Hyun Duck Choi

L2-L∞ Filtering for Takagi-Sugeno fuzzy neural networks based on Wirtinger-type inequalities

Abstract This paper deals with the L 2 – L ∞ filtering problem for continuous-time Takagi–Sugeno fuzzy delayed Hopfield neural networks based on Wirtinger-type inequalities. A new set of delay-dependent conditions is established to estimate the state variables of fuzzy neural networks through the observed input and output variables. This ensures that the state estimation error system is asymptotically stable with a guaranteed L 2 – L ∞ performance. The presented criterion is formulated in terms of linear matrix inequalities (LMIs). An example with simulation results is given to illustrate the effectiveness of the proposed fuzzy neural state estimator.

Smart air condition load forecasting based on thermal dynamic model and finite memory estimation for peak-energy distribution

In this paper, we propose a new load forecasting method for smart air conditioning (A/C) based on the modified thermodynamics of indoor temperature and the unbiased finite memory estimator (UFME). Based on modified first-order thermodynamics, the dynamic behavior of indoor temperature can be described by the time-domain state-space model, and an accurate estimate of indoor temperature can be achieved by the proposed UFME. In addition, a reliable A/C load forecast can be obtained using the proposed method. Our study involves the experimental validation of the proposed A/C load forecasting method and communication construction between DR server and HEMS in a test bed. Through experimental data sets, the effectiveness of the proposed estimation method is validated.

Multi-target tracking algorithm based on FIR filters

This research article proposes a multi-target tracking algorithm by using a finite impulse response (FIR) filter. The advantage of the FIR filter is to observe robust filtering to incorrect data such as model uncertainty and noise, etc. This paper demonstrates robust tracking performance for multi-target tracking using the properties of FIR filter. In order to use FIR structure, all possible paths are constructed and the estimate of each path is chosen as the target through Mahalanobis distance if it is appropriate target.

Discrete-Time Takagi-Sugeno Fuzzy Finite Impulse Response Filter

In this research article, a new finite impulse response (FIR) filter for discrete time Takagi-Sugeno (T-S) fuzzy system is proposed to estimate the state of non-linear system. Compared with conventional T-S fuzzy filters which are known to have infinite impulse response (IIR) and require whole data from initial time to the current time to operate, the proposed filter is a FIR filter which guarantees to have robustness to unknown initial data. In addition, the proposed FIR filter satisfies the unbiased condition with the minimum variance error. Simulation result demonstrates that the proposed FIR filter is more robust and fast in convergence under the incorrect initial data than the existing non-linear filters with the same condition by using tunnel diode model.

Gaussian distribution resampling algorithm of particle filter

This paper proposes a new resampling algorithm - Gaussian distributed resampling (GDR) algorithm - in order to solve particle impoverishment phenomenon in particle filter. The key idea of proposed algorithm generates the new particles based on Gaussian distribution, which depend on the size of weight in resampling process. In comparison with established resampling algorithms, diversity of particles can be maintained, and thus the proposed algorithm avoids the sample impoverishment.