Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent MachinesSpringer Science & Business Media, 2003 - 307 頁 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. |
內容
Prologue | 1 |
Evolving Processes and Evolving Connectionist Systems | 7 |
Evolving Connectionist Systems for Unsupervised Learning | 31 |
Evolving Connectionist Systems for Supervised Learning | 57 |
Recurrent Evolving Systems Reinforcement Learning | 91 |
Evolving NeuroFuzzy Inference Systems | 99 |
Evolutionary Computation and Evolving Connectionist | 125 |
Evolving Connectionist Machines Framework Biological | 143 |
Modelling the Emergence of Acoustic Segments Phonemes | 209 |
OnLine Adaptive Speech Recognition | 229 |
Recognition | 237 |
OnLine Image and Video Data Processing | 245 |
Evolving Systems for Integrated MultiModal Information | 257 |
Epilogue | 273 |
Extended Glossary | 291 |
305 | |
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activation adaptive aggregation algorithm analysis applied associated auditory brain calculated cell Chapter classification cluster cluster centres complex connections consists continuous created data set defined DENFIS depending distance dynamic ECOS EFuNN English error ESOM evolving evolving connectionist systems example existing experiment extracted Figure frames function further fuzzy rules genes genetic given human individual inference initial input input vector intelligent Kasabov knowledge language layer learning machines means measure membership functions methods mode modules neural networks neurons off-line on-line on-line learning operation optimisation output parameters patterns performance phoneme points prediction presented principle problem procedure proteins receptive field represent representation require rule nodes samples selection sequence shown shows signal similar space spoken Step structure Table task techniques threshold tion types values variables vector visual weights