Polystochastic Models for Complexity

by Octavian Iordache

Overview

This book is devoted to complexity understanding and management, considered as the main source of efficiency and prosperity for the next decades. Divided into six chapters, the book begins with a presentation of basic concepts as complexity, emergence and closure. The second chapter looks to methods and introduces polyshastic models, the wave equation, possibilities and entropy. The third chapter focusing on physical and chemical systems analyzes flow-sheet synthesis, cyclic operations of separation, drug delivery systems and entropy production. Biomimetic systems represent the main objective of the fourth chapter. Case studies refer to bio-inspired calculation methods, to the role of artificial genetic codes, neural networks and neural codes for evolutionary calculus and for evolvable circuits as biomimetic devices. The fifth chapter, taking its inspiration from systems sciences and cognitive sciences looks to engineering design, case base reasoning methods, failure analysis, and multi-agent manufacturing systems. Perspectives and integrative points of view are discussed in the sixth chapter with reference to the classification of sciences, cybernetics and its extensions, and to transdisciplinarity and categorification. Written for: engineers, researchers, and students in chemical, biochemical, computing and systems science engineering, in neuroscience, psychology, philosophy and mathematics

Product Details

ISBN-13:	9783642106538
Publisher:	Springer Berlin Heidelberg
Publication date:	04/19/2010
Series:	Understanding Complex Systems
Edition description:	2010
Pages:	298
Product dimensions:	6.20(w) x 9.30(h) x 1.00(d)

Table of Contents

1 Introduction 1

1.1 Complexity 1

1.2 Emergence 3

1.3 Evolvability 6

1.4 Closure and Circularity 8

1.4.1 General Concepts 8

1.4.2 Closure Paradigms 11

2 Methods 17

2.1 Polystochastic Models 17

2.1.1 Introduction to Polystochastic Models 17

2.1.2 Frameworks for Polystochastic Models 21

2.1.3 Higher Dimensional Modeling 26

2.2 Wave Equation 40

2.2.1 Frameworks for "Time" and "Space" 40

2.2.2 First Order Wave Equation 46

2.2.3 Kinetic Model 47

2.2.4 Convection Model 49

2.3 Possibilities 52

2.3.1 Similarities and Classification 52

2.3.2 Ultrametric Measures 54

2.4 Entropy 56

2.4.1 Informational Entropy 56

2.4.2 Informational Results 57

3 Physical and Chemical Systems 63

3.1 Flow-Sheet Synthesis 63

3.1.1 Flow-Sheet Generation 63

3.1.2 Methodology 66

3.1.3 Illustrative Examples 71

3.1.4 Differential Model 77

3.1.5 Transfer Function 80

3.1.6 Perspectives 82

3.2 Cyclic Operations of Separation 89

3.2.1 Cyclic Separations 89

3.2.2 Cyclic Separations Scheduling 91

3.2.3 Evolvability 102

3.2.4 Perspectives 110

3.3 Drug Delivery Systems 116

3.3.1 Complexity of Drug Delivery 116

3.3.2 Developments 118

3.3.3 Evolvable Systems 119

3.3.4 Perspectives 123

3.4 Entropy Production 125

3.4.1 Complexity Issues 125

3.4.2 Entropy Balance 126

3.4.3 Case Studies 129

3.4.4 Perspectives 133

4 Biosystems and Bioinspired Systems 141

4.1 Artificial Genetic Codes 141

4.1.1 Genetic Code Evolution 141

4.1.2 Model for Code Evolution 143

4.1.3 Codons and Amino Acids 147

4.1.4 Polypeptides 148

4.1.5 Basic Framework Evaluation 150

4.1.6 Perspectives 153

4.2 Artificial Neural Networks 157

4.2.1 Architecture Problem 157

4.2.2 Graph Generation Grammar 159

4.2.3 Cell Space Encoding 162

4.2.4 Perspectives 165

4.3 Artificial Neural Codes 170

4.3.1 Neural Coding 170

4.3.2 Symbolic Connectionist Hybrids 171

4.3.3 Temporal Synchrony 173

4.3.4 Perspectives 175

4.4 Evolvable Circuits 183

4.4.1 Evolutionary Circuits 183

4.4.2 Evolvable Circuits 190

4.4.3 Perspectives 199

5 Systems Sciences and Cognitive Systems 213

5.1 Evolvability for Engineering Design 213

5.1.1 Modeling Design Processes 213

5.1.2 Framework for Engineering Design 214

5.1.3 Multiple Scales Evolvable Designs 216

5.1.4 Schema for Multiple Scales 218

5.1.5 Perspectives 220

5.2 Case Based Reasoning 225

5.2.1 Case Based Reasoning Method 225

5.2.2 Case Based Reasoning Frameworks 228

5.2.3 Schema Modification 228

5.2.4 Multiple Scales 230

5.2.5 Schema for Multiple Scales 232

5.2.6 Perspectives 234

5.3 Failure Analysis 237

5.3.1 Complexity Challenges 237

5.3.2 Basic Framework for Diagnosis 238

5.3.3 Perspectives 239

5.4 Multi Agent Manufacturing Systems 241

5.4.1 Multi Agent Systems 241

5.4.2 Frameworks for Manufacturing Systems 243

5.4.3 Evolvable Manufacturing Systems 248

5.4.4 Belief Desire Intention Agents 252

5.4.5 Multiple Levels Cognitive Architecture 253

6 Perspectives 261

6.1 Cybernetics and Classification of Sciences 261

6.2 Transdisciplinarity 264

6.3 Synopsis 268

A Appendices 275

A.1 Categorical Framework 275

A.2 Higher Categories 277

A.3 Periodic Table 280

A.4 Categorification and Coherence 282

A.5 Computads or Polygraphs 285

A.6 Multicategories and Operads 287

A.7 Rewriting Systems 289

Index 295