About book The book that accompanies the programs, which can be downloaded from this Web page is intended to introduce a Reader in an attracting and tireless way into the theme of neural networks. These networks belong to the popular and interesting field of Artificial Intelligence, which nowadays is often called 'soft computational techniques' (or 'soft computing'). This 'softness' of neural networks comes from the fact that to solve computer science problems they do not use created a’priori 'hard' algorithms, but they learn by themselves how to solve tasks presented to them.
For this reason they often succeed to solve problems for which nobody has yet managed to build any efficient algorithm - and this is just beautiful! There are many books which describe neural networks, however this book, introduced here by us, distinguishes itself, because with the help of this book the Reader by himself discovers the properties of neural networks. To enrich and fully support this discovery on this Web page there are placed programs, however to find out how to use them and how to interpret all the receiving results - it is necessary to read the book. So this book is the guide and the adviser for all persons practicing with these free available programs, and its additional advantage is the fact that this book can be read by literally everybody, because it presents all concepts through simple descriptions and inspiring pictures - without the use of even one mathematical formula. In spite of this limitation (which in reality is an advantage) after reading this book the Reader will possess almost a complete set of information on the subject of building neural networks, their functioning and the methods of applying them. After every chapter in this book there have been additionally placed the numerous assignments and examples for independent realizations, and which allow us to verify to what extent the Reader has really gained this knowledge.
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We invite to use these programs and to read the indicated book, because by studying about neural networks one gets to know not only a very efficient and modern computer tool, but also finds out about functioning of one’s own brain. By practicing with the programs available here it is also possible to make a fascinating travel into the deep inside of our own brain with all its mental and intellectual abilities, because neural networks are the models of fragments of the natural biological nervous system. To investigate an alive brain one needs to have enormous laboratories and great funds, whereas programs presented here (and descriptions included inside the book) will allow You at home on Your own computer to carry on very interesting and inspiring experiences, thanks to which You will be able to discover and to deepen also the nature of a human intellect.
Programs Attention: a new version of programs (1.1)! We strongly advice everyone who downloaded the previous version to make use of the new one. Welcome to the page where you may download programs that accompany the book „Exploring Neural Networks with C#” published by CRC Press. To be able to use these programs a few steps are needed:. Download.NET Framework 2.0. It is enough that you click and then do the following:.
If you use Internet Explorer web browser then click on the button Save or the option Save this program on a disk. Then point the location where the file should be saved and once more click on the button Save. Try to remember the place where you have saved your file. If You use Mozilla Firefox web browser then to save a file click on the button Save. Firefox by default saves files on the Desktop, unless one points a different location by clicking the following buttons: Tools - Options - Main - Downloading. If you know that.NET Framework version 2.0 (or higher) has already been installed on your computer, then you may skip steps no 2 and 3. Download the program installer.
Click and then - in the same way as previously - save the file on the disk. Install the.NET Framework. Click twice on the previously downloaded file dotnetfx.exe. The installer will appear and will guide you through the installation process. Install the example programs. Click twice on the file „NeuralNetworksExamplesSetup.msi”.
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There will appear the next sequential installer. On the first screen click on the button Next. On the second screen You may click at once on the button Next, unless Your computer is also used by some other people and You would like to allow them to use these programs - then before clicking Next mark the option Everyone. After doing this click on the button Next and then on the button Close. If You have made everything correctly then on Your Start menu our example programs should appear. If You have had some problems, make sure you have everything in the way that is needed; and if despite everything you cannot manage - then.
Source codes Important: Source codes are not required to run the programs! Source codes of the programs can be downloaded.
They have form of a Visual Studio solution packed into ZIP archive. A free version of Visual Studio Express can be downloaded. Version archive Although we recommend to always use the latest version of programs, there sometimes might be need to use a previous version.
In such case, please use our.
Abstract The many difficult problems that must now be addressed in mining sciences make us search for ever newer and more efficient computer tools that can be used to solve those problems. Among the numerous tools of this type, there are neural networks presented in this article – which, although not yet widely used in mining sciences, are certainly worth consideration. Neural networks are a technique which belongs to so called artificial intelligence, and originates from the attempts to model the structure and functioning of biological nervous systems.
Initially constructed and tested exclusively out of scientific curiosity, as computer models of parts of the human brain, neural networks have become a surprisingly effective calculation tool in many areas: in technology, medicine, economics, and even social sciences. Unfortunately, they are relatively rarely used in mining sciences and mining technology. The article is intended to convince the readers that neural networks can be very useful also in mining sciences. It contains information how modern neural networks are built, how they operate and how one can use them. The preliminary discussion presented in this paper can help the reader gain an opinion whether this is a tool with handy properties, useful for him, and what it might come in useful for. Of course, the brief introduction to neural networks contained in this paper will not be enough for the readers who get convinced by the arguments contained here, and want to use neural networks.
Torrent faites entrer l accuse saison 13 vf. They will still need a considerable portion of detailed knowledge so that they can begin to independently create and build such networks, and use them in practice. However, an interested reader who decides to try out the capabilities of neural networks will also find here links to references that will allow him to start exploration of neural networks fast, and then work with this handy tool efficiently. This will be easy, because there are currently quite a few ready-made computer programs, easily available, which allow their user to quickly and effortlessly create artificial neural networks, run them, train and use in practice. The key issue is the question how to use these networks in mining sciences.
The fact that this is possible and desirable is shown by convincing examples included in the second part of this study. From the very rich literature on the various applications of neural networks, we have selected several works that show how and what neural networks are used in the mining industry, and what has been achieved thanks to their use. The review of applications will continue in the next article, filed already for publication in the journal „Archives of Mining Sciences“.
