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A Guide to Monte Carlo Simulations in Statistical Physics ISBN13：9781108490146 出版社：Cambridge Univ Pr 作者：David Landau 裝訂／頁數：精裝／578頁 版次：5 出版日：2021/02/28
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A Guide to Monte Carlo Simulations in Statistical Physics (5版)
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詳細資訊
A Guide to Monte Carlo Simulations in Statistical Physics ISBN13：9781108490146 出版社：Cambridge Univ Pr 作者：David Landau 裝訂／頁數：精裝／578頁 版次：5 出版日：2021/02/28
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This new and updated edition deals with all aspects of Monte Carlo simulation of complex physical systems encountered in condensed-matter physics, statistical mechanics, and related fields. After briefly recalling essential background in statistical mechanics and probability theory, it gives a succinct overview of simple sampling methods. The concepts behind the simulation algorithms are explained comprehensively, as are the techniques for efficient evaluation of system configurations generated by simulation. It contains many applications, examples, and exercises to help the reader and provides many new references to more specialized literature. This edition includes a brief overview of other methods of computer simulation and an outlook for the use of Monte Carlo simulations in disciplines beyond physics. This is an excellent guide for graduate students and researchers who use computer simulations in their research. It can be used as a textbook for graduate courses on computer simulations in physics and related disciplines.
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【簡介】 What are the principles behind intelligent systems? How are they built? What are intelligent systems useful for? How do we choose the right tool for the job? These questions are answered by Michael Negnevitsky’s Artificial Intelligence: A Guide to Intelligent Systems. Unlike many books on computer intelligence, which use complex computer science terminology and are crowded with complex matrix algebra and differential equations, this text demonstrates that the ideas behind intelligent systems are simple and straightforward. This text assumes little or no programming experience as it tackles topics like expert systems, fuzzy systems, artificial neural networks, evolutionary computation, knowledge engineering, and data mining. 【目錄】 Introduction to Intelligent Systems 1.1 Intelligent Machines, or What Machines Can Do 1.2 The History of Artificial Intelligence, or From the ‘Dark Ages’ to Knowledge-based Systems 1.3 Generative AI 1.4 Summary Questions for Review References Expert Systems 2.1 Introduction, or Knowledge Representation Using Rules 2.2 The Main Players in the Expert System Development Team 2.3 Structure of a Rule-based Expert System 2.4 Fundamental characteristics of an expert system 2.5 Forward Chaining and Backward Chaining Inference Techniques 2.6 MEDIA ADVISOR: A Demonstration Rule-based Expert System 2.7 Conflict Resolution 2.8 Uncertainty Management in Rule-based Expert Systems 2.9 Advantages and Disadvantages of Rule-based Expert systems 2.10 Summary Questions for Review References Fuzzy Systems 3.1 Introduction, or What Is Fuzzy Thinking? 3.2 Fuzzy Sets 3.3 Linguistic Variables and Hedges 3.4 Operations of Fuzzy Sets 3.6 Fuzzy Inference 3.7 Building a Fuzzy Expert System 3.8 Summary Questions for Review References Frame-based Systems and Semantic Networks 4.1 Introduction, or What Is a Frame? 4.2 Frames as a Knowledge Representation Technique 4.3 Inheritance in Frame-based Systems 4.4 Methods and Demons 4.5 Interaction of Frames and Rules 4.6 Buy Smart: A Frame-based Expert System 4.7 The Web of Data 4.8 RDF – Resource Description Framework and RDF Triples 4.9 Turtle, RDF Schema and OWL 4.10 Querying the Semantic Web with SPARQL 4.11 Summary Questions for Review References Artificial Neural Networks 5.1 Introduction, or How the Brain Works 5.2 The Neuron as a Simple Computing Element 5.3 The Perceptron 5.4 Multilayer Neural Networks 5.5 Accelerated Learning in Multilayer Neural Networks 5.6 The Hopfield Network 5.7 Bidirectional Associative Memory 5.8 Self-organising Neural Networks 5.9 Reinforcement Learning 5.10 Summary Questions for Review References Deep Learning and Convolutional Neural Networks 6.1 Introduction, or How “Deep” Is a Deep Neural Network? 6.2 Image Recognition or How Machines See the World 6.3 Convolution in Machine Learning 6.4 Activation Functions in Deep Neural Networks 6.5 Convolutional Neural Networks 6.6 Back-propagation Learning in Convolutional Networks 6.7 Batch Normalisation 6.8 Summary Questions for Review References Evolutionary Computation 7.1 Introduction, or Can Evolution Be Intelligent? 7.2 Simulation of Natural Evolution 7.3 Genetic Algorithms 7.4 Why Genetic Algorithms Work 7.5 Maintenance Scheduling with Genetic Algorithms 7.6 Genetic Programming 7.7 Evolution Strategies 7.8 Ant Colony Optimisation 7.9 Particle Swarm Optimisation 7.10 Summary Questions for Review References Hybrid Intelligent Systems 8.1 Introduction, or How to Combine German Mechanics with Italian Love 8.2 Neural Expert Systems 8.3 Neuro-Fuzzy Systems 8.4 ANFIS: Adaptive Neuro-Fuzzy Inference System 8.5 Evolutionary Neural Networks 8.6 Fuzzy Evolutionary Systems 8.7 Summary Questions for Review References Knowledge Engineering 9.1 Introduction, or What Is Knowledge Engineering? 9.2 Will an Expert System Work for My Problem? 9.3 Will a Fuzzy Expert System Work for My Problem? 9.4 Will a Neural Network Work for My Problem? 9.5 Will a Deep Neural Network Work for My Problem? 9.6 Will Genetic Algorithms Work for My Problem? 9.7 Will Particle Swarm Optimisation Work for My Problem? 9.8 Will a Hybrid Intelligent System Work for My Problem? 9.9 Summary Questions for Review References Data Mining and Knowledge Discovery 10.1 Introduction, or What Is Data Mining? 10.2 Statistical Methods and Data Visualisation 10.3 Principal Components Analysis 10.4 Relational Databases and Database Queries 10.5 The Data Warehouse and Multidimensional Data Analysis 10.6 Decision Trees 10.7 Association Rules and Market Basket Analysis 10.8 Summary Questions for Review References Glossary Index
