Zhi-Qiang Liu

Soft Computing and Human-Centered Machines

Introduction multisets and fuzzy multisets model logic, rough sets, and fuzzy sets fuzzy cognitive maps - analysis and extensions methods in hard and fuzzy clustering soft-competitive learning paradigms aggregation operations for fusing fuzzy information fuzzy gated neural networks in pattern recognition soft computing technique in kansei (emotional) information processing vagueness in human judgment and decision making chaos and time series analysis a short course for fuzzy set theory.

Type-2 fuzzy variables and their arithmetic

This paper proposes an axiomatic framework from which we develop the theory of type-2 (T2) fuzziness, called fuzzy possibility theory. First, we introduce the concept of a fuzzy possibility measure in a fuzzy possibility space (FPS). The fuzzy possibility measure takes on regular fuzzy variable (RFV) values, so it generalizes the scalar possibility measure in the literature. One of the interesting consequences of the FPS is that it leads to a new definition of T2 fuzzy set on the Euclidean space

The modes of convergence in the approximation of fuzzy random optimization problems

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Type-2 fuzzy hidden Markov models to phoneme recognition

which we call T2 fuzzy vector, as a map to the space instead of on the space. More precisely, we define a T2 fuzzy vector as a measurable map from an FPS to the space

Speech Animation Using Coupled Hidden Markov Models

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Script Visualization (ScriptVis): a smart system that makes writing fun

of real vectors. In the current development, we are suggesting that T2 fuzzy vector is a more appropriate definition for a T2 fuzzy set on