Browsing by Author "Hor, Kar-Chun"

Now showing 1 - 3 of 3

From fuzzy center average defuzzifier (CAD) to fuzzy lookup table controller (FLTC) with an efficient Heaviside search algorithm (HSA)
(2019-09-01)
Wang, Chi-Hsu
;
Hor, Kar-Chun
;
交大名義發表
;
電機學院
;
National Chiao Tung University
This paper proposes an alternate interpretation of the traditional lookup table controller via the center average defuzzifier (CAD) in fuzzy set theory with a set of disjoint block pulse membership functions (DBPMFs). The DBPMFs with CAD can be proved to be equivalent to the traditional fuzzy lookup table controller (FLTC). Further, a new Heaviside search algorithm (HSA) is also proposed to implement the FLTC in a more efficient way. The HSA is to find the indices for every fuzzy input variable independently, in order to speed up the table-searching process. The computational complexity of our new HSA is far less than the complexity of the traditional linear search algorithm (LSA), where HSA is the sum of R-i, where R-i is the number of membership functions for ith input variable in FLTC. The average computational complexity in LSA is the product of R-i divided by 2. The HSA also reduces the complexity of coding significantly in the same order as the comparison of computation complexities between HSA and LSA. The balancing of the inverted pendulum system is adopted as the benchmark to show the feasibility and efficiency of this new FLTC and HSA.
Fuzzy Lookup Table Controller (FLTC) via Center Average De-fuzzifier (CAD) with a New Efficient Heaviside Search Algorithm (HSA) for Lookup Table Implementation
(2018-01-01)
Wang, Chi-Hsu
;
Hor, Kar-Chun
;
Chen, Wei-Chuan
;
電機學院
;
電機工程學系
This paper proposes an alternate interpretation of the traditional lookup table controller via the Center Average De- fuzzier (CAD) in Fuzzy Set Theory with a set of Disjoint Block Pulse Membership Functions (DBPMFs). Further, a new Heaviside Search Algorithm (HSA) is also proposed to implement the Fuzzy Lookup Table Controller (FLTC) in a more efficient way. The computation complexity of our new HSA is O(Sigma R-i), where R-i is the number of membership functions for i th input variable in FLTC. Whereas the average computational complexity in traditional Linear Search Algorithm (LSA) is O((Sigma R-i)/2). In comparison with LSA, the HSA also reduces the complexity of coding significantly in the same order as the comparison of computation complexities between HSA and LSA. The balancing of Inverted Pendulum System (IPS) is adopted as the benchmark to show the feasibility and efficiency of this new FLTC and HSA.
Identifying an Unknown Flying Target in Missile Defense Systems Using Intelligent Fuzzy Neural Networks
(2017-01-01)
Wang, Chi-Hsu
;
Hor, Kar-Chun
;
Wang, Bin
;
電機工程學系
;
Department of Electrical and Computer Engineering
For radar automatic target recognition (RATR), this paper aims at identifying the incoming unknown flying missiles in the Missile Defense Systems (MDS), using intelligent fuzzy neural networks (FNNs) with intelligent feature extraction. The training data for FNNs is obtained by sampling the Radar Power Signal Envelop (RPSE) of the radar echo signal (from High Resolution Range Profile (HRRP) radar) for the incoming flying missiles under different azimuth and elevation angles. The RPSEs under different irradiation angles of incoming flying targets can be generated by a high frequency structural simulator (HFSS) package, which is close to real RPSE and has been adopted by academic researchers in this area. The premise part in FNN is composed of a set of Uniform Distributed Gaussian Membership Functions (UDGMFs) and the consequent part is a two layer Neural Network (NN) which can be trained by Dynamic Optimal Training Algorithm (DOTA). By using this approach, the identification of five different flying missiles is performed in this paper. From the bench mark test, this intelligent FNN configuration can identify the unknown flying missiles very accurately due to the fact that the number of training patterns is well below the capacity of the proposed FNN configuration under a certain noise intensity.