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Principles of mathematical analysis

Walter Rudin
TLDR
The real and complex number system as discussed by the authors is a real number system where the real number is defined by a real function and the complex number is represented by a complex field of functions.
Abstract
Chapter 1: The Real and Complex Number Systems Introduction Ordered Sets Fields The Real Field The Extended Real Number System The Complex Field Euclidean Spaces Appendix Exercises Chapter 2: Basic Topology Finite, Countable, and Uncountable Sets Metric Spaces Compact Sets Perfect Sets Connected Sets Exercises Chapter 3: Numerical Sequences and Series Convergent Sequences Subsequences Cauchy Sequences Upper and Lower Limits Some Special Sequences Series Series of Nonnegative Terms The Number e The Root and Ratio Tests Power Series Summation by Parts Absolute Convergence Addition and Multiplication of Series Rearrangements Exercises Chapter 4: Continuity Limits of Functions Continuous Functions Continuity and Compactness Continuity and Connectedness Discontinuities Monotonic Functions Infinite Limits and Limits at Infinity Exercises Chapter 5: Differentiation The Derivative of a Real Function Mean Value Theorems The Continuity of Derivatives L'Hospital's Rule Derivatives of Higher-Order Taylor's Theorem Differentiation of Vector-valued Functions Exercises Chapter 6: The Riemann-Stieltjes Integral Definition and Existence of the Integral Properties of the Integral Integration and Differentiation Integration of Vector-valued Functions Rectifiable Curves Exercises Chapter 7: Sequences and Series of Functions Discussion of Main Problem Uniform Convergence Uniform Convergence and Continuity Uniform Convergence and Integration Uniform Convergence and Differentiation Equicontinuous Families of Functions The Stone-Weierstrass Theorem Exercises Chapter 8: Some Special Functions Power Series The Exponential and Logarithmic Functions The Trigonometric Functions The Algebraic Completeness of the Complex Field Fourier Series The Gamma Function Exercises Chapter 9: Functions of Several Variables Linear Transformations Differentiation The Contraction Principle The Inverse Function Theorem The Implicit Function Theorem The Rank Theorem Determinants Derivatives of Higher Order Differentiation of Integrals Exercises Chapter 10: Integration of Differential Forms Integration Primitive Mappings Partitions of Unity Change of Variables Differential Forms Simplexes and Chains Stokes' Theorem Closed Forms and Exact Forms Vector Analysis Exercises Chapter 11: The Lebesgue Theory Set Functions Construction of the Lebesgue Measure Measure Spaces Measurable Functions Simple Functions Integration Comparison with the Riemann Integral Integration of Complex Functions Functions of Class L2 Exercises Bibliography List of Special Symbols Index

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Journal ArticleDOI

Generalized predictive control using recurrent fuzzy neural networks for industrial processes

TL;DR: Both results from numerical simulations and experiments show that the proposed method is capable of controlling industrial processes with satisfactory performance under setpoint and load changes.
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Graph-optimized locality preserving projections

TL;DR: A novel DR algorithm called Graph-optimized Locality Preserving Projections (GoLPP) is developed, which is to integrate graph construction with specific DR process into a unified framework, which results in an optimized graph rather than predefined one.
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Mapping Process to Pattern in the Landscape Change of the Amazonian Frontier

TL;DR: In this paper, the authors address the land use and land-cover decisions of colonist farmers in the Amazon Basin as a function, in part, of household characteristics, and present a framework that integrates them into a model of forest dynamics at household level.
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On integration of fuzzy mappings

TL;DR: The concept of an integral of a fuzzy mapping is introduced that generalises the integral introduced by Matloka and also extends Riemann-Stieltjes integral over a closed interval to fuzzy mappings.
Journal ArticleDOI

How many parameters can a model have and still be testable

TL;DR: In this article, the rank of the Jacobian matrix of a model is defined as a measure of the probability that a model's predictions have zero probability of being correct by chance.