A novel application of a neuro–fuzzy computational technique in modeling of thermal cracking of heavy feedstock to light olefin

RSC Advances ◽  
2014 ◽  
Vol 4 (54) ◽  
pp. 28390 ◽  
Author(s):  
Mehdi Sedighi ◽  
Mostafa Ghasemi ◽  
Majid Mohammadi ◽  
Sedky H. A. Hassan
Keyword(s):  
Thermal Cracking ◽  
Computational Technique ◽  
Light Olefin ◽  
Heavy Feedstock ◽  
Neuro Fuzzy

scholarly journals Investigating the Use of an Adaptive Neuro-Fuzzy Inference System in Software Development Effort Estimation

2021 ◽  
pp. 18-24
Author(s):  
Zainab Rustum Mohsin
Keyword(s):  
Software Development ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Computational Technique ◽  
Development Effort ◽  
Effort Estimation ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Software Development Effort ◽  
Software Development Effort Estimation

Modeling software development effort estimation models has been a hot research topic over the last three decades. Numerous models were proposed in these decades to predict the effort. The key challenges for future software development is providing accurate software estimation. Failure to acknowledge the accuracy of effort estimation can cause inaccurate estimation, customer disappointment, and poor software development or project failure. This research presents a novel computational technique, named adaptive neuro-fuzzy inference system (ANFIS), for the modeling of software effort estimation. It was developed utilizing the Constructive Cost Model (COCOMO) dataset. The data were randomly divided into two sets: 83% for training and 17% for testing. The mean magnitude relative-error (MMRE) and the coefficient of correlation (R) were used as assessment indices. Results showed that the accuracy of the proposed model is quite satisfactory in comparison with actual values. Moreover, a comparison study was conducted with another approach. The results showed that ANFIS produced better results in comparison with Function Point Analysis, Software Lifecycle Management, and COCOMO methods. ANFIS was found to be a potential predictive model for software development effort estimation.

scholarly journals A neuro-fuzzy model to predict the inflow to the guardialfiera multipurpose dam (Southern Italy) at medium-long time scales

2013 ◽  
Vol 44 (2s) ◽  
Author(s):  
L.F. Termite ◽  
F. Todisco ◽  
L. Vergni ◽  
F. Mannocchi
Keyword(s):  
Time Scales ◽  
Time Scale ◽  
Input Data ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Computational Technique ◽  
Inference System ◽  
The Past ◽  
Neuro Fuzzy ◽  
Multipurpose Dam

Intelligent computing tools based on fuzzy logic and artificial neural networks have been successfully applied in various problems with superior performances. A new approach of combining these two powerful tools, known as neuro-fuzzy systems, has increasingly attracted scientists in different fields. Few studies have been undertaken to evaluate their performances in hydrologic modeling. Specifically are available rainfall-runoff modeling typically at very short time scales (hourly, daily or event for the real-time forecasting of floods) with in input precipitation and past runoff (i.e. inflow rate) and in few cases models for the prediction of the monthly inflows to a dam using the past inflows as input. This study presents an application of an Adaptive Network-based Fuzzy Inference System (ANFIS), as a neuro-fuzzy-computational technique, in the forecasting of the inflow to the Guardialfiera multipurpose dam (CB, Italy) at the weekly and monthly time scale. The latter has been performed both directly at monthly scale (monthly input data) and iterating the weekly model. Twenty-nine years of rainfall, temperature, water level in the reservoir and releases to the different uses were available. In all simulations meteorological input data were used and in some cases also the past inflows. The performance of the defined ANFIS models were established by different efficiency and correlation indices. The results at the weekly time scale can be considered good, with a Nash- Sutcliffe efficiency index E = 0.724 in the testing phase. At the monthly time scale, satisfactory results were obtained with the iteration of the weekly model for the prediction of the incoming volume up to 3 weeks ahead (E = 0.574), while the direct simulation of monthly inflows gave barely satisfactory results (E = 0.502). The greatest difficulties encountered in the analysis were related to the reliability of the available data. The results of this study demonstrate the promising potential of ANFIS in the forecasting of the short term inflows to a reservoir and in the simulation of different scenarios for the water resources management in the longer term.

Computational Technique for the Qualification of Focal Spot Size and Shape of Industrial Radioscopy Equipment Using Star Patterns

2020 ◽  
Vol 78 (4) ◽  
pp. 479-486
Author(s):  
Marcela Tatiana Fernandes Beserra ◽  
◽  
Ricardo Tadeu Lopes ◽  
Davi Ferreira de Oliveira ◽  
Claudio Carvalho Conti ◽  
...  
Keyword(s):  
Focal Spot ◽  
Spot Size ◽  
Computational Technique ◽  
Focal Spot Size ◽  
Size And Shape

THE SETTINGS ALGORITHM OF A NEURO-FUZZY PI-CONTROLLER

2020 ◽  
Vol 12 (2) ◽  
Author(s):  
Atamurat Mambetov ◽  
Rasul Beglerbekov ◽  
Hurliman Sultanova
Keyword(s):  
Pi Controller ◽  
Neuro Fuzzy ◽  
Fuzzy Pi Controller

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

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