Fuzzy Clustering Neural Networks for Real-Time Odor Recognition System

Karlık, Bekir; Yüksek, Kemal

doi:10.1155/2007/38405

Cited by 9 publications

(5 citation statements)

References 16 publications

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“…Fuzzy clustering-based cascade classifier is a hybrid learning algorithm which integrates both unsupervised Fuzzy c-mean clustering and supervised classifiers such as ANN, SVM, and Naïve Bayes. This method is defined and used by [8]- [10]. As seen in Figure 3.1, the structure of the proposed Fuzzy clustering-based cascade classifier which consists of two phases.…”

Section: Fuzzy Clustering -Based Cascade Classifiermentioning

confidence: 99%

Healthy and Asthmatic Sounds Classification using Fuzzy Clustering based Cascade Classifier Methods

Harman¹

2018

IJCA

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In this study, our aim is to presents different cascade classifier models based on Fuzzy C-means clustering methods, to classified normal and asthmatic lung sounds. The lung sounds recorded from both left and right lobe of the lung and divided inspiration and expiration phases. The frequency spectrum is obtained by using Fast Fourier Transform (FFT). After obtaining the spectrums of healthy and asthmatic lung sounds, fuzzy c-mean is applied to cluster the spectrum and then supervised classifier ANN, Naive Bayes and SVM are applied to classified healthy and asthmatic lung sounds.

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Section: Fuzzy Clustering -Based Cascade Classifiermentioning

confidence: 99%

Healthy and Asthmatic Sounds Classification using Fuzzy Clustering based Cascade Classifier Methods

Harman¹

2018

IJCA

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“…A metal-oxide semiconductor gas or odor sensor, i.e., OMX-GR, is engaged as an odor detecting element. Sensitivities of OMX-GR sensors are explained by two factors, strength and classification [ 9 ]. This provides a lot of benefits for such applications regarding odor detection and measurement.…”

Section: Odor Recognition Using Cmac Neural Networkmentioning

confidence: 99%

“…Each neuron of a layer is full connected to all the neurons of the following layer (feed-forward neural network). These connections are directed (from the input to the output layer) and have weights assigned to them [ 9 ]. For comparing with CMAC, a MLP trained with the back-propagation algorithm was used for recognition of collecting data of hazardous odors.…”

Section: Odor Recognition Using Cmac Neural Networkmentioning

confidence: 99%

Hazardous Odor Recognition by CMAC Based Neural Networks

Bucak

Karlık

2009

Sensors

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Electronic noses are being developed as systems for the automated detection and classification of odors, vapors, and gases. Artificial neural networks (ANNs) have been used to analyze complex data and to recognize patterns, and have shown promising results in recognition of volatile compounds and odors in electronic nose applications. When an ANN is combined with a sensor array, the number of detectable chemicals is generally greater than the number of unique sensor types. The odor sensing system should be extended to new areas since its standard style where the output pattern from multiple sensors with partially overlapped specificity is recognized by a neural network or multivariate analysis. This paper describes the design, implementation and performance evaluations of the application developed for hazardous odor recognition using Cerebellar Model Articulation Controller (CMAC) based neural networks.

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“…Xu et al [27] proposed a WLAN hybrid indoor positioning method based on FCM and ANN, which reduces the positioning error while ensuring ef ciency. Karlik et al [28] proposed a new fuzzy clustering neural network (FCNN) algorithm as a pattern classi er for real-time odor recognition systems. The FCNN algorithm uses FCM clustering to reduce the number of data points before inputting to the neural network system, thereby shortening the training cycle of the neural network.…”

Section: Introductionmentioning

confidence: 99%

Application of FCMAlgorithm Combined with Artcial Neural Network in TBM Operation Data

Fang¹,

Song²,

Yao³

et al. 2021

Computer Modeling in Engineering &Amp; Sciences

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Fuzzy clustering theory is widely used in data mining of full-face tunnel boring machine. However, the traditional fuzzy clustering algorithm based on objective function is di cult to e ectively cluster functional data. We propose a new Fuzzy clustering algorithm, namely FCM-ANN algorithm. The algorithm replaces the clustering prototype of the FCM algorithm with the predicted value of the arti cial neural network. This makes the algorithm not only satisfy the clustering based on the traditional similarity criterion, but also can e ectively cluster the functional data. In this paper, we rst use the t-test as an evaluation index and apply the FCM-ANN algorithm to the synthetic datasets for validity testing. Then the algorithm is applied to TBM operation data and combined with the crossvalidation method to predict the tunneling speed. The predicted results are evaluated by RMSE and R 2 . According to the experimental results on the synthetic datasets, we obtain the relationship among the membership threshold, the number of samples, the number of attributes and the noise. Accordingly, the datasets can be e ectively adjusted. Applying the FCM-ANN algorithm to the TBM operation data can accurately predict the tunneling speed. The FCM-ANN algorithm has improved the traditional fuzzy clustering algorithm, which can be used not only for the prediction of tunneling speed of TBM but also for clustering or prediction of other functional data.

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Fuzzy Clustering Neural Networks for Real-Time Odor Recognition System

Cited by 9 publications

References 16 publications

Healthy and Asthmatic Sounds Classification using Fuzzy Clustering based Cascade Classifier Methods

Healthy and Asthmatic Sounds Classification using Fuzzy Clustering based Cascade Classifier Methods

Hazardous Odor Recognition by CMAC Based Neural Networks

Application of FCMAlgorithm Combined with Artcial Neural Network in TBM Operation Data

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