Applying Computational Intelligence: How to Create Value / Edition 1

by Arthur Kordon

Overview

In theory, there is no difference between theory and practice. But, in practice, there is. Jan L. A. van de Snepscheut The flow of academic ideas in the area of computational intelligence has penetrated industry with tremendous speed and persistence. Thousands of applications have proved the practical potential of fuzzy logic, neural networks, evolutionary com- tation, swarm intelligence, and intelligent agents even before their theoretical foundation is completely understood. And the popularity is rising. Some software vendors have pronounced the new machine learning gold rush to “Transfer Data into Gold”. New buzzwords like “data mining”, “genetic algorithms”, and “swarm optimization” have enriched the top executives’ vocabulary to make them look more “visionary” for the 21st century. The phrase “fuzzy math” became political jargon after being used by US President George W. Bush in one of the election debates in the campaign in 2000. Even process operators are discussing the perf- mance of neural networks with the same passion as the performance of the Dallas Cowboys. However, for most of the engineers and scientists introducing computational intelligence technologies into practice, looking at the growing number of new approaches, and understanding their theoretical principles and potential for value creation becomes a more and more difficult task.

Product Details

ISBN-13:	9783540699101
Publisher:	Springer Berlin Heidelberg
Publication date:	12/10/2009
Edition description:	2010
Pages:	459
Product dimensions:	6.20(w) x 9.10(h) x 1.60(d)

About the Author

Arthur K. Kordon is a Data Mining and Modeling Leader in the Data Mining and Modeling Capability of The Dow Chemical Company. He is an internationally recognized expert in applying emerging technologies in industry, and has given talks and chaired panels on the topic at the major computational intelligence conferences such as WCCI and GECCO. He has successfully introduced several novel technologies for improved manufacturing and new product design in the chemical industry, and his research interests include application issues of computational intelligence, robust empirical modeling, intelligent process monitoring and control, and data mining.

