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  • 1
    Book
    Book
    Boston ; Basel ; Berlin :Birkhäuser,
    UID:
    almafu_BV001848543
    Format: X, 193 S. : graph. Darst.
    ISBN: 3-7643-3421-5 , 0-8176-3421-5
    Series Statement: Mathematical modeling 3
    Language: English
    Subjects: Biology , Mathematics
    RVK:
    RVK:
    RVK:
    RVK:
    Keywords: Ökosystem ; Mathematisches Modell ; Ökologie ; Mathematisches Modell ; Energiebilanz ; Graph ; Differentialgleichung ; Stabilität ; Dynamisches System ; Aufgabensammlung ; Aufgabensammlung ; Aufgabensammlung
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Book
    Book
    Boston [u.a.] :Birkhäuser,
    UID:
    almafu_BV005485220
    Format: VII, 166 S. : , graph. Darst.
    ISBN: 3-7643-3585-8 , 0-8176-3585-8
    Series Statement: Mathematical modeling 7
    Note: Literaturverz. S. 160 - 162
    Language: English
    Subjects: Computer Science
    RVK:
    RVK:
    Keywords: Neuronales Netz ; Codierungstheorie ; Neuronales Netz ; Problemlösen ; Neuronales Netz ; Dynamisches System ; Mustererkennung ; Code
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    Online Resource
    Online Resource
    Boston, MA : Birkhäuser Boston
    UID:
    b3kat_BV042420116
    Format: 1 Online-Ressource
    ISBN: 9781461232162 , 9781461278368
    Series Statement: Mathematical Modeling 7
    Note: In mathematics there are limits, speed limits of a sort, on how many computational steps are required to solve certain problems. The theory of computational complexity deals with such limits, in particular whether solving an n-dimensional version of a particular problem can be accomplished with, say, 2 n n steps or will inevitably require 2 steps. Such a bound, together with a physical limit on computational speed in a machine, could be used to establish a speed limit for a particular problem. But there is nothing in the theory of computational complexity which precludes the possibility of constructing analog devices that solve such problems faster. It is a general goal of neural network researchers to circumvent the inherent limits of serial computation. As an example of an n-dimensional problem, one might wish to order n distinct numbers between 0 and 1. One could simply write all n! ways to list the numbers and test each list for the increasing property. There are much more efficient ways to solve this problem; in fact, the number of steps required by the best sorting algorithm applied to this problem is proportional to n In n
    Language: English
    Keywords: Neuronales Netz ; Problemlösen ; Neuronales Netz ; Dynamisches System ; Neuronales Netz ; Codierungstheorie ; Mustererkennung ; Code
    Library Location Call Number Volume/Issue/Year Availability
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  • 4
    Online Resource
    Online Resource
    Boston, MA : Birkhäuser Boston
    UID:
    b3kat_BV042420309
    Format: 1 Online-Ressource (208p)
    ISBN: 9781461245506 , 9780817634216
    Series Statement: Mathematical Modelling 3
    Note: Mathematical ecology is the application of mathematics to describe and understand ecosystems. There are two main approaches. One is to describe natural communities and induce statistical patterns or relationships which should generally occur. However, this book is devoted entirely to introducing the student to the second approach: to study deterministic mathematical models and, on the basis of mathematical results on the models, to look for the same patterns or relationships in nature. This book is a compromise between three competing desiderata. It seeks to: maximize the generality of the models; constrain the models to "behave" realistically, that is, to exhibit stability and other features; and minimize the difficulty of presentations of the models. The ultimate goal of the book is to introduce the reader to the general mathematical tools used in building realistic ecosystem models. Just such a model is presented in Chapter Nine. The book should also serve as a stepping-stone both to advanced mathematical works like Stability of Biological Communities by Yu. M. Svirezhev and D. O. Logofet (Mir, Moscow, 1983) and to advanced modeling texts like Freshwater Ecosystems by M. Straskraba and A. H. Gnauch (Elsevier, Amsterdam, 1985)
    Language: English
    Keywords: Ökologie ; Mathematisches Modell ; Ökosystem ; Mathematisches Modell ; Energiebilanz ; Graph ; Differentialgleichung ; Stabilität ; Dynamisches System ; Aufgabensammlung
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  • 5
    Online Resource
    Online Resource
    Boston, MA :Birkhäuser Boston,
    UID:
    almahu_9947362987802882
    Format: X, 194 p. , online resource.
    ISBN: 9781461245506
    Series Statement: Mathematical Modelling ; 3
    Content: Mathematical ecology is the application of mathematics to describe and understand ecosystems. There are two main approaches. One is to describe natural communities and induce statistical patterns or relationships which should generally occur. However, this book is devoted entirely to introducing the student to the second approach: to study deterministic mathematical models and, on the basis of mathematical results on the models, to look for the same patterns or relationships in nature. This book is a compromise between three competing desiderata. It seeks to: maximize the generality of the models; constrain the models to "behave" realistically, that is, to exhibit stability and other features; and minimize the difficulty of presentations of the models. The ultimate goal of the book is to introduce the reader to the general mathematical tools used in building realistic ecosystem models. Just such a model is presented in Chapter Nine. The book should also serve as a stepping-stone both to advanced mathematical works like Stability of Biological Communities by Yu. M. Svirezhev and D. O. Logofet (Mir, Moscow, 1983) and to advanced modeling texts like Freshwater Ecosystems by M. Straskraba and A. H. Gnauch (Elsevier, Amsterdam, 1985).
    Note: One-An Introduction to Dynamical Systems as Models -- 1.1 Ecosystem Development in Terms of Ecology -- 1.2 State Space, or How to Add Apples and Oranges -- 1.3 Dynamical Systems as Treasure Hunts -- Two-Simple Difference Equation Models -- 2.1 Predator-Prey Difference Equation Dynamical Systems -- 2.2 Probabilistic Limit Cycles -- Three-Formalizing the Notion of Stability -- 3.1 The Concept of Ecosystem Stability -- 3.2 The Relation of Difference and Differential Equations -- 3.3 Limit Cycles -- 3.4 Lyapunov Theory -- 3.5 The Trapping of Trajectories -- Four-Introduction to Ecosystem Models -- 4.1 Brewing Beer and Yeast Population Dynamics -- 4.2 Attractor Trajectories -- 4.3 Derivatives of System Functions -- 4.4 The Linearization Theorem -- 4.5 The Hurwitz Stability Test -- Five-Introduction to Ecosystem Models -- 5.1 The Community Matrix -- 5.2 Predator-Prey Equations and Generalizations Thereof -- 5.3 Signed Digraphs -- 5.4 Qualitative Stability of Linear Systems -- Six-Qualitative Stability of Ecosystem Models -- 6.1 Qualitative Results in Modeling -- 6.2 Holistic Ecosystem Models -- 6.3 Holistic Ecosystem Models with Attractor Trajectories -- Seven-The Behavior of Models with Attractor Regions -- 7.1 Attractor Regions -- 7.2 The Lorenz Model -- 7.3 Elementary Ecosystem Models with Chaotic Dynamics -- Eight-Holistic Ecosystem Models with Attractor Regions -- 8.1 An Attractor Region Theorem -- 8.2 An Example -- Nine-Sequencing Energy Flow Models to Account for Shortgrass Prairie Energy Dynamics -- 9.1 Energy Flow and Accumulation Modeling -- 9.2 Accumulation Modeling -- 9.3 Estimating Energy Flows -- 9.4 Equations and Trajectories -- 9.5 Stability.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9780817634216
    Language: English
    Library Location Call Number Volume/Issue/Year Availability
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  • 6
    UID:
    almahu_9947362836902882
    Format: online resource.
    ISBN: 9781461232162
    Series Statement: Mathematical Modeling ; 7
    Content: In mathematics there are limits, speed limits of a sort, on how many computational steps are required to solve certain problems. The theory of computational complexity deals with such limits, in particular whether solving an n-dimensional version of a particular problem can be accomplished with, say, 2 n n steps or will inevitably require 2 steps. Such a bound, together with a physical limit on computational speed in a machine, could be used to establish a speed limit for a particular problem. But there is nothing in the theory of computational complexity which precludes the possibility of constructing analog devices that solve such problems faster. It is a general goal of neural network researchers to circumvent the inherent limits of serial computation. As an example of an n-dimensional problem, one might wish to order n distinct numbers between 0 and 1. One could simply write all n! ways to list the numbers and test each list for the increasing property. There are much more efficient ways to solve this problem; in fact, the number of steps required by the best sorting algorithm applied to this problem is proportional to n In n .
    Note: 0—The Neural Network Approach to Problem Solving -- 0.1 Defining a Neural Network -- 0.2 Neural Networks as Dynamical Systems -- 0.3 Additive and High Order Models -- 0.4 Examples -- 0.5 The Link with Neuroscience -- 1—Neural Networks as Dynamical Systems -- 1.1 General Neural Network Models -- 1.2 General Features of Neural Network Dynamics -- 1.3 Set Selection Problems -- 1.4 Infeasible Constant Trajectories -- 1.5 Another Set Selection Problem -- 1.6 Set Selection Neural Networks with Perturbations -- 1.7 Learning -- Problems and Answers -- 2—Hypergraphs and Neural Networks -- 2.1 Multiproducts in Neural Network Models -- 2.2 Paths, Cycles, and Volterra Multipliers -- 2.3 The Cohen-Grossberg Function -- 2.4 The Foundation Function ? -- 2.5 The Image Product Formulation of High Order Neural Networks -- Problems and Answers -- 3—The Memory Model -- 3.1 Dense Memory with High Order Neural Networks -- 3.2 High Order Neural Network Models -- 3.3 The Memory Model -- 3.4 Dynamics of the Memory Model -- 3.5 Modified Memory Models Using the Foundation Function -- 3.6 Comparison of the Memory Model and the Hopfield Model -- Problems and Answers -- 4—Code Recognition, Digital Communications, and General Recognition -- 4.1 Error Correction for Binary Codes -- 4.2 Additional Tests of the Memory Model as a Decoder -- 4.3 General Recognition -- 4.4 Scanning in Image Recognition -- 4.5 Commercial Neural Network Decoding -- Problems and Answers -- 5—Neural Networks as Dynamical Systems -- 5.1 A Two-Dimensional Limit Cycle -- 5.2 Wiring -- 5.3 Neural Networks with a Mixture of Limit Cycles and Constant Trajectories -- Problems and Answers -- 6—Solving Operations Research Problems with Neural Networks -- 6.1 Selecting Permutation Matrices with Neural Networks -- 6.2 Optimization in a Modified Permutation Matrix Selection Model -- 6.3 The Quadratic Assignment Problem -- Appendix A—An Introduction to Dynamical Systems -- A.1 Elements of Two-Dimensional Dynamical Systems -- A.2 Elements of n-Dimensional Dynamical Systems -- A.3 The Relation Between Difference and Differential Equations -- A.4 The Concept of Stability -- A.5 Limit Cycles -- A.6 Lyapunov Theory -- A.7 The Linearization Theorem -- A.8 The Stability of Linear Systems -- Appendix B—Simulation of Dynamical Systems with Spreadsheets -- References -- Index of Key Words -- Epilog.
    In: Springer eBooks
    Additional Edition: Printed edition: ISBN 9781461278368
    Language: English
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