Browsing by Author "Li, CP"

Now showing 1 - 6 of 6

Effect of the orientation of CdSe nanorods on the electron mobility of CdSe/P4VP nanodomains self-assembled within a poly(styrene-b-(4)-vinyipyridine) diblock copolymer thin film
(2006-03-01)
Li, CP
;
Yeh, SW
;
Chang, HC
;
Huang, JY
;
Wei, KH
Enhanced collective electron transport by CdSe quantum dots confined in the poly(4vinylpyridine) nanodomains of a poly(styrene-b-4-vinylpyridine) diblock copolymer thin film
(2006)
Li, CP
;
Wei, KH
;
Huang, JY
;
材料科學與工程學系
;
光電工程學系
Properties of covalently bonded layered-silicate/polystyrene nanocomposites synthesized via atom transfer radical polymerization
(2005-02-01)
Li, CP
;
Huang, CM
;
Hsieh, MT
;
Wei, KH
;
材料科學與工程學系
Covalently bonded layered silicated/polystyrene nanocomposites were synthesized via atom transfer radical polymerization in the presence of initiator-modified layered silicate. The resulting nanocomposites had an intercalated and partially exfoliated structure, as confirmed by X-ray diffraction and transmission electron microscopy. The thermal properties of the nanocomposites improved substantially over those of neat polystyrene. In particular, a maximum increase of 35.5 degreesC in the degradation temperature was displayed by these nanocomposites. Additionally, the surface elastic modulus and hardness of these nanocomposites were more than double those of pure polystyrene. (C) 2004 Wiley Periodicals, Inc.
Temperature control with a neural fuzzy inference network
(1999-08-01)
Lin, CT
;
Juang, CF
;
Li, CP
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Although multilayered backpropagation neural networks (BPNN's) have demonstrated high potential in the nonconventional branch of adaptive control, their long training time usually discourages their applications in industry. Moreover, when they are trained on line to adapt to plant variations, the over-tuned phenomenon usually occurs. To overcome the weakness of the BPNN, in this paper we propose a neural fuzzy inference network (NFIN) suitable for adaptive control of practical plant systems in general and for adaptive temperature control of a water bath system in particular. The NFIN is inherently a modified Takagi-Sugeno-Kang (TSK)-type fuzzy rule-based model possessing a neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as on-line learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a practical water bath temperature-control system. As compared to the BPNN under the same training procedure, the simulated results show that not only can the NFIN greatly reduce the training time and avoid the over-tuned phenomenon, but the NFIN also has perfect regulation ability. The performance of the NFIN is also compared to that of the traditional PID controller and; fuzzy logic controller (FLC) on the water bath temperature-control system. The three control schemes are compared through experimental studies with respect to set-points regulation, ramp-points tracking, and the influence of unknown impulse noise and large parameter variation in the temperature-control system. It is found that the proposed NFIN control scheme has the best control performance of the three control schemes.
Temperature control with a neural fuzzy inference network
(1996)
Lin, CT
;
Juang, CF
;
Li, CP
;
交大名義發表
;
電控工程研究所
In this paper, we propose a neural fuzzy inference network (NFIN) suitable for adaptive temperature control of a water bath system. The rules in the NFIN are created and adapted as on-line learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a practical water bath temperature control system. The performance of the NFIN is compared to that of the PID controller and fuzzy logic controller (FLC) on the water bath temperature control system. The three control schemes are compared through experimental studies. It's found that the proposed NFIN control scheme has best control performance among the three control schemes.
Water bath temperature control with a neural fuzzy inference network
(2000-04-16)
Lin, CT
;
Juang, CF
;
Li, CP
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Although multilayered backpropagation neural networks (BPNN) have demonstrated high potential in the nonconventional branch of adaptive control, its long training time usually discourages their applications in industry. Moreover, when they are trained on-line to adapt to plant variations, the overtuned phenomenon usually occurs. To overcome the weakness of the BPNN, we propose a neural fuzzy inference network (NFIN) in this paper suitable for adaptive control of practical plant systems in general, and for adaptive temperature control of a water bath system in particular. The NFIN is inherently a modified TSK (Takagi-Sugeno-Kang)-type fuzzy rule-based model possessing neural network's learning ability. In contrast to the general adaptive neural fuzzy networks, where the rules should be decided in advance before parameter learning is performed, there are no rules initially in the NFIN. The rules in the NFIN are created and adapted as on-line learning proceeds via simultaneous structure and parameter identification. The NFIN has been applied to a water bath temperature control system. As compared to the BPNN under the same training procedure, the control results show that not only can the NFIN greatly reduce the training time and avoid the overtuned phenomenon, but the NFIN also has perfect regulation ability. (C) 2000 Elsevier Science B.V. All rights reserved.