Only studying these two articles will provide sufficient knowledge for initial guidance in the area of issues under consideration here. Ahmadi M.A., 2015. Developing a robust surrogate model of chemical flooding based on the artificial neural network for enhanced oil recovery implications. Mathematical Problems in Engineering, 706897 (9 pp.) doi. Aliouane L., Ouadfeul SA., Boudella A., 2013. Fractal analysis based on the continuous wavelet transform and lithofacies classification from well-logs data using the self-organizing map neural network. Arabian Journal of Geosciences, 6(6), 1681-1691.
Asoodeh M., Shadizadeh S.R., Zargar G., 2015. The Estimation of Stoneley Wave Velocity from Conventional Well Log Data: Using an Integration of Artificial Neural Networks. Energy Sources, Part A (Recovery, Utilization, and Environmental Effects), vol. Baijie Wang; Xin Wang; Zhangxin Chen, 2013.
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A hybrid framework for reservoir characterization using fuzzy ranking and an artificial neural network. Computers-and-Geosciences, 57, 1-10. Dali Guo, Kai Zhu, Liang Wang, Jiaqi Li, Jiangwen Xu, 2014. A new methodology for identification of potential pay zones from well logs: Intelligent system establishment and application in the Eastern Junggar Basin, China.
Petroleum Science, vol.11, no.2, June, 258-264. Fegh A., Riahi M.A., Norouzi G.H., 2013. Permeability prediction and construction of 3D geological model: application of neural networks and stochastic approaches in an Iranian gas reservoir. Neural Computing and Applications. 23(6): 1763-1770. Ghavipour M., Ghavipour M., Chitsazan M., Najibi S.H., Ghidary S.S., 2013.
Experimental study of natural gas hydrates and a novel use of neural network to predict hydrate formation conditions. Chemical Engineering Research and Design, vol. 2, Feb., 264-273.
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Ghiasi Freez J., Kadkhodaie Ilkhchi A., Ziaii M., 2012. The Application of Committee Machine with Intelligent Systems to the Prediction of Permeability from Petrographic Image Analysis and well logs Data: a case Study from the South pars gas Field, South Iran. Petroleum Science and Technology, 30(20), 2122-2136. Ghiasi M.M., Bahadori A., Zendehboudi S., Chatzis I., 2015. Rigorous models to optimise stripping gas rate in natural gas dehydration units. Fuel, 140, 421-428. Jiuyong Li, Longbing Cao, Can Wang, Kay Chen Tan, Bo Liu, Jian Pei, Tseng VS, 2013.
Ensemble learning model for petroleum reservoir characterization: a case of feed-forward back-propagation neural networks. Trends and Applications in Knowledge Discovery and Data Mining. Springer-Verlag, 71-82. Konate’ A.A., Heping Pan, Sinan Fang, Asim S., Ziggah Y.Y., Chengxiang Deng.
Khan N., 2015. Capability of self-organizing map neural network in geophysical log data classification: Case study from the CCSD-MH. Journal of Applied Geophysics, vol.118, July, 37-46. Lee S., Hyun Joo Oh, 2011. Application of Artificial Neural Network for Mineral Potential Mapping. Artificial Neural Networks Application, 67-104. Li Yang; Lixue Chen; Xinyu Gen; Lin Wang; Jun Zhang, 2012.
Application of factor neural network in multi-expert system for oil-gas reservoir protection. Journal of Theoretical and Applied Information Technology, 46(1), 303-308. Morshedi S., Torkaman M., Sedaghat M.H., Ghazanfari M.H., 2014. The simulation of microbial enhanced oil recovery by using a two-layer perceptron neural network.
Petroleum Science and Technology, vol. 22, 2700-2707. Nooruddin H.A., Anifowose F., Abdulraheem A., 2014. Using soft computing techniques to predict corrected air permeability using Thomeer parameters, air porosity and grain density. Computers & Geosciences, vol. 64, March, 72-80. Olatunji S.O., Selamat A., Abdul Raheem A.A., 2013.
Extreme Learning Machines Based Model for Predicting Permeability of Carbonate Reservoir. International Journal of Digital Content Technology and its Applications, 7(1), 450-459.
Silva A.A., Lima Neto I.A., Missa’gia R.M., Ceia M.A., Carrasquilla A.G., Archilha N.L., 2015. Artificial neural networks to support petrographic classification of carbonate-siliciclastic rocks using well logs and textural information. Journal of Applied Geophysics, vol. 117, June, 118-125. Tadeusiewicz R., Chaki R., Chaki N., 2014. Exploring Neural Networks with C#. CRC Press, Taylor & Francis Group, Boca Raton.
Torabi F., Jamaloei B.Y., Zunti C.J., Markwart C.C., 2014. The Prediction of Viscosity, Formation Volume Factor, and Bubble Point Pressure of Heavy Oil Using Statistical Analysis, Artificial Neural Networks, and Three-dimensional Modeling: A Comparative Evaluation. Energy Sources, Part A (Recovery, Utilization, and Environmental Effects), vol. Torppa J., Nykanen V., 2013. Using self-organizing maps in mineral potential studies. Bulletin – Geological Survey of Finland, vol.198, 185-186.
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Wei Zheng, Xiuwen Mo, 2014. Complex lithology automatic identification technology based on fuzzy clustering and neural networks. 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 227-231. Wei-Zhang, Yibing-Shi, Yanjun-Li, 2014. An effective detection method based on IPSO-WNN for acoustic telemetry signal of well logging while drilling. International-Conference-on-Information-Science,-Electronics-and-Electrical-Engineering-ISEEE, 49-53.
Wonseok-Lee, Hochang-Jang, Jeonghwan-Lee, 2014. Development and application of the artificial neural network based technical screening guide system to select production methods in a coalbed methane reservoir. 32(5): 791-804.