the same as in AFCRC, but applied to this case:…”

mentioning

confidence: 99%

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Multimodel Approaches for Plasma Glucose Estimation in Continuous Glucose Monitoring. Development of New Calibration Algorithms

Rico¹

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AgraïmentsHan sigut quatre els anys dedicats a este treball i han passat moltes coses durant este temps. Moltes són les persones que han estat al meu costat en esta part del meu camí iés complicat anomenar a tot el món que d'alguna forma a contribuït a este treball. Igualment,és quasi impossible que puga transformar a paraules tot l'agraïment que sent i els dec.Sense Jorge i José Luis este treball no seria comés. En els inicis, em va eixir la possibilitat de treballar amb ells i vaig acceptar per estar en unàrea que m'agradaba i m'agrada, sense saber com resultaria. I la veritat, difícil seria que haguera eixit millor. Gràcies per la vostra dedicació a este treball i gràcies per la vostra consideració en tantes coses.També estan els meus companys de treball, els que ja s'han anat i els que queden. Sense ells no haguera sigut igual anar a treballar i no ho haguera fet amb les mateixes ganes. Gràcies pels cafés per començar el dia. Gràcies per tots els moments fora de la sala. Especialment a David que ha sigut el meu company de camí durant molts anys i a Laguna, que sempre ha estat disponible, sobre tot en els moments de major frustració.Els meus amics han sigut una peça fonamental per a desconectar. Gràcies per tots els moments. Braulio, gràcies per totes les converses profundes i trivials.A la meua familia (Aitor, Silvia, Sergio, Mari, Pedro, Sergio, Iaia, Abuela) li dec molt més que agraïments. Sempre han estat al meu costat i espere que sempre seguisquen estan. Gràcies per tot l'ànim que m'heu donat. Gràcies per ser com sou.Carol, muchas gracias por ayudarme a ver que este camino puede ser, y es, mucho más agradable y menos complicado de lo que yo imaginaba.A la meua germana li he d'agraïr una infinitat de coses, sobre tot el seu suport incondicional. Tata, ets un dels pilars que aguanta la meua estructura, ara i sempre. Gràcies per tot. A Arantxa li done les gràcies per aparèixer un dia i quedar-se. Gràcies a les dos.Finalment dir queés als meus pares als que dec en gran part estar on estic. Sempre heu cregut en el que he fet i en la importància d'este treball. Estes línies mai seran suficient per expressar tot el que vos agraïsc. Gràcies per totes les coses que heu fet per mi i que seguiu fent cada dia que m'han ajudat a estar ací. Gràcies.En definitiva, gràcies a cadascun de vosaltes. Gràcies per estar amb mi. GRÀCIES A TOTS. Al club de Fa AbstractDiabetes Mellitus (DM) embraces a group of metabolic diseases which main characteristic is the presence of high glucose levels in blood. It is one of the diseases with major social and health impact, both for its prevalence and also the consequences of the chronic complications that it implies. One of the research lines to improve the quality of life of people with diabetes is of technical focus. It involves several lines of research, including the development and improvement of devices to estimate "online" plasma glucose: continuous glucose monitoring systems (CGMS), both invasive and non-invasive. These devices estimate plasma glucose from sensor measurements fr...

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On Combining Clusterwise Linear Regression and K-Means with Automatic Weighting of the Explanatory Variables

Silva

Carvalho

2017

Artificial Neural Networks and Machine Learning – ICANN 2017

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Adaptive fuzzy clustering and fuzzy prediction models

Cited by 12 publications

References 6 publications

The classification in metamorphic rocks using modified fuzzy cluster analysis from geophysical log data: evidence from Chinese Continental Scientific Drilling Main Hole

The classification in metamorphic rocks using modified fuzzy cluster analysis from geophysical log data: evidence from Chinese Continental Scientific Drilling Main Hole

Multimodel Approaches for Plasma Glucose Estimation in Continuous Glucose Monitoring. Development of New Calibration Algorithms

On Combining Clusterwise Linear Regression and K-Means with Automatic Weighting of the Explanatory Variables

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