Dynamic Analysis Of Feedforward Neural Networks Using Simulated And Measured Data
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Dynamic Analysis Of Feedforward Neural Networks Using Simulated And Measured Data
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Author : Gregory L. Tarr (CAPT, USAF.)
language : en
Publisher:
Release Date : 1988
Dynamic Analysis Of Feedforward Neural Networks Using Simulated And Measured Data written by Gregory L. Tarr (CAPT, USAF.) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with Neural networks (Computer science) categories.
Dynamic Analysis Of Feedforward Neural Networks Using Simulated And Measured Data
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Author : Gregory L. Tarr
language : en
Publisher:
Release Date : 1988
Dynamic Analysis Of Feedforward Neural Networks Using Simulated And Measured Data written by Gregory L. Tarr and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with Computer vision categories.
An environment is developed for the study of dynamic changes in patterns of weight and node values for artificial neural networks. Graphic representations of neural network internal states are displayed using a high resolution video terminal. Patterns of node firings and changes in weight vectors are displayed to provide insight during training. Four pattern recognition problems are applied to four types of artificial neural networks. Using simulated data, a simple disjoint region classification problem is developed and examined using a Kohonen net and a multilayer feedforward back propagation (MFB) network. A MFB neural network is also used to simulate a Fourier filter. Using a Kohonen net, a MFB, a counterpropagation and a hybrid network, data measured from infrared and laser radar imagery of military vehicles is analyzed. The accuracy and training times for a MFB net and a Hybrid net are compared using an ambiguous decision region problem. Each classification problem is examined and compared to classical, nearest neighbor pattern recognition techniques. Using dynamic analysis, neural network is developed using Kohonen training rules for the first hidden layer followed by one or two hidden layers using standard back propagation rules for training. Advantage of the hybrid network is shown for classification problems involving anomalies characteristic of measured data. The Hybrid network requires less training and fewer interconnections than MFB when classifications involves ambiguous decision regions. Theses. (RH).
Scientific And Technical Aerospace Reports
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Author :
language : en
Publisher:
Release Date : 1995
Scientific And Technical Aerospace Reports written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Aeronautics categories.
Masters Theses In The Pure And Applied Sciences
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Author : Wade H. Shafer
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Masters Theses In The Pure And Applied Sciences written by Wade H. Shafer and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Science categories.
Masters Theses in the Pure and Applied Sciences was first conceived, published, and disseminated by the Center for Information and Numerical Oata Analysis and Synthesis (CINOAS) * at Purdue. University in 1957, starting its coverage of theses with the academic year 1955. Beginning with Volume 13, the printing and dissemination phases of the activity were transferred to University Microfilms/Xerox of Ann Arbor, Michigan, with the thought that such an arrangement would be more beneficial to the academic and general scientific and technical community. After five years of this joint undertaking we had concluded that it was in the interest of all con cerned if the printing and distribution of the volumes were handled by an interna tional publishing house to assure improved service and broader dissemination. Hence, starting with Volume 18, Masters Theses in the Pure and Applied Sciences has been disseminated on a worldwide basis by Plenum Publishing Cor poration of New York, and in the same year the coverage was broadened to include Canadian universities. All back issues can also be ordered from Plenum. We have reported in Volume 33 (thesis year 1988) a total of 13,273 theses titles from 23 Canadian and 1 85 United States universities. We are sure that this broader base for these titles reported will greatly enhance the value of this important annual reference work. While Volume 33 reports theses submitted in 1988, on occasion, certain univer sities do report theses submitted in previous years but not reported at the time.
Computational Ecology
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Author : Wenjun Zhang
language : en
Publisher: World Scientific
Release Date : 2010
Computational Ecology written by Wenjun Zhang and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Computers categories.
