Artificial Intelligence (AI) is the branch of Computer Science which is concerned with making computers behave like human beings. Currently no computers exhibit full AI but significant advances have taken place recently in the areas of games playing (e.g., the chess programs such as Deep Blue which are capable of beating human players). Neural Networks is the hottest area of AI at the moment and this book looks at how this and related techniques can be used in applications in areas such as speech recognition.

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.

I Evolving Connectionist Systems: Methods and Techniques.- 1 Evolving Processes and Evolving Connectionist Systems.- 2 Evolving Connectionist Systems for Unsupervised Learning.- 3 Evolving Connectionist Systems for Supervised Learning.- 4 Recurrent Evolving Systems, Reinforcement Learning and Evolving Automata.- 5 Evolving Neuro-Fuzzy Inference Systems.- 6 Evolutionary Computation and Evolving Connectionist Systems.- 7 Evolving Connectionist Machines - Framework, Biological Motivation and Implementation Issues.- II Evolving Connectionist Systems: Applications in Bioinformatics, Brain Study, and Intelligent Systems.- 8 Data Analysis, Modelling and Knowledge Discovery in Bioinformatics.- 9 Dynamic Modelling of Brain Functions and Cognitive Processes.- 10 Modelling the Emergence of Acoustic Segments (Phonemes) in Spoken Languages.- 11 On-Line Adaptive Speech Recognition.- 12 On-Line Image and Video Data Processing.- 13 Evolving Systems for Integrated Multi-Modal Information Processing.- Epilogue.- References.- Extended Glossary.