Search Results: 'neural networking'

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64 results for "neural networking"
Architectures of Neural Networks By Homework Help Classof1
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The way the human body functions has always been a matter of fascination for people all over the world. Scientists and researchers have always tried to find a way to explore the functions of human... More > body and incorporate the same logics and same methodologies to create artificial intelligence or technological devices so that they can replace humans and make their work easier.< Less
Neural Networks: introduction and some applications By Giovanni Cialdino
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Nature has often faced problems which are similar to those faced every day by engineers. Solutions found by nature have been improved and tested over millions of years so it is not surprising that... More > such solutions work very well. Engineers can therefore get inspired by natural solutions. The first two chapters of the book try to imitate the most powerful processing units found in nature : the neuron. In the third chapter I will try to make an interconnection of artificial neurons to construct an artificial neural network with multiple applications , some of which will be shown in the fourth chapter. The last three chapters are dedicated to the use of neural networks in solving differential equations . In particular, the last chapter focuses on the solution of the Helmholtz equation with neural networks. This equation is typical of a large quantity of physical problems , and it is also used in the study of electromagnetic fields to find the fields’distribution into a metallic wave-guide .< Less
The Neural Dogma By Bradford Merrill
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A comparison of the network of neurons and the wiring of a computer to exhibit the neuron's role as a part of an irreducible whole. The thesis includes a contribution to the description of... More > emergence, and an argument that a reductionist "neuron-only" view of behavior dominates the neurosciences.< Less
KB Neural Data Mining: with Python sources By Roberto Bello
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The aim of this work is to present and describe in detail the algorithms to extract the knowledge hidden inside data using Python language, which allows us to read and easily understand the nature... More > and the characteristics of the rules of the computing utilized, as opposed to what happens in commercial applications, which are available only in the form of running codes, which remain impossible to modify. The algorithms of computing contained within the work, are minutely described, documented and available in the Python source format, and serve to extract the hidden knowledge within the data whether they are textual or numerical kinds. There are also various examples of usage, underlining the characteristics, method of execution and providing comments on the obtained results.< Less
An Investigation into the use of a Neural Tree Classifier for Knowledge Discovery in OLAP databases By David Swinburne
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Modern OLAP platforms are capable of creating databases terabytes in size and present a significant challenge to the analyst with the goal of knowledge discovery. Artificial neural networks... More > represent an aspect of machine learning that offers promise in this area. A neural map can learn to identify patterns in data of high dimensionality and a specific type of neural map, a neural tree classifier, can provide a hierarchical classification of the patterns identified. The investigation begins with a comparison of two neural tree classifiers and continues by illustrating how their application can allow the identification of multi-dimensional areas of analytical interest in an OLAP database. Finally, a novel OLAP exception "explain" technique is outlined, enabled through the use of a neural tree classifier in conjunction with discovery-driven exploration.< Less
An Investigation into the use of a Neural Tree Classifier for Knowledge Discovery in OLAP databases By David Swinburne
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Modern OLAP platforms are capable of creating databases terabytes in size and present a significant challenge to the analyst with the goal of knowledge discovery. Artificial neural networks... More > represent an aspect of machine learning that offers promise in this area. A neural map can learn to identify patterns in data of high dimensionality and a specific type of neural map, a neural tree classifier, can provide a hierarchical classification of the patterns identified. The investigation begins with a comparison of two neural tree classifiers and continues by illustrating how their application can allow the identification of multi-dimensional areas of analytical interest in an OLAP database. Finally, a novel OLAP exception "explain" technique is outlined, enabled through the use of a neural tree classifier in conjunction with discovery-driven exploration.< Less
Online Learning of a Neural Fuel Control System for Gaseous Fueled SI Engines By Travis Wiens
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This PhD dissertation presents a new type of fuel control algorithm for gaseous fuelled vehicles. Gaseous fuels such as hydrogen and natural gas have been shown to be less polluting than liquid... More > fuels, both at the tailpipe and on a total cycle. Unfortunately, it can be expensive to convert vehicles to gaseous fuels. One of major development costs for a new vehicle is the development and calibration of the fuel controller. The research presented here includes a fuel controller which does not require an expensive calibration phase. The controller is based upon a two-part model, using online training of a neural network to model the engine’s steady state characteristics. An experimental test was performed, in which the controller was installed on a truck fuelled by natural gas. The tailpipe emissions of the truck with the new controller showed better results than the OEM controller on both carbon monoxide and nitrogen oxides, and required no calibration and very little information about the engine.< Less
Online Learning of a Neural Fuel Control System for Gaseous Fueled SI Engines By Travis Wiens
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This PhD dissertation presents a new type of fuel control algorithm for gaseous fuelled vehicles. Gaseous fuels such as hydrogen and natural gas have been shown to be less polluting than liquid... More > fuels, both at the tailpipe and on a total cycle. Unfortunately, it can be expensive to convert vehicles to gaseous fuels. One of major development costs for a new vehicle is the development and calibration of the fuel controller. The research presented here includes a fuel controller which does not require an expensive calibration phase. The controller is based upon a two-part model, using online training of a neural network to model the engine’s steady state characteristics. An experimental test was performed, in which the controller was installed on a truck fuelled by natural gas. The tailpipe emissions of the truck with the new controller showed better results than the OEM controller on both carbon monoxide and nitrogen oxides, and required no calibration and very little information about the engine.< Less
Probabilistic Models of Phase Variables for Visual Representation and Neural Dynamics By Charles Cadieu
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My work seeks to contribute to three broad goals: predicting the computational representations found in the brain, developing algorithms that help us infer the computations that the brain performs,... More > and producing better statistical models of natural signals. My thesis is broken down into three major chapters that reflect these three goals. Within each chapter I develop novel probabilistic models of phase variables and apply these models to the invariant representation of visual motion, to the inference of connectivity in networks of coupled neural oscillators, and to the development of statistical models of edge structure in images.< Less
Probabilistic Models of Phase Variables for Visual Representation and Neural Dynamics By Charles Cadieu
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My work seeks to contribute to three broad goals: predicting the computational representations found in the brain, developing algorithms that help us infer the computations that the brain performs,... More > and producing better statistical models of natural signals. My thesis is broken down into three major chapters that reflect these three goals. Within each chapter I develop novel probabilistic models of phase variables and apply these models to the invariant representation of visual motion, to the inference of connectivity in networks of coupled neural oscillators, and to the development of statistical models of edge structure in images.< Less