Ch. 1. Introduction. 1. Computational ecology. 2. Artificial neural networks and ecological applications -- pt. I. Artificial neural networks : principles, theories and algorithms. ch. 2. Feedforward neural networks. 1. Linear separability and perceptron. 2. Some analogies of multilayer feedforward networks. 3. Functionability of multilayer feedforward networks. ch. 3. Linear neural networks. 1. Linear neural networks. 2. LMS rule. ch. 4. Radial basis function neural networks. 1. Theory of RBF neural network. 2. Regularized RBF neural network. 3. RBF neural network learning. 4. Probabilistic neural network. 5. Generalized regression neural network. 6. Functional link neural network. 7. Wavelet neural network. ch. 5. BP neural network. 1. BP algorithm. 2. BP theorem. 3. BP training. 4. Limitations and improvements of BP algorithm. ch. 6. Self-organizing neural networks. 1. Self-organizing feature map neural network. 2. Self-organizing competitive learning neural network. 3. Hamming neural network. 4. WTA neural network. 5. LVQ neural network. 6. Adaptive resonance theory. ch. 7. Feedback neural networks. 1. Elman neural network. 2. Hopfield neural networks. 3. Simulated annealing. 4. Boltzmann machine. ch. 8. Design and customization of artificial neural networks. 1. Mixture of experts. 2. Hierarchical mixture of experts. 3. Neural network controller. 4. Customization of neural networks. ch. 9. Learning theory, architecture choice and interpretability of neural networks. 1. Learning theory. 2. Architecture choice. 3. Interpretability of neural networks. ch. 10. Mathematical foundations of artificial neural networks. 1. Bayesian methods. 2. Randomization, bootstrap and Monte Carlo techniques. 3. Stochastic process and stochastic differential equation. 4. Interpolation. 5. Function approximation. 6. Optimization methods. 7. Manifold and differential geometry. 8. Functional analysis. 9. Algebraic topology. 10. Motion stability. 11. Entropy of a system. 12. Distance or similarity measures. ch. 11. Matlab neural network toolkit. 1. Functions of perceptron. 2. Functions of linear neural networks. 3. Functions of BP neural network. 4. Functions of self-organizing neural networks. 5. Functions of radial basis neural networks. 6. Functions of probabilistic neural network. 7. Function of generalized regression neural network. 8. Functions of Hopfield neural network. 9. Function of Elman neural network -- pt. II. Applications of artificial neural networks in ecology. ch. 12. Dynamic modeling of survival process. 1. Model description. 2. Data description. 3. Results. 4. Discussion. ch. 13. Simulation of plant growth process. 1. Model description. 2. Data source. 3. Results. 4. Discussion. ch. 14. Simulation of food intake dynamics. 1. Model description. 2. Data description. 3. Results. 4. Discussion. ch. 15. Species richness estimation and sampling data documentation. 1. Estimation of plant species richness on grassland. 2. Documentation of sampling data of invertebrates. ch. 16. Modeling arthropod abundance from plant composition of grassland community. 1. Model description. 2. Data description. 3. Results. 4. Discussion. ch. 17. Pattern recognition and classification of ecosystems and functional groups. 1. Model description. 2. Data source. 3. Results. 4. Discussion. ch. 18. Modeling spatial distribution of arthropods. 1. Model description. 2. Data description. 3. Results. 4. Discussion. ch. 19. Risk assessment of species invasion and establishment. 1. Invasion risk assessment based on species assemblages. 2. Determination of abiotic factors influencing species invasion. ch. 20. Prediction of surface ozone. 1. BP prediction of daily total ozone. 2. MLP Prediction of hourly ozone levels. ch. 21. Modeling dispersion and distribution of oxide and nitrate pollutants. 1. Modeling nitrogen dioxide dispersion. 2. Simulation of nitrate distribution in ground water. ch. 22. Modeling terrestrial biomass. 1. Estimation of aboveground grassland biomass. 2. Estimation of trout biomass
Masters Theses In The Pure And Applied Sciences
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Author : W. H. Shafer
language : en
Publisher: Plenum Publishing Corporation
Release Date : 1992
Masters Theses In The Pure And Applied Sciences written by W. H. Shafer and has been published by Plenum Publishing Corporation this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Education categories.
Masters Theses Listed by Discipline: Aerospace Engineering. Agricultural Economics, Sciences and Engineering. Architechtural Engineering and Urban Planning. Astronomy. Astrophysics. Ceramic Engineering. Communications Engineering and Computer Science. Cryogenic Engineering. Electrical Engineering. Engineering Mechanics. Engineering Physics. Engineering Science. Fuels, Combustion, and Air Pollution. General and Environmental Engineering. Geochemistry and Soil Science. Geological Sciences and Geophysical Engineering. Geology and Earth Science. Geophysics. Industrial Engineering. Marine and Ocean Engineering. Materials Science and Engineering. Mechanical Engineering and Bioengineering. Metallurgy. Meteorology and Atmospheric Science. 17 additional disciplines. Index.
Technical Reports Awareness Circular Trac
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Author :
language : en
Publisher:
Release Date : 1989-03
Technical Reports Awareness Circular Trac written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989-03 with Science categories.
Government Reports Annual Index
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Author :
language : en
Publisher:
Release Date : 199?
Government Reports Annual Index written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 199? with categories.
Neural Networks And Simulation Methods
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Author : Wu
language : en
Publisher: CRC Press
Release Date : 1993-12-14
Neural Networks And Simulation Methods written by Wu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-12-14 with Computers categories.
This work explains network dynamics, learning paradigms, and computational capabilities of feedforward, self-organization, and feedback neural network models-addressing specific problems such as data fusion and data modeling. It goes on to describe a neural network simulation software package - USTCNET and gives some segments of the program.
Simulation Of Dynamic Processes With Adaptive Neural Networks
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Author :
language : en
Publisher:
Release Date : 1998
Simulation Of Dynamic Processes With Adaptive Neural Networks written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with categories.
Many industrial processes are highly non-linear and complex. Their simulation with first-principle or conventional input-output correlation models is not satisfactory, either because the process physics is not well understood, or it is so complex that direct simulation is either not adequately accurate, or it requires excessive computation time, especially for on-line applications. Artificial intelligence techniques (neural networks, expert systems, fuzzy logic) or their combination with simple process-physics models can be effectively used for the simulation of such processes. Feedforward (static) neural networks (FNNs) can be used effectively to model steady-state processes. They have also been used to model dynamic (time-varying) processes by adding to the network input layer input nodes that represent values of input variables at previous time steps. The number of previous time steps is problem dependent and, in general, can be determined after extensive testing. This work demonstrates that for dynamic processes that do not vary fast with respect to the retraining time of the neural network, an adaptive feedforward neural network can be an effective simulator that is free of the complexities introduced by the use of input values at previous time steps.