Titlebook: ;

T-cell

書(shū)目名稱Granular Computing影響因子(影響力)




書(shū)目名稱Granular Computing影響因子(影響力)學(xué)科排名




書(shū)目名稱Granular Computing網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Granular Computing網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Granular Computing被引頻次




書(shū)目名稱Granular Computing被引頻次學(xué)科排名




書(shū)目名稱Granular Computing年度引用




書(shū)目名稱Granular Computing年度引用學(xué)科排名




書(shū)目名稱Granular Computing讀者反饋




書(shū)目名稱Granular Computing讀者反饋學(xué)科排名

減震

瑣事

B. Boeryd,J. Eldh,L.-E. Peterson, which we prove forms a vector space. The node functions of the linguistic neural network are defined in terms of this linguistic arithmetic. The learning method used for the network is a modified Backpropagation algorithm, with the original arithmetic operations replaced by their linguistic equiva

Adrenaline

U. M. Lütolf,P. Huguenin,C. Glanzmannbols may be compared to the importance of the symbols alone. Due to complexity of music nature and music notation, music representation, necessary to store and reuse recognised information, is also the key issue in music notation recognition and music processing. Both: the data representation and th

Accord

Interval and Ellipsoidal Uncertainty Models,ntation to the extent of prior knowledge. The relative merits of interval and ellipsoidal representations of noise are discussed in a set theoretic setting and are illustrated using both a simple synthetic example and a real-life scenario of state estimation of a water distribution system.

Palpitation

Rough Sets and Boolean Reasoning,soning is crucial for all the discussed methods..We also present an overview of some extensions of the classical rough set approach. Among them is rough mereology developed as a tool for synthesis of objects satisfying a given specification in a satisfactory degree. Applications of rough mereology i

Palpitation

Granular Computing in Neural Networks,, which we prove forms a vector space. The node functions of the linguistic neural network are defined in terms of this linguistic arithmetic. The learning method used for the network is a modified Backpropagation algorithm, with the original arithmetic operations replaced by their linguistic equiva

賭博

Eulogy

硬化

Targeted Therapien und Immuntherapie,aining data. The trade-offs between granularity, specificity, interpretability, and efficiency are examined for rule-bases produced in each of these manners. An algorithm that combines rule learning with region merging is introduced to incorporate beneficial features of both the heuristic and learning approaches to producing fuzzy models.