By Derong Liu, Fei-Yue Wang
Computational Intelligence (CI) is a lately rising zone in basic and utilized study, exploiting a few complicated details processing applied sciences that in most cases include neural networks, fuzzy common sense and evolutionary computation. With a tremendous hindrance to exploiting the tolerance for imperfection, uncertainty, and partial fact to accomplish tractability, robustness and occasional answer rate, it turns into obtrusive that composing tools of CI might be operating simultaneously instead of individually. it really is this conviction that learn at the synergism of CI paradigms has skilled major development within the final decade with a few parts nearing adulthood whereas many others closing unresolved. This publication systematically summarizes the newest findings and sheds mild at the respective fields that will result in destiny breakthroughs.
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Extra resources for Advances in Computational Intelligence: Theory And Applications
The organization of this chapter is as follows. -Y. Wang mapping are constructed. 3, the structure, numerical procedure and existence of fixed-points of type-II LDS are discussed. 4, the LDS controller design principles for controlling type-II LDS are addressed. 5, the chapter is concluded with remarks for future works. 2 "type-I Linguistic Dynamic Systems The procedure of converting a conventional dynamic system into a type-I LDS is called abstracting process, namely, extracting linguistic dynamic models in words from conventional dynamic models in numbers.
The evolving laws of a type-I LDS are constructed by applying the fuzzy extension principle to those of its conventional counterpart with linguistic states. The evolution of type-I LDS represents the dynamics of state uncertainty derived from the corresponding conventional dynamic process. In addition to linguistic states, the evolving laws of type-II LDS are modeled by a finite number of linguistic decision rules. Analysis of fixed points is conducted based on point-to-fuzzy-set mappings and linguistic controllers are designed for goals specified in words for type-II LDS.
Second, we would like to assure a high level of interpretability which is the case here: evidently each neuron comes with a well-defined semantics and our intent is to retain it so at the very end the network can be easily mapped (translated) into a well-structured and transparent logic expression. This quest for interpretability and transparency has been clearly identified and strongly promoted in the most recent literature, cf. ; refer also to [5, 6, 13, 20, 23] for additional issues raised with this regard.