Tai-Wei Chiang

  • Citations Per Year
Learn More
A new neuro-fuzzy computing paradigm using complex fuzzy sets is proposed in this paper. The novel computing paradigm is applied to the problem of function approximation to test its nonlinear mapping ability. A complex fuzzy set (CFS) is an extension of traditional type-1 fuzzy set whose membership is within the unit real-valued interval. For a CFS, the(More)
Financial investors often face an urgent need to predict the future. Accurate forecasting may allow investors to be aware of changes in financial markets in the future, so that they can reduce the risk of investment. In this paper, we present an intelligent computing paradigm, called the Complex Neuro-Fuzzy System (CNFS), applied to the problem of financial(More)
A new complex fuzzy computing paradigm using complex fuzzy sets (CFSs) to the problem of time series forecasting is proposed in this study. Distinctive from traditional type-1 fuzzy set, the membership for elements belong to a CFS is characterized in the unit disc of the complex plane. Based on the property of complex-valued membership, CFSs can be used to(More)
A new complex neuro-fuzzy self-learning approach to the problem of function approximation is proposed, where complex fuzzy sets are used to design a complex neuro-fuzzy system as the function approximator. Particle swarm optimization (PSO) algorithm and recursive least square estimator (RLSE) algorithm are used in hybrid way to adjust the free parameters of(More)
A complex neuro-fuzzy self-learning approach using complex fuzzy sets to the problem of function approximation is proposed in this paper. The concept of complex fuzzy sets (CFSs) is an extension of traditional fuzzy set whose membership degrees are within a unit disk in the complex plane. The Particle Swarm Optimization (PSO) algorithm and the recursive(More)
A new adaptive fuzzy approach to function approximation is proposed in the paper. A Takagi-Sugeno (T-S) type fuzzy system is used as the function approximator in the study. The proposed approach uses a hybrid learning method to train the T-S fuzzy system to achieve high accuracy in function approximation. The hybrid learning method combines both the(More)
  • 1