簡化退火演算法基於模糊類神經網路控制器於非線性系統之控制

CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator. Performance of technical analysis in growth and small

Pascanu et al., “On the difficulty of training recurrent neural networks,” in ICML, 2013..

2 System modeling and problem formulation 8 3 Adaptive Minimum Variance Control of T-S Fuzzy Model 12 3.1 Stability of Stochastic T-S Fuzzy

In this paper, by using Takagi and Sugeno (T-S) fuzzy dynamic model, the H 1 output feedback control design problems for nonlinear stochastic systems with state- dependent noise,

We shall show that after finite times of switching, the premise variable of the fuzzy system will remain in the universe of discourse and stability of the adaptive control system

Moreover, this chapter also presents the basic of the Taguchi method, artificial neural network, genetic algorithm, particle swarm optimization, soft computing and

蔣松原，1998，應用 應用 應用 應用模糊理論 模糊理論 模糊理論

Kuo, R.J., Chen, C.H., Hwang, Y.C., 2001, “An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and