نتایج جستجو برای: anfis subtractive clustering method
تعداد نتایج: 1711021 فیلتر نتایج به سال:
Clustering of web user sessions is extremely significant to comprehend their surfing activities on the internet. Users with similar browsing behaviour are grouped together, and further analysis of discovered user groups by domain experts may generate usable and actionable knowledge. In this paper, a conglomerative clustering approach is presented to identify web user session clusters from web s...
Audio surveillance system in a public transport vehicle that detects event like screams and gunshots by classifying signals as normal or in crisis condition using adaptive neuro fuzzy inference system (ANFIS) is presented. Sample audio signals were edited to remove the silent part. Audio signals were divided into frames and represented by its feature. Twelve mel frequency cepstral coefficients ...
A method based on adaptive neuro-fuzzy inference system (ANFIS) for computing the effective permittivity and the characteristic impedance of the micro-coplanar strip (MCS) line is presented. The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy inference systems (FISs). A hybrid learning algorithm, which combines the least square method and the backpropagation alg...
In this work a new method based on the adaptive neuro-fuzzy inference system (ANFIS) was successfully introduced to determine the characteristic parameters, effective permittivities and characteristic impedances, of conventional coplanar waveguides. The ANFIS has the advantages of expert knowledge of fuzzy inference system and learning capability of neural networks. A hybrid-learning algorithm,...
Üst ekstremite hareketi tam olarak sağlanamadığında, yapay zeka (artificial intelligence/AI) sistemleri kullanıcılara amaçlanan hareketin uygulanması konusunda yardımcı olurlar. Kas aktivitesinin temsili olan elektromiyografi (EMG), sanal gerçeklik uygulamaları ve protez kontrolleri gibi AI-tabanlı sistemlerde kullanıldığında çeşitli faydalar sağlar. Bu çalışmada, bahsedilen sistemlere etkin ko...
In this paper, a subtractive clustering fuzzy identification method and a Sugeno-type fuzzy inference system are used to monitor tile defects in tile manufacturing process. The models for the tile defects are identified by using the firing mechanical resistance, water absorption, shrinkage, tile thickness, dry mechanical resistance and tiles temperature as input data, and using the concavity de...
this thesis is a study on insurance fraud in iran automobile insurance industry and explores the usage of expert linkage between un-supervised clustering and analytical hierarchy process(ahp), and renders the findings from applying these algorithms for automobile insurance claim fraud detection. the expert linkage determination objective function plan provides us with a way to determine whi...
We present an efficient method for extracting fuzzy classification rules from high dimensional data. A cluster estimation method called subtractive clustering is used to efficiently extract rules from a high dimensional feature space. A complementary search method can quickly identify the important input features from the resultant high dimensional fuzzy classifier, and thus provides the abilit...
In this study, a new approach based on adaptive neuro-fuzzy inference system (ANFIS) was presented for detection of electrocardiographic changes in patients with partial epilepsy. Decision making was performed in two stages: feature extraction using the wavelet transform (WT) and the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. Two...
An Algorithm to Model Paradigm Shifting in Fuzzy Clustering p. 70 ANFIS Synthesis by Hyperplane Clustering for Time Series Prediction p. 77 Generalized Splitting 2D Flexible Activation Function p. 85 Face Localization with Recursive Neural Networks p. 99 Multi-class Image Coding via EM-KLT Algorithm p. 106 A Face Detection System Based on Color and Support Vector Machines p. 113 A Neural Archit...
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