Ebook: Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines

Many methods and models have been proposed for solving difficult problems such as prediction, planning and knowledge discovery in application areas such as bioinformatics, speech and image analysis. Most, however, are designed to deal with static processes which will not change over time. Some processes - such as speech, biological information and brain signals - are not static, however, and in these cases different models need to be used which can trace, and adapt to, the changes in the processes in an incremental, on-line mode, and often in real time. This book presents generic computational models and techniques that can be used for the development of evolving, adaptive modelling systems. The models and techniques used are connectionist-based (as the evolving brain is a highly suitable paradigm) and, where possible, existing connectionist models have been used and extended. The first part of the book covers methods and techniques, and the second focuses on applications in bioinformatics, brain study, speech, image, and multimodal systems. It also includes an extensive bibliography and an extended glossary. Evolving Connectionist Systems is aimed at anyone who is interested in developing adaptive models and systems to solve challenging real world problems in computing science or engineering. It will also be of interest to researchers and students in life sciences who are interested in finding out how information science and intelligent information processing methods can be applied to their domains.

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Many methods and models have been proposed for solving difficult problems such as prediction, planning and knowledge discovery in application areas such as bioinformatics, speech and image analysis. Most, however, are designed to deal with static processes which will not change over time. Some processes - such as speech, biological information and brain signals - are not static, however, and in these cases different models need to be used which can trace, and adapt to, the changes in the processes in an incremental, on-line mode, and often in real time. This book presents generic computational models and techniques that can be used for the development of evolving, adaptive modelling systems. The models and techniques used are connectionist-based (as the evolving brain is a highly suitable paradigm) and, where possible, existing connectionist models have been used and extended. The first part of the book covers methods and techniques, and the second focuses on applications in bioinformatics, brain study, speech, image, and multimodal systems. It also includes an extensive bibliography and an extended glossary. Evolving Connectionist Systems is aimed at anyone who is interested in developing adaptive models and systems to solve challenging real world problems in computing science or engineering. It will also be of interest to researchers and students in life sciences who are interested in finding out how information science and intelligent information processing methods can be applied to their domains.
Content:
Front Matter....Pages i-3
Front Matter....Pages 5-5
Evolving Processes and Evolving Connectionist Systems....Pages 7-30
Evolving Connectionist Systems for Unsupervised Learning....Pages 31-56
Evolving Connectionist Systems for Supervised Learning....Pages 57-89
Recurrent Evolving Systems, Reinforcement Learning and Evolving Automata....Pages 91-98
Evolving Neuro-Fuzzy Inference Systems....Pages 99-123
Evolutionary Computation and Evolving Connectionist Systems....Pages 125-141
Evolving Connectionist Machines — Framework, Biological Motivation and Implementation Issues....Pages 143-161
Front Matter....Pages 163-163
Data Analysis, Modelling and Knowledge Discovery in Bioinformatics....Pages 165-192
Dynamic Modelling of Brain Functions and Cognitive Processes....Pages 193-208
Modelling the Emergence of Acoustic Segments (Phonemes) in Spoken Languages....Pages 209-228
On-Line Adaptive Speech Recognition....Pages 229-244
On-Line Image and Video Data Processing....Pages 245-256
Evolving Systems for Integrated Multi-Modal Information Processing....Pages 257-271
Epilogue....Pages 273-273
Back Matter....Pages 275-307

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Many methods and models have been proposed for solving difficult problems such as prediction, planning and knowledge discovery in application areas such as bioinformatics, speech and image analysis. Most, however, are designed to deal with static processes which will not change over time. Some processes - such as speech, biological information and brain signals - are not static, however, and in these cases different models need to be used which can trace, and adapt to, the changes in the processes in an incremental, on-line mode, and often in real time. This book presents generic computational models and techniques that can be used for the development of evolving, adaptive modelling systems. The models and techniques used are connectionist-based (as the evolving brain is a highly suitable paradigm) and, where possible, existing connectionist models have been used and extended. The first part of the book covers methods and techniques, and the second focuses on applications in bioinformatics, brain study, speech, image, and multimodal systems. It also includes an extensive bibliography and an extended glossary. Evolving Connectionist Systems is aimed at anyone who is interested in developing adaptive models and systems to solve challenging real world problems in computing science or engineering. It will also be of interest to researchers and students in life sciences who are interested in finding out how information science and intelligent information processing methods can be applied to their domains.
Content:
Front Matter....Pages i-3
Front Matter....Pages 5-5
Evolving Processes and Evolving Connectionist Systems....Pages 7-30
Evolving Connectionist Systems for Unsupervised Learning....Pages 31-56
Evolving Connectionist Systems for Supervised Learning....Pages 57-89
Recurrent Evolving Systems, Reinforcement Learning and Evolving Automata....Pages 91-98
Evolving Neuro-Fuzzy Inference Systems....Pages 99-123
Evolutionary Computation and Evolving Connectionist Systems....Pages 125-141
Evolving Connectionist Machines — Framework, Biological Motivation and Implementation Issues....Pages 143-161
Front Matter....Pages 163-163
Data Analysis, Modelling and Knowledge Discovery in Bioinformatics....Pages 165-192
Dynamic Modelling of Brain Functions and Cognitive Processes....Pages 193-208
Modelling the Emergence of Acoustic Segments (Phonemes) in Spoken Languages....Pages 209-228
On-Line Adaptive Speech Recognition....Pages 229-244
On-Line Image and Video Data Processing....Pages 245-256
Evolving Systems for Integrated Multi-Modal Information Processing....Pages 257-271
Epilogue....Pages 273-273
Back Matter....Pages 275-307
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