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"The second edition of the book augments the first edition with more tricks, which have resulted from 14 years of theory and experimentation by some of the world's most prominent neural network researchers. These tricks can make a substantial difference (in terms of speed, ease of implementation, and accuracy) when it comes to putting algorithms to work on real problems."

"ABSTRACTKecepatan gelombang-P adalah parameter yang sangat penting dalam kegiatan eksplorasi. Kecepatan gelombang-P (Vp) dapat ditentukan dari data logging wireline. Umumnya, industri hanya melakukan penebangan pada kedalaman tertentu yang dianggap memiliki prospek untuk menghemat biaya eksplorasi. Data logging wireline yang hilang pasti akan menjadi masalah serius karena membutuhkan data yang lengkap dan akurat sehingga peluang keberhasilan eksplorasi tinggi. Diperlukan metode untuk memperkirakan Vp menggunakan data selain sonic log. Penelitian ini bertujuan untuk memperkirakan Vp berdasarkan data log yang tersedia menggunakan metode Genetic Algorithm (GA) dan Neural Network (NN). Proses inversi dilakukan dengan menggunakan metode di sumur Ikpikpuk1 sampai hubungan Vp diperoleh dengan log gamma ray, log resistivitas dan log densitas. Proses selanjutnya memperkirakan Vp dengan tes buta pada sumur yang sama tetapi kedalamannya berbeda dari inversi. Hasil penelitian menunjukkan bahwa metode Neural Network lebih unggul daripada metode Genetic Algorithm. Dalam tiga formasi yang menjadi objek penelitian, metode Neural Network konsisten karena eror estimasi lebih kecil dari metode Genetic Algorithm.

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ABSTRACTP-wave velocity is a very important parameter in exploration activities. P-wave velocity (Vp) can be determined from wireline data logging. Generally, industries only cut down to certain depths which are considered to have prospects to save on exploration costs. Lost wireline logging data will definitely be a serious problem because it requires complete and accurate data so the chances of exploration success are high. A method is needed to estimate Vp using data other than sonic logs. This study aims to estimate Vp based on log data available using the Genetic Algorithm (GA) and Neural Network (NN) methods. The inversion process is carried out using the method at Ikpikpuk 1 well until the Vp relationship is obtained by gamma ray log, resistivity log and density log. The next process estimates Vp by blind testing at the same well but the depth is different from inversion. The results showed that the Neural Network method is superior to the Genetic Algorithm method. In the three formations that are the object of research, the Neural Network method is consistent because the estimation error is smaller than the Genetic Algorithm method."

"Instructs graduate and undergraduate-level students in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering on the concepts of complex-valued neural networks. This title focuses on neural networks that deal with complex numbers and the practical advantages of complex-valued neural networks. "

"Electroencephalography (EEG) is recording of the electrical signals on the scalp. These signals come from sources of activity within the brain; however it can be difficult to determine where the sources originate from just by looking at the signals. Through signal processing, these EEG signals can be analyzed and displayed as more useful information. This research explored the evolution of EEG (Brain-waves) topography. The aim of this research was to extract the origins of brain-waves within the brain from EEG data and develop an algorithm to analyze and display this information. This was done in the MATLAB environment by creating: a working software to display and pre-process multichannel EEG data; software/algorithms that could localize sources of EEG within the brain; and a clinician-friendly GUI block. Neural networks are a supervised machine learning technique that can be used to train a system based on previously seen data. Using this approach, it is possible to accurately extract signal positions within the brain."

"ABSTRACTHiggs Boson yang ditemukan pada tahun 2012 di Large Hadron Collider LHC hingga saat ini masih dianggap Higgs Boson Standard Model -sebuah partikel skalar. Padahal, ada kemungkinan bahwa partikel itu adalah Higgs Boson pseudo-scalar. Saat ini, LHC masih belum dapat membedakan apakah partikel Higgs itu scalar, pseudo-scalar, atau campuran dari keduanya. Fokus dari penelitian ini adalah pembentukan dari algoritma neural nerwork untuk membedakan sinyal yang datang dari Higgs Boson scalar dan Higgs Boson pseudo-scalar pada tumbukan proton-proton.

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ABSTRACTThe Higgs Boson discovered in 2012 at the Large Hadron Collider LHC is still assumed to be Standard Model Higgs Boson a scalar particle. But there are still possibilities for it to be a pseudo scalar Higgs Boson. Currently LHC has not been able to discriminate whether the particle is a scalar, pseudo scalar, or mixed pseudoscalar particle. The focus of this research is in the formulation of the neural network algorithm to discriminate between event signals from a scalar Higgs Boson and pseudo scalar Higgs Boson from proton proton collision."

"The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions, this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video."

"In this research it is applied a pattern recognition system by using an artificial neural networks to recognize several samples on weaving products, such as plain weave, twill weave, and sateen weave. In order to extract textural characteristics or features from sample images, it is used the Neighboring Grey Level Dependence Matrix (NGLDM)-method as proposed by Sun, which is invariant under rotation and linear grey level transformation. Five textural features i.e. Small Number Emphasis, Large Number Emphasis, Number Non uniformity, Second Moment, and Entropy will be used as the representative features of sample images. Those features are used as input to the neural networks, which have learned by the back propagation method. Baths methods (continuous and periodic) for changing the interconnection weights, and the performances of the two types of neuron transfer functions are also observed and investigated, in order to obtain an optimal network configurations. The results of experiment will be very useful for the next stage of research in designing an integrated vision system for the recognition of weaving product's quality in textile industry."