Evolving Connectionist Systems: Methods & Applications in Bioinformatics, Brain Study & Intelligent Machines
By Kasabov, N., Auckland University of Technology, Auckland, New Zealand
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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ECOS Toolbox for Matlab The Matlab toolbox includes three major modules:
ECOS modules are now part of the project "NeuCom". For more information, please visit the NeuCom homepage.
Selected papers on ECOS
2003 - Kasabov, N., Data Mining and Knowledge Discovery Using Adaptive Neural Networks, Tutorial, IJCNN'03, Portland, USA
2003 - Kasabov, N., Goh, L., NeuCom - Environment for teaching and research in Bioinformatics, ISBM'2003, Brisbane, Australia
2002-Kasabov, N., and Song, Q., DENFIS: Dynamic Evolving Neural-Fuzzy Inference System and its Application for Time Series Prediction, IEEE Transactions on Fuzzy Systems, vol. 10, no.2, April, (2002) 144-154.
2001 - Kasabov, N., Evolving Fuzzy Neural Networks for Supervised/Unsupervised On-Line, Knowledge-Based Learning, IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, Vol 31, No. 6 Issue (December 2001, pp.902-918)
2001 - Kasabov, N., Artificial Neural Networks for Intelligent Information Processing, Transactions of Chemical Engineering, London, June 2001, 27:28
2001 - Kasabov, N. On-line learning, reasoning, rule extraction and aggregation in locally optimised evolving fuzzy neural networks, Neurocomputing, 41 (2001) 25-41
1999 - Kasabov, N . Evolving connectionist and fuzzy connectionist systems – theory and applications for adaptive, on-line intelligent systems, In: Neuro-Fuzzy Techniques for Intelligent Information Processing, N. Kasabov and R. Kozma (eds.), Heidelberg Physica Verlag.
1999 - Kasabov, N. . Evolving connectionist and fuzzy connectionist systems for on-line adaptive decision making and control, In: Advances in Soft Computing - Engineering Design and Manufacturing, R. Roy, T. Furuhashi and P.K. Chawdhry (Eds.) Springer-Verlag.
1999 - Kasabov, N. . Evolving connectionist systems for fast identification, classification and decision making, Australian Journal of Intelligent Information Processing Systems.
1998 - Kasabov, N. . The ECOS framework and the 'eco' training method for evolving connectionist systems, Journal of Advanced Computational Intelligence, vol.2, No.6, 1-8.
ECOS Power Point Presentation
2000 - Nikola Kasabov. Evolving Connectionist Systems: Methods, Techniques, Applications, IJCNN'2000 Tutorial.