Titlebook: Advanced Fuzzy Systems Design and Applications; Yaochu Jin Book 2003 Springer-Verlag Berlin Heidelberg 2003 algorithms.artificial neural n

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書目名稱Advanced Fuzzy Systems Design and Applications影響因子(影響力)




書目名稱Advanced Fuzzy Systems Design and Applications影響因子(影響力)學(xué)科排名




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書目名稱Advanced Fuzzy Systems Design and Applications網(wǎng)絡(luò)公開度學(xué)科排名




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書目名稱Advanced Fuzzy Systems Design and Applications被引頻次學(xué)科排名




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書目名稱Advanced Fuzzy Systems Design and Applications讀者反饋學(xué)科排名

飾帶

Evolutionary Algorithms,gs, and between retained and distributed profits. The aim was to create equality and to use the tax regime as part of a ‘low effort bargain‘ that balanced wage restraint and low inflation against modest dividends. The tax regime favoured long-term, prudential savings in life insurance policies and p

貨物

Artificial Neural Networks,nt and historical economic issues.Addresses the problems of Since the end of World War II, the Japanese economy has seen rapid changes and remarkable progress. It has also experienced a bubble economy and period of prolonged stagnation. The book seeks to address three major questions: What kind of c

和諧

單純

Fuzzy Knowledge Incorporation into Neural Networks,Atlantic Forest, with some exotic plant elements. Reforestation activities and conservationism require knowledge about pollination and seed production among native trees and shrubs adapted to local soil conditions. Besides other insects, birds, and bats, native Meliponini are significant pollinators

淘氣

Fuzzy Preferences Incorporation into Multi-objective Optimization,st geological epochs from the Cambrian to the present (Sonnenfeld 1985). The predominant mineral form is sylvite (KCl), found in almost every deposit with halite (NaCl) to form the mixture called sylvinite. In most occurrences fairly pure sylvinite exists with essentially no soluble sulfate or other

古文字學(xué)

忘恩負(fù)義的人

DEFT

https://doi.org/10.1007/978-3-663-13763-4e Knowledge through a process of learning and the acquired knowledge is stored in distribution among nodes and weights. Mathematically, most feed-forward neural networks are linear or non-linear function mapping systems that can perform function approximation (regression) and classification.

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