Table of Contents

Part I Computational Intelligence in a Nutshell

1 Artificial vs. Computational Intelligence 3

1.1 Artificial Intelligence: The Pioneer 4

1.2 Computational Intelligence: The Successor 22

1.3 Key Differences Between AI and CI 27

1.4 Summary 29

Suggested Reading 30

2 A Roadmap Through the Computational Intelligence Maze 31

2.1 Strengths and Weaknesses of CI Approaches 31

2.2 Key Scientific Principles of Computational Intelligence 42

2.3 Key Application Areas of Computational Intelligence 45

2.4 Summary 50

Suggested Reading 50

3 Let's Get Fuzzy 51

3.1 Fuzzy Systems in a Nutshell 51

3.2 Benefits of Fuzzy Systems 60

3.3 Fuzzy Systems Issues 62

3.4 How to Apply Fuzzy Systems 63

3.5 Typical Applications of Fuzzy Systems 65

3.6 Fuzzy Systems Marketing 68

3.7 Available Resources for Fuzzy Systems 70

3.8 Summary 71

Suggested Reading 72

4 Machine Learning: The Ghost in the Learning Machine 73

4.1 Neural Networks in a Nutshell 76

4.2 Support Vector Machines in a Nutshell 84

4.3 Benefits of Machine learning 91

4.4 Machine Learning Issues 96

4.5 How to Apply Machine learning Systems 97

4.6 Typical Machine Learning Applications 105

4.7 Machine Learning Marketing 108

4.8 Available Resources for Machine Learning 111

4.9 Summary 112

Suggested Reading 113

5 Evolutionary Computation: The Profitable Gene 115

5.1 Evolutionary Computation in a Nutshell 116

5.2 Benefits of Evolutionary Computation 128

5.3 Evolutionary Computation Issues 130

5.4 How to Apply Evolutionary Computation 130

5.5 Typical Applications of Evolutionary Computation 136

5.6 Evolutionary Computation Marketing 141

5.7 Available Resources for Evolutionary Computation 142

5.8 Summary 143

Suggested Reading 144

6 Swarm Intelligence: The Benefits of Swarms 145

6.1 Swarm Intelligence in a Nutshell 146

6.2 Benefits of Swarm Intelligence 157

6.3 Swarm Intelligence Issues 159

6.4 How to Apply Swarm Intelligence 160

6.5 Typical Swarm Intelligence Applications 166

6.6 Swarm Intelligence Marketing 171

6.7 Available Resources for Swarm Intelligence 173

6.8 Summary 173

Suggested Reading 174

7 Intelligent Agents: The Computer Intelligence Agency (CIA) 175

7.1 Intelligent Agents in a Nutshell 176

7.2 Benefits of Intelligent Agents 186

7.3 Intelligent Agents Issues 189

7.4 How to Apply Intelligent Agents 190

7.5 Typical Applications of Intelligent Agents 193

7.6 Intelligent Agents Marketing 196

7.7 Available Resources for Intelligent Agents 199

7.8 Summary 199

Suggested Reading 200

Part II Computational Intelligence Creates Value

8 Why We Need Intelligent Solutions 203

8.1 Beat Competition 204

8.2 Accelerate Innovations 207

8.3 Produce Efficiently 210

8.4 Distribute Effectively 212

8.5 Impress Customers 215

8.6 Enhance Creativity 217

8.7 Attract Investors 220

8.8 Improve National Defense 222

8.9 Protect Health 225

8.10 Have Fun 228

8.11 Summary 231

Suggested Reading 231

9 Competitive Advantages of Computational Intelligence 233

9.1 Competitive Advantage of a Research Approach 233

9.2 Key Competitive Approaches to Computational Intelligence 237

9.3 How Computational Intelligence Beats the Competition 247

9.4 Summary 255

Suggested Reading 256

10 Issues in Applying Computational Intelligence 257

10.1 Technology Risks 257

10.2 Modeling Fatigue 261

10.3 Looks Too Academic 263

10.4 Perception of High Cost 265

10.5 Missing Infrastructure 267

10.6 No Marketing 269

10.7 Wrong Expectations 271

10.8 No Application Methodology 274

10.9 Summary 275

Suggested Reading 276

Part III Computational Intelligence Application Strategy

11 Integrate and Conquer 279

11.1 The Nasty Reality of Real-World Applications 280

11.2 Requirements for Successful Real-World Applications 282

11.3 Why Integration Is Critical for Real-World Applications 284

11.4 Integration Opportunities 287

11.5 Integrated Methodology for Robust Empirical Modeling 294

11.6 Integrated Methodology in Action 301

11.7 Summary 309

Suggested Reading 309

12 How to Apply Computational Intelligence 311

12.1 When Is Computational Intelligence the Right Solution? 311

12.2 Obstacles in Applying Computational Intelligence 313

12.3 Methodology for Applying CI in a Business 316

12.4 Computational Intelligence Project Management 322

12.5 CI for Six Sigma and Design for Six Sigma 331

12.6 Summary 340

Suggested Reading 341

13 Computational Intelligence Marketing 343

13.1 Research Marketing Principles 343

13.2 Techniques - Delivery, Visualization, Humor 348

13.3 Interactions Between Academia and Industry 359

13.4 Marketing CI to a Technical Audience 363

13.5 Marketing to a Nontechnical Audience 369

13.6 Summary 372

Suggested Reading 373

14 Industrial Applications of Computational Intelligence 375

14.1 Applications in Manufacturing 375

14.2 Applications in New Product Development 390

14.3 Unsuccessful Computational Intelligence Applications 401

14.4 Acknowledgements 403

14.5 Summary 403

Suggested Reading 403

Part IV The Future of Computational Intelligence

15 Future Directions of Applied Computational Intelligence 407

15.1 Supply-Demand-Driven Applied Research 407

15.2 Next-Generation on Applied Computational Intelligence 413

15.3 Projected Industrial Needs 425

15.4 Sustainability of Applied Computational Intelligence 431

15.5 Summary 433

Suggested Reading 434

Glossary 435

Index 447

Editorial Reviews

This book demonstrates profound understanding regarding the problems and issues associated with technology transfer by bridging two complex worlds: the world of advanced theory of computational intelligence, soft computing, and cybernetics and the world of engineering methodologies, practices, and industrial applications." Dimitar Filev, Ford Research & Advanced Engineering, Dearborn, Michigan, USA

"Kordon comprehensively analyses how to apply computational intelligence to give real return on investment. He devotes complete chapters to comparing CI to traditional approaches and to discussing in what sense CI has a 'competitive advantage'. ... This book is particularly designed for non-expert readers interested in computational intelligence and its applications. Moreover, parts II and III are essential for academic readers without industrial experience. These parts bring together a novel, broad, and useful perspective on the marketing of CI. This will certainly help researchers to design successful strategies to develop and deploy industrial applications, as well as improving presentations to both academic and industrial audiences. ... [The book] represents a great contribution that shows us alternative ways of transferring computational intelligence from the realm of exciting research ideas into a highly competitive generator of intelligent and valuable solutions to real-world problems". (G. Leguizamón, Genetic Programming and Evolvable Machines (2011) 12:85–86)

"[The author] has enjoyed significant success as an industry leader in computational intelligence and with this book he has fulfilled a long-standing need for a pragmatic treatment of the field. From checklists and prescriptions for application development to advice for overcoming cultural barriers and gaining management buy-in, Applying Computational Intelligence effectively addresses the value creation challenge posed in its subtitle." (TariqSamad, Honeywell Automation and Control Solutions)

From the Publisher