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Overview""Et moi, ..., si j'avait Sll comment en revenir. One sennce mathematics has rendered the human race. It has put common sense back je n'y serais point alle.' Jules Verne whe"", it belongs, on the topmost shelf next to the dusty canister labelled 'discarded non- The series is divergent; therefore we may be smse'. able to do something with it. Eric T. Bell O. Heaviside Mathematics is a tool for thought. A highly necessary tool in a world where both feedback and non- linearities abound. Similarly, all kinds of parts of mathematics serve as tools for other parts and for other sciences. Applying a simple rewriting rule to the quote on the right above one finds such statements as: 'One service topology has rendered mathematical physics ...'; 'One service logic has rendered com- puter science ...'; 'One service category theory has rendered mathematics ...'. All arguably true. And all statements obtainable this way form part of the raison d'!ltre of this series. Full Product DetailsAuthor: E. Goles , Servet MartínezPublisher: Springer Imprint: Springer Edition: Softcover reprint of the original 1st ed. 1990 Volume: 58 Dimensions: Width: 15.50cm , Height: 1.40cm , Length: 23.50cm Weight: 0.415kg ISBN: 9789401067249ISBN 10: 9401067244 Pages: 264 Publication Date: 21 September 2011 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of Contents1. Automata Networks.- 1.1. Introduction.- 1.2. Definitions Regarding Automata Networks.- 1.3. Cellular Automata.- 1.4. Complexity Results for Automata Networks.- 1.5. Neural Networks.- 1.6. Examples of Automata Networks.- 2. Algebraic Invariants on Neural Networks.- 2.1. Introduction.- 2.2. K-Chains in 0-1 Periodic Sequences.- 2.3. Covariance in Time.- 2.4. Algebraic Invariants of Synchronous Iteration on Neural Networks.- 2.5. Algebraic Invariants of Sequential Iteration on Neural Networks.- 2.6. Block Sequential Iteration on Neural Networks.- 2.7. Iteration with Memory.- 2.8. Synchronous Iteration on Majority Networks.- 3. Lyapunov Functionals Associated to Neural Networks.- 3.1. Introduction.- 3.2. Synchronous Iteration.- 3.3. Sequential Iteration.- 3.4. Tie Rules for Neural Networks.- 3.5. Antisymmetrical Neural Networks.- 3.6. A Class of Symmetric Networks with Exponential Transient Length for Synchronous Iteration.- 3.7. Exponential Transient Classes for Sequential Iteration.- 4. Uniform One and Two Dimensional Neural Networks.- 4.1. Introduction.- 4.2. One-Dimensional Majority Automata.- 4.3. Two-Dimensional Majority Cellular Automata.- 4.4. Non-Symmetric One-Dimensional Bounded Neural Networks.- 4.5. Two-Dimensional Bounded Neural Networks.- 5. Continuous and Cyclically Monotone Networks.- 5.1. Introduction.- 5.2. Positive Networks.- 5.3. Multithreshold Networks.- 5.4. Approximation of Continuous Networks by Multithreshold Networks.- 5.5. Cyclically Monotone Networks.- 5.6. Positive Definite Interactions. The Maximization Problem.- 5.7. Sequential Iteration for Decreasing Real Functions and Optimization Problems.- 5.8. A Generalized Dynamics.- 5.9. Chain-Symmetric Matrices.- 6. Applications on Thermodynamic Limits on the Bethe Lattice.- 6.1. Introduction.- 6.2.The Bethe Lattice.- 6.3. The Hamiltonian.- 6.4. Thermodynamic Limits of Gibbs Ensembles.- 6.5. Evolution Equations.- 6.6. The One-Site Distribution of the Thermodynamic Limits.- 6.7. Distribution of the Thermodynamic Limits.- 6.8.Period ? 2 Limit Orbits of Some Non Linear Dynamics on $$ \mathbb{R}_{ + }^{s} $$.- 7. Potts Automata.- 7.1. The Potts Model.- 7.2. Generalized Potts Hamiltonians and Compatible Rules.- 7.3. The Complexity of Synchronous Iteration on Compatible Rules.- 7.4. Solvable Classes for the Synchronous Update.- References.- Author and Subject Index.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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