There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

物件導向軟體之架構(Object-Oriented Software Construction)探討

Mathematica--A System for Doing Mathematics by Computer.

Artificial intelligence techniques for photovoltaic applications: A review

The author presents a summary of the basic concepts and techniques underlying the application of fuzzy logic to knowledge representation. He then describes a number of examples relating to its use as a computational system for dealing with uncertainty and imprecision in the context of knowledge, meaning, and inference. It is noted that one of the basic aims of fuzzy logic is to provide a computational framework for knowledge representation and inference in an environment of uncertainty and imprecision. In such environments, fuzzy logic is effective when the solutions need not be precise and/or it is acceptable for a conclusion to have a dispositional rather than categorical validity. The importance of fuzzy logic derives from the fact that there are many real-world applications which fit these conditions, especially in the realm of knowledge-based systems for decision-making and control. >

Knowledge representation in fuzzy logic

From the Publisher:
Presents a theory of uncertainty, consistent with and combining the theories of probability and fuzzy sets. Extends the logic programming form of knowledge representation and method of inference to permit the inclusion of uncertainties such as probabilistic knowledge and fuzzy incompleteness. Describes the application to general areas of knowledge engineering including expert and decision-support systems, evidential and case-based reasoning, fuzzy control and databases. An accompanying disk for Macintosh and one for the IBM PC enables readers to implement the examples while following the text.

Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence

A mass assignment theory of the probability of fuzzy events

The implementation of fprolog—a fuzzy prolog interpreter

Intelligent Data Engineering and Automated Learning

A mass assignment based ID3 algorithm for learning probabilistic fuzzy decision trees is introduced. Fuzzy partitions are used to discretize continuous feature universes and to reduce complexity when universes are discrete but with large cardinalities. Furthermore, the fuzzy partitioning of classification universes facilitates the use of these decision trees in function approximation problems. Generally the incorporation of fuzzy sets into this paradigm overcomes many of the problems associated with the application of decision trees to real-world problems. The probabilities required for the trees are calculated according to mass assignment theory applied to fuzzy labels. The latter concept is introduced to overcome computational complexity problems associated with higher dimensional mass assignment evaluations on databases. ©1997 John Wiley & Sons, Inc.

Trevor P Martin

Papers

Fril- Fuzzy and Evidential Reasoning in Artificial Intelligence

A mass assignment theory of the probability of fuzzy events

The implementation of fprolog—a fuzzy prolog interpreter

Intelligent Data Engineering and Automated Learning

A mass assignment based ID3 algorithm for decision tree induction