Backpropagation neural networks for modeling gasoline consumption

Nasr, G. E.; Badr, E. A.; Joun, C.

doi:10.1016/s0196-8904(02)00087-0

Cited by 82 publications

(53 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Correctly specified econometric demand models behave well for forecasting energy demand unless there is a fundamental shift in the structure of the economy. Models based on artificial neural networks have recently been used by Geem andRoper 2009 andNasr et. al.…”

Section: Bhattacharya and Timilsina (2009) Jebaraj And Iniyanmentioning

confidence: 99%

Modeling and forecasting natural gas demand in Bangladesh

et al. 2011

View full text Add to dashboard Cite

Natural gas is the major indigenous source of energy in Bangladesh and accounts for almost one-half of all primary energy used in the country. Per capita and total energy use in Bangladesh is still very small, and it is important to understand how energy, and natural gas, demand will evolve in the future. We develop a dynamic econometric model to understand the natural gas demand in Bangladesh, both in the national level, and also for a few sub-sectors. Our demand model shows large long run income elasticity around 1.5 for aggregate demand for natural gas. Forecasts into the future also show a larger demand in the future than predicted by various national and multilateral organizations. Even then, it is possible that our forecasts could still be at the lower end of the future energy demand. Price response was statistically not different from zero, indicating that prices are possibly too low and that there is a large suppressed demand for natural gas in the country.

show abstract

Section: Bhattacharya and Timilsina (2009) Jebaraj And Iniyanmentioning

confidence: 99%

Modeling and forecasting natural gas demand in Bangladesh

et al. 2011

View full text Add to dashboard Cite

show abstract

“…Cross entropy (CE) adalah fungsi error JST yang menjadi standar pilihan untuk penyelesaian masalah dalam bidang klasifikasi (Granitto et al 2002). Penggunaan CE sebagai fungsi error dapat mempercepat konvergensi (Yuan et al 2003) dan memberikan kinerja JST yang baik dibandingkan dengan fungsi kinerja mean square error (MSE) (Nasr et al 2002). Oleh karena itu, semakin kecil nilai CE semakin baik kinerja model JST tersebut dan semakin tinggi nilai akurasi menujukkan bahwa model JST memiliki kinerja yang baik dalam proses klasifikasi.…”

Section: Analisis Dataunclassified

The use of Vis-NIR for Detecting Huanglongbing Disease on Leaves of Citrus Orchard

Firmansyah¹,

Seminar

Widodo

2017

JTEP

View full text Add to dashboard Cite

Jurnal Keteknikan Pertanian (JTEP) terakreditasi berdasarkan SK Dirjen Penguatan Riset dan Pengembangan Kementerian Ristek Dikti Nomor I/E/KPT/2015 tanggal 21 September 2015. Selain itu, JTEP juga telah terdaftar pada Crossref dan telah memiliki Digital Object Identifier (DOI) dan telah terindeks pada ISJD, IPI, Google Scholar dan DOAJ. JTEP terbit tiga kali setahun yaitu pada bulan April, Agustus dan Desember berisi 12 naskah untuk setiap monuron baik dalam edisi cetak maupun edisi online. Jurnal berkala ilmiah ini berkiprah dalam pengembangan ilmu keteknikan untuk pertanian tropika dan lingkungan hayati. Penulis makalah tidak dibatasi pada anggota PERTETA tetapi terbuka bagi masyarakat umum. Lingkup makalah, antara lain: teknik sumberdaya lahan dan air, alat dan mesin budidaya pertanian, lingkungan dan bangunan pertanian, energi alternatif dan elektrifikasi, ergonomika dan elektronika pertanian, teknik pengolahan pangan dan hasil pertanian, manajemen dan sistem informasi pertanian. Makalah dikelompokkan dalam invited paper yang menyajikan isu aktual nasional dan internasional, review perkembangan penelitian, atau penerapan ilmu dan teknologi, technical paper hasil penelitian, penerapan, atau diseminasi, serta research methodology berkaitan pengembangan modul, metode, prosedur, program aplikasi, dan lain sebagainya. Penulisan naskah harus mengikuti panduan penulisan seperti tercantum pada website dan naskah dikirim secara elektronik (online submission) melalui http:// journal.ipb.ac.id/index.php/jtep. Penanggungjawab:Ketua

show abstract

“…So, all of the input and output data were normalized between 0,1 and 0,9 due to restriction of sigmoid function (Nasr, Badrand Joun, 2003), (Sanjay, Jyothi and Chin, 2006), (Nasr, Badr, and Joun, 2003) using the below rearranged formula as follows:…”

Section: Normalization Of Experimental Datamentioning

confidence: 99%

Optimal Network Architecture for Nusselt Number and Friction Factor

Tandiroglu¹

2015

EJOVOC Electronic Journal of Vocational Colleges

View full text Add to dashboard Cite

This present research uses artifical neural networks (ANNs) to determine Nusselt numbers and friction factors for nine different baffle plate inserted tubes. MATLAB toolbox was used to search better network configuration prediction by using commonly used multilayer feedforward neural networks (MLFNN) with back propagation (BP) learning algorithm with five different training functions with adaptation learning function of mean square error and TANSIG transfer function. In this research, eighteen data samples were used in a series of runs for each nine samples of baffle-inserted tube. Up to 70% of the whole experimental data was used to train the models, 15 % was used to test the outputs and the remaining data points which were not used for training were used to evaluate the validity of the ANNs. The results show that the TRAINBR training function was the best model for predicting the target experimental outputs.

show abstract

Backpropagation neural networks for modeling gasoline consumption

Cited by 82 publications

References 9 publications

Modeling and forecasting natural gas demand in Bangladesh

Modeling and forecasting natural gas demand in Bangladesh

The use of Vis-NIR for Detecting Huanglongbing Disease on Leaves of Citrus Orchard

Optimal Network Architecture for Nusselt Number and Friction Factor

Contact Info

Product

Resources

About