Fuzzy Clustering and Adaptive Smoothing Parameters
Adaptive Smoothing Method Based on Fuzzy Theory Study and Realization
... filter smoothing method breaks through the concept of target modeling, and overcomes the high sensi- tivity of the sampling by track ...other smoothing methods (such as fit- ting straight lines, moving ...
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Selecting Parameters of the Fuzzy Possibilistic Clustering Algorithm
... the fuzzy classification ...(original fuzzy c-means algorithm and its extensions) wherever the value of the fuzzy exponent is ...the fuzzy exponent is also different in FCM and typical ...
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Adaptive neuro-fuzzy model with fuzzy clustering for nonlinear prediction and control
... the fuzzy logic ...an adaptive neuro-fuzzy inference system (ANFIS) is used to capture the nonlinear connections between the air-fuel ratio and control parameters such manifold air pressure, ...
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Fuzzy clustering with volume prototypes and adaptive cluster merging
... (9) Note that the term can also be written as a modified (squared) distance . When (4) is minimized by iterating between (6) and (8), the volume prototypes extend a distance from the cluster centers and the points within ...
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Possibilistic Clustering Adaptive Smoothing Bilateral Filter Using Artificial Neural Network
... We are living in a natural environment where noise is inevitable and ubiquitous. Therefore, it is essential to use noise reduction techniques. Noise reduction techniques are a challenging problem due to some reasons. ...
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Study on Clustering Large Data Using Fuzzy Adaptive Resonance Theory
... — clustering is an approach that is used to form group of similar ...the clustering algorithms to separate the ...data. Fuzzy Adaptive Resonance Theory (ART) adopts unsupervised feature ...
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A Novel Fuzzy c -Means Clustering Algorithm Using Adaptive Norm
... suitable parameters for the upper bound of the tolerance vectors and regularization parameters remains a problem ...of fuzzy coef- ficients and limit the impact of outliers, the setting of empirical ...
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An adaptive fuzzy clustering algorithm with generalized entropy based on weighted sample
... at fuzzy clustering with generalized entropy, an adaptive fuzzy clustering algorithm with generalized entropy based on weighted sample is ...for fuzzy clustering with ...
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Adaptive Fuzzy Chaotic Genetic Clustering Based Continuous Keystroke Authentication
... in Fuzzy C-Means ...of clustering, the chaotic genetic clustering selects some of the members of the population for reproduction and it is accomplished by using cross over and ...
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Komparasi Metode Peramalan Automatic Clustering Technique and Fuzzy Logical Relationships Dengan Single Exponential Smoothing
... Automatic Clustering Technique and Fuzzy Logical Relationship ...konsep fuzzy logic yang digunakan untuk pemodelan data time ...exponential smoothing berdasarkan keakuratan ...
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Adaptive Basis Sampling for Smoothing Splines
... INTRODUCTION Smoothing splines provide flexible nonparametric regression ...features, smoothing splines stand out as a popular choice among nonparametric modeling methods (Ruppert et ...simple. ...
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Adaptive Smoothing Path Integral Control
... Both can be estimated by samples from the distribution p u θ . 4 The ASPIC Algorithm In this section, we derive an iterative algorithm that takes a parametrized control function u θ and performs smooth parameter updates ...
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Kernelized Fuzzy C-means Clustering with Adaptive Thresholding for Segmenting Liver Tumors
... with adaptive thresholding to detect tumor lesion of a liver ...by Fuzzy C- means and Kernelized fuzzy C-means with adoptive ...assigned fuzzy membership with a gray level intensity from ...
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A New Approach to Adaptive Neuro-fuzzy Modeling using Kernel based Clustering
... Data clustering is a well known technique for fuzzy model identification or fuzzy modelling for apprehending the system behavior in the form of fuzzy if-then rules based on experimental ...
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Robust Cell Detection Using Adaptive Fuzzy C Means Clustering and Classification
... using Fuzzy C means clustering (FCM) for accurate automatic Ki- 67 counting for NET and to localize both tumor and non- tumor ...non-fuzzy clustering algorithms, FCM is less sensitive to noise ...
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Automatic generation of fuzzy classification rules using granulation-based adaptive clustering
... of fuzzy modelling is the generation of fuzzy rules that fit the data to the highest possible ...of fuzzy rules from ...data clustering without the requirement of predefining any ...
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Adaptive sampling for QoS traffic parameters using fuzzy system and regression model
... The transmitted traffic were video streaming, VoIP, HTTP and FTP. The packet size for VoIP was 160 bytes. G711 protocol was used as audio coding with 64 kbps transmission rate. The packet size for video streaming was 512 ...
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Accelerated Fuzzy Clustering
... Compared with rseFCM, eFFCM takes a “double hit” in overhead from the sample selection process and from the increased sample size. Assuming that all other factors (parameters and randomization) are equal, the ...
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An Efficient Anomaly Detection using Fuzzy based Adaptive Neighbouring Splitting and Merging Clustering
... data clustering arises obviously in a lot of applications, and have regularly presented a great covenant with for usual data mining ...with Adaptive Neighboring Splitting and Merging (FKANSM)”, which takes ...
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Survey of fuzzy inference model and impact on QOS parameters using adaptive neuro fuzzy inference system in MANET
... proposed Fuzzy logic control method to improve the performance and reliability of the multicast routing protocols in ...nodes. Fuzzy logic is proposed to distinguish the strong and weak nodes in the ...the ...
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