Survey on Neuro-Fuzzy systems and their applications in technical diagnostics and measurement

Viharos, Zsolt János; Kis, Krisztián Balázs

doi:10.1016/j.measurement.2015.02.001

Cited by 64 publications

(24 citation statements)

References 42 publications

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“…Kar et al [35] provided a recent survey on the NF classification model development during the period of 2002 to 2012 in different application fields such as traffic control, economic system, medical system, and image processing. Viharos and Kis [36] conducted a detailed survey on different NF models such as ANFIS, FALCON, GARIC, NEFCON, and SONFIN along with their architecture. It also gives the detailed survey of the use of these models in the technical diagnostics and measurement field.…”

Section: Literature Surveymentioning

confidence: 99%

A Hybrid Neuro-Fuzzy and Feature Reduction Model for Classification

Das

Naik

Behera

2020

Advances in Fuzzy Systems

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The evolvement of the fuzzy system has shown influential and successful in many universal approximation capabilities and applications. This paper proposes a hybrid Neuro-Fuzzy and Feature Reduction (NF-FR) model for data analysis. This proposed NF-FR model uses a feature-based class belongingness fuzzification process for all the patterns. During the fuzzification process, all the features are expanded based on the number of classes available in the dataset. It helps to deal with the uncertainty issues and assists the Artificial Neural Network- (ANN-) based model to achieve better performance. However, the complexity of the problem increases due to this expansion of input features in the fuzzification process. These expanded features may not always contribute significantly to the model. To overcome this problem, feature reduction (FR) is used to filter out the insignificant features, resulting the network less computational cost. These reduced significant features are used in the ANN-based model to classify the data. The effectiveness of this proposed model is tested and validated with ten benchmark datasets (both balanced and unbalanced) to demonstrate the performance of the proposed NF-FR model. The performance comparison of the NF-FR model with other counterparts has been carried out based on various performance measures such as classification accuracy, root means square error, precision, recall, and f-measure for quantitative analysis of the results. The obtained simulated results have been tested using the Friedman, Holm, and ANOVA tests under the null hypothesis for statistical validity and correctness proof of the results. The result analysis and statistical analysis show that the NF-FR model has achieved a considerable improvement in accuracy and is found to be efficient in eliminating redundant and noisy information.

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Section: Literature Surveymentioning

confidence: 99%

A Hybrid Neuro-Fuzzy and Feature Reduction Model for Classification

Das

Naik

Behera

2020

Advances in Fuzzy Systems

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“…Akan tetapi fungsi sebenarnya lebih kompleks lagi. Logika Fuzzy adalah sebuah logika yang menggunakan fuzzy set untuk memperlihatkan banyaknya nilai dan hasil yang menggambarkan kecerdasan manusia dalam bahasa matematika (Viharos et al,2015). Karena itu, pengalaman manusia bisa mengendalikannya dengan control value yang didapat dari banyaknya pengalaman dan ditunjukan dengan bahasa yang intuitif.…”

Section: Fuzzyunclassified

“…Kekurangan dari JST adalah fungsi JST yang hanya menghitung parameter output tanpa mengerti hubungan antara parameter input dan output sementara itu Logika fuzzy memilki ketergantungan terhadap pembuatnya. Selain itu fuzzy juga hanya menggunakan informasi yang dibuat di sistem dan tak bebas (Viharos et al,2015). Pengabungan dengan JST akan membuat sistem menjadi lebih baik.…”

Section: Neuro-fuzzyunclassified

Prediksi Stabilitas Lereng Menggunakan Adaptive Neuro-Fuzzy Metode Hybrid

Faisal¹,

Surjandari²,

Setiono³

2018

MATEKSI JTS UNS

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Permasalahan yang terjadi di alam, didominasi hampir 80% permasalahan non linier atau mungkin lebih. Permasalahan ini sering ditemui pada bencana alam seperti tanah longsor. Terkadang dalam pengaplikasian suatu metode, tidak sesuai dengan yang direncanakan. Faktor-faktor luar yang tidak diperhitungkan dalam perencanaan, menjadi penyumbang utama permasalahan. Berbagai metode pendekatan digunakan dalam menyelesaikan permasalahan tersebut. Penelitian ini akan menganalisis stabilitas lereng dengan menggunakan salah satu metode pendekatan dari Neuro-Fuzzy yaitu ANFIS.Sumber data yang digunakan merupakan lereng alami yang didapatkan dari beberapa penelitian sebelumnya. Penelitian ini menggunakan metode hybrid ANFIS untuk mendapatkan prediksi SF. Fine tuning dilakukan pada rule maupun membership function untuk mendapatkan nilai keluaran yang tepat. Digunakan sejumah data yaitu: 60 Data, 50 Data, 40 Data, 30 Data, 20 Data yang divariasikan dalam bentuk Ganjil, Genap, Kelipatan, SF, Beda Tinggi, Beda Kohesi. Pengecekan hasil menggunakan pengujian RMSE dan Matrix Confusion.Hasil penelitian memperlihatkan bahwa predkisi antara SF ANFIS dan SF asli dipengaruhi oleh membership function, rule, jumlah data, pengacakan data, hasil output serta nilai jarak dari input. Prediksi ANFIS paling optimal terjadi pada 20 Data Varian SF yang menghasilkan nilai Recall sebesar 1, Precision sebesar 1, Accuracy sebesar 1, serta Error Rate sebesar 0.

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“…It is based on applying the evolving fuzzy neural network (EFuNN) [28,29] paradigm, which allows for modeling the fuzzy logic engine by learning from the data. The evolving capability of the EFuNN paradigm addresses the problem of rule explosion.…”

Section: Evolutionary Re-engineeringmentioning

confidence: 99%

Applying evolutionary methods for early prediction of sleep onset

Malcangi

2015

Neural Comput & Applic

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Driving safety can be achieved by predicting imminent falling asleep at the wheel. Several methods of early detection have been investigated by continuous monitoring of physiological and behavioral parameters. Requirements for noninvasive, unattended, personal adaptation need to be met, along with the effectiveness of the detection method, in order to perform reliably when applied. Because wakefulness and sleep are reflected in several human physiological conditions, such as cardiac activity, breathing, movement, and galvanic skin conductance, captured bioelectric signal features were extracted. A fuzzy decision-fusion logic was tuned to make inferences about oncoming driver fatigue and drowsiness. The evolving fuzzy neural network paradigm was applied to the previous developed framework to improve reliability while keeping target system complexity low.

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Survey on Neuro-Fuzzy systems and their applications in technical diagnostics and measurement

Cited by 64 publications

References 42 publications

A Hybrid Neuro-Fuzzy and Feature Reduction Model for Classification

A Hybrid Neuro-Fuzzy and Feature Reduction Model for Classification

Prediksi Stabilitas Lereng Menggunakan Adaptive Neuro-Fuzzy Metode Hybrid

Applying evolutionary methods for early prediction of sleep onset

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