Fuzzy-neuro LVQ and its comparison with fuzzy algorithm LVQ in artificial odor discrimination system

Kusumoputro, Benyamin; Budiarto, Hary; Jatmiko, Wisnu

doi:10.1016/s0019-0578(07)60097-4

Cited by 28 publications

(20 citation statements)

References 12 publications

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“…Researchers proposed the combination of LVQ with other prototype-based learning schemes like SOM or Neural Gas to include neighborhood cooperation into the learning process [12][13][14][15][16]. Also, some techniques to realize fuzzy classification based on the general LVQ approach were proposed the last years [17], [18]. Recent research in vector quantization learning algorithm area are [15], [19], it is show that this learning area is interesting.…”

Section: Generalized Learning Vector Quantization (Amglvq) Amglvq Ismentioning

confidence: 99%

Electrocardiogram for Biometrics by using Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ): Integrating Feature Extraction and Classification

Imah

Jatmiko

Basaruddin

2013

International Journal on Smart Sensing and Intelligent Systems

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Section: Generalized Learning Vector Quantization (Amglvq) Amglvq Ismentioning

confidence: 99%

Electrocardiogram for Biometrics by using Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ): Integrating Feature Extraction and Classification

Imah

Jatmiko

Basaruddin

2013

International Journal on Smart Sensing and Intelligent Systems

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“…FLVQ-PSO dikembangkan pertama kali oleh Benyamin Kusumoputro dan rekan-rekan di dalam [11]. Konsep FLVQ yang merupakan bentuk modifikasi dari algoritma LVQ masih dianggap belum cukup karena FLVQ memiliki ketergantungan yang besar terhadap inisialisasi vektor awal, jika tidak sesuai maka nilai akurasi klasifikasi akan jauh dari harapan.…”

Section: Zaman Dkk Implementasi Sistem Klasifikasi Mobil Pada Sistunclassified

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Ramadhan¹,

Fajar²,

Suryana³

et al. 2012

Jurnal Ilmu Komputer dan Informasi

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AbstrakSistem Pengaturan Lampu Lalu Lintas Terdistribusi adalah sebuah sistem lampu lalu lintas yang ditujukan untuk memenuhi kebutuhan akan kinerja pengaturan lampu lalu lintas yang cerdas dengan pengambilan data secara real-time. Sistem ini dapat melakukan penjadwalan dan pengaturan jaringan banyakpersimpangan secarareal-time yang tidak bisa dilakukan oleh sistem pengaturan lampu lalu lintas konvensional. Penerapan klasifikasi di dalam sistem ini digunakan untuk meningkatkan akurasi dari pengenalan mobil. Proses klasifikasi diimplementasikan menggunakan tiga algoritma Jaringan Syaraf Tiruan, yakni Backpropagation, FLVQ, dan FLVQ-PSO. Berdasarkan hasil ujicoba, dapat ditunjukkan bahwa algoritma Backpropagationmemiliki performa akurasi yang lebih baik dibandingkan dua algoritma JST yang lainnya. Kata Kunci: backpropagation, FLVQ, FLVQ-PSO,jaringan syaraf tiruan,lampu lalu lintas AbstractDistributed Traffic Light Control System is a traffic light system intended to meet the need for setting the performance of intelligent traffic lights with real-time data capturing. The system can perform scheduling and network settings of multi-junction in real time that can not be done by a conventional traffic light settings system. Application of classification within this system is used to improve the accuracy of the car recognition. Classification process is implemented using three neural network algorithms, namely Backpropagation, FLVQ, and FLVQ-PSO. Based on the test results, it can be shown that the Backpropagation algorithm performs better accuracy than the other two algorithms.

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Section: Fuzzy-neuro Learning Vector Quantization With Matrix Simentioning

confidence: 99%

“…It has been proved that the LVQ together with fuzzy theory shows high recognition capability compared with other NNs [9], [13]. Other features from FNLVQ can also be used for recognizing the unknown fragrance [13]. Matrix similarity analysis (MSA) is then proposed to increase the accuracy of the FNLVQ, became FNLVQ-MSA neural system in determining the best exemplar vector, for speeding up its convergence [15].…”

Section: Fuzzy-neuro Learning Vector Quantization With Matrix Simentioning

confidence: 99%

“…As a matter of fact, in recent years, many researchers in artificial discrimination system give more attention to solve the odor mixture problem [10]- [12]. This system also had disadvantages, such as the unknown category of odor would be classified as the known category of odor [8], [13], [14]. In order to improve the performance of the proposed system, development of the sensor and other neural networks (NNs) are being sought.…”

Section: Introductionmentioning

confidence: 99%

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Artificial odor discrimination system using multiple quartz resonator sensors and various neural networks for recognizing fragrance mixtures

et al. 2006

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An electronic odor discrimination system had been developed by using four quartz resonator-sensitive membranes basic-resonance frequencies at 10 MHz as a sensor and analyzed the measurement data through a back propagation (BP) as the pattern recognition system. The developed system showed high recognition probability to discriminate various single odors to its high generality properties; however, the system had a limitation in recognizing the fragrances mixture. This system also had other disadvantages, such as classifying the unknown category of odor as the known category of odor. In order to improve the performance of the proposed system, development of the sensor and other neural networks (NNs) are being sought. This paper explains the improvement of the capability of that system. In this experiment, the improvement is conducted not only by replacing the last hardware system from four quartz resonator-basic resonance frequencies at 10 MHz with new 16 quartz resonator-basic resonance frequencies at 20 MHz, but also by replacing the pattern classifier from BP NNs with the variance of BP, probabilistic NNs, and fuzzy-neuro learning vector quantization (FNLVQ). Matrix similarity analysis (MSA) is then proposed to increase the accuracy of the FNLVQ, to become FNLVQ-MSA neural systems in determining the best exemplar vector, for speeding up its convergence. The purpose of the recent study is to construct a new artificial odor discrimination system for recognizing the fragrance mixtures, in addition to recognizing the unknown fragrance mixtures. The use of new sensing systems and FNLVQ-MSA has produced higher capability, compared to the previously mentioned system.Index Terms-Fuzzy-neuro learning vector quantization (FNLVQ), matrix similarity analysis (MSA), multiple quartz-resonator sensors, neural networks (NNs), odor discrimination system.

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Fuzzy-neuro LVQ and its comparison with fuzzy algorithm LVQ in artificial odor discrimination system

Cited by 28 publications

References 12 publications

Electrocardiogram for Biometrics by using Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ): Integrating Feature Extraction and Classification

Electrocardiogram for Biometrics by using Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ): Integrating Feature Extraction and Classification

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Artificial odor discrimination system using multiple quartz resonator sensors and various neural networks for recognizing fragrance mixtures

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