By Derong Liu, Fei-Yue Wang
Computational Intelligence (CI) is a lately rising quarter in primary and utilized examine, exploiting a couple of complicated info processing applied sciences that in most cases embrace neural networks, fuzzy common sense and evolutionary computation. With a tremendous obstacle to exploiting the tolerance for imperfection, uncertainty, and partial fact to accomplish tractability, robustness and occasional answer price, it turns into obvious that composing tools of CI may be operating at the same time instead of individually. it truly is this conviction that examine at the synergism of CI paradigms has skilled major progress within the final decade with a few parts nearing adulthood whereas many others last unresolved. This booklet systematically summarizes the newest findings and sheds gentle at the respective fields that would result in destiny breakthroughs.
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Additional info for Advances in computational intelligence: theory & applications
A Quest for Granular Computing and Logic Processing With the design of the interfaces, we exercise two general approaches (in the literature we encounter far more diversified techniques but they are usually more specialized in the sense of the underlying computing mechanisms): 1. Granulation of individual variables This mechanism of granulation is quite common in the realm of fuzzy modeling. In essence, we define several fuzzy sets in the universe of discourse of the variable of interest so that any input is transformed via the membership functions defined there and the resulting membership grades are used in further computations by the model.
14] S. K. Pal and S. Mitra, Neuro-Fuzzy Pattern Recognition, New York: John Wiley, 1999.  Z. Pawlak, Rough Sets-Theoretical Aspects of Reasoning About Data, Dordercht: Kluwer Academic Publishers, 1991.  W. Pedrycz, "Neurocomputations in relational systems," IEEE Trans, on Pattern Analysis and Machine Intelligence, vol. 13, pp. 289-297,1991.  W Pedrycz, "Fuzzy neural networks and neurocomputations," Fuzzy Sets and Systems, vol. 56, pp. 1-28,1993.  W Pedrycz and A. Rocha, "Knowledge-based neural networks," IEEE Trans, on Fuzzy Systems, vol.
Pedrycz The intuitive form of this predicate is self-evident: the statement "x is included in a" and modeled as INCL(a;, a) — x —> a comes with the truth value equal to 1 if a; is less or equal to a (which in other words means that x is included in a) and produces lower truth values once x starts exceeding the truth values of "a". Higher values of "a;" (those above the reference point "o") start generating lower truth values of the predicate. The dominance predicate acts in a dual manner. It returns 1 once "x" dominates "a" (so that its values exceeds "a") and values below 1 for x lower than the given threshold.
Advances in computational intelligence: theory & applications by Derong Liu, Fei-Yue Wang