Evolving Connectionist Systems: The Knowledge Engineering ApproachSpringer Science & Business Media, 2007年8月23日 - 451 頁 This second edition of the must-read work in the field presents generic computational models and techniques that can be used for the development of evolving, adaptive modeling systems, as well as new trends including computational neuro-genetic modeling and quantum information processing related to evolving systems. New applications, such as autonomous robots, adaptive artificial life systems and adaptive decision support systems are also covered. |
內容
| 1 | |
1 | 13 |
7 | 48 |
1 | 83 |
8 | 91 |
3 | 97 |
Knowledge Manipulation in Evolving Fuzzy Neural | 109 |
6 | 125 |
Modelling the Emergence of Acoustic Segments in Spoken Languages | 303 |
Evolving Intelligent Systems for Adaptive Speech Recognition | 325 |
Evolving Intelligent Systems for Adaptive Image Processing | 341 |
13 | 373 |
Evolving Intelligent Systems for Robotics and Decision Support | 381 |
21 | 386 |
Quantum Inspired Evolving Intelligent Systems? | 393 |
Appendix A A Sample Program in MATLAB for TimeSeries Analysis | 405 |
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常見字詞
accuracy activation aggregation algorithm analysis applied auditory and visual bioinformatics brain calculated cell Chapter classification cluster centres clustering method coefficients colour computational connection weights created dataset defined DENFIS distance dynamic ECOS EFuNN model ensemble error ESOM evolving clustering evolving fuzzy frequency fuzzy neural network fuzzy rules gene expression gene expression data gene regulatory network genetic global hypersphere incrementally adaptive information processing input space input variables input vector integrated Kasabov KBNN knowledge language layer membership functions microRNAs mode module neuro-fuzzy neurons normalised number of rule off-line optimisation output parameters patterns performance phoneme prediction presented problem space profiles proteins quantisation radius receptive field represented rule extraction rule nodes samples sequence shown signal speech recognition spiking spiking neural network structure subsystem supervised learning task techniques threshold transductive updated values visual information words