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Now showing 1 - 8 of 8

A Hybrid of Bacterial Foraging Optimization and Particle Swarm Optimization for Evolutionary Neural Fuzzy Classifier
(2014-09-01)
Chen, Cheng-Hung
;
Su, Miin-Tsair
;
Lin, Cheng-Jian
;
Lin, Chin-Teng
;
交大名義發表
This study presents a new evolutionary learning algorithm to optimize the parameters of the neural fuzzy classifier (NFC). This new evolutionary learning algorithm is based on a hybrid of bacterial foraging optimization and particle swarm optimization. It is thus called bacterial foraging particle swarm optimization (BFPSO). The proposed BFPSO method performs local search through the chemotactic movement operation of bacterial foraging whereas the global search over the entire search space is accomplished by a particle swarm operator. The NFC model uses functional link neural networks as the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. Finally, the proposed neural fuzzy classifier with bacterial foraging particle swarm optimization (NFC-BFPSO) is adopted in several classification applications. Experimental results have demonstrated that the proposed NFC-BFPSO method can outperform other methods.
Characteristics of an Electrically Pumped GaN-based Microcavity Light Emitter with an AlN Current Blocking Layer
(2012)
Cheng, Bo-Siao
;
Wu, Tzeng-Tsong
;
Lai, Ying-You
;
Wu, Yun-Lin
;
Chen, Cheng-Hung
We demonstrate a GaN-based microcavity light emitter (MCLE) with an AlN current blocking layer. An AlN layer is inserted on the InGaN/GaN multiple quantum wells as a current blocking layer and an optical confinement layer. (C)2011 Optical Society of America
Highly-integrated, quad bands Delta Sigma fractional-N frequency synthesizer design in 0.18-mu m standard CMOS process
(2005)
Chen, Cheng-Hung
;
Lien, Wei-Cheng
;
Jou, Christina F.
;
電信工程研究所
;
Institute of Communications Engineering
A highly-integrated, quad bands frequency synthesizer (802.11a/b/g and GSM/DCS1800) is presented in this paper. This circuit is a single-path solution and the chip size is only half of the other dual-band frequency synthesizer works. By 50% frequency division technique, the quad-bands signals can be generated. Total power consumption for simultaneously quad-bands operation is 105mW, with apply voltage of 1.8V. Die area is 1.62mm(2).
A dual-band concurrent RF front-end receiver design for GPS and Bluetooth applications
(2005)
Chen, Po-Da
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Chen, Cheng-Hung
;
Chang, Win-Ming
;
Cheng, Kuo-Hua
;
Jou, Christina F.
This paper presents a new concurrent dual-band radio frequency receiver front-end design that is intended for Globe Positioning System (GPS) and Bluetooth applications. A low IF architecture is chosen for low-cost and low-power purpose. Using