不连贯 发表于 2025-3-23 11:09:32
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f standard optimization.Comprehensive consideration of uncersections dealing with fuzzy functions and fuzzy random functions are certain to be of special interest. The reader is expected to be in command of the knowledge gained in a basic university mathematics course, with the inclusion of stochast一瞥 发表于 2025-3-23 23:13:08
Thomas Gries,Gernot Sieg,Holger Strulikiven by perception formation of knowledge about goals and objectives, and about costs and benefits of competing decision-choice elements in the action space. This perception formation takes place against a foundation of socially accepted knowledge, the social background as well as depends on the perOATH 发表于 2025-3-24 03:03:30
Thomas Gries,Gernot Sieg,Holger Strulikreason. Within this context of knowledge construction, rationality is seen as a guide to cognitive activities at the level of critical deliberations. Here rationality is viewed in terms of decision-choice process in operating on the best cognitive path of scientific practice that maximizes the possi会犯错误 发表于 2025-3-24 09:42:00
Thomas Gries,Gernot Sieg,Holger Strulikrecise. Fuzzy logic resembles human reasoning in its use of imprecise informa tion to generate decisions. Unlike classical logic which requires a deep under standing of a system, exact equations, and precise numeric values, fuzzy logic incorporates an alternative way of thinking, which allows mode泥沼 发表于 2025-3-24 14:27:54
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Thomas Gries,Gernot Sieg,Holger Strulikrsity of control tasks to be handled, it became obvious that the concept of this numerical platform mastered so far cannot be sufficient in many situations. Some techniques of Artificial Intelligence (such as e.g., control expert systems) on one hand, and nonparametric parallel optimization (i.e. st无法破译 发表于 2025-3-24 21:58:00
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his real fuzzy world. In the application of these conepts to knowledge engineering, there are two problems, i.e., learning of fuzzy knowledge and automatic inference of unknown variables. On the, other hand, one can identify any complex complex functions by means or perceptron-type neural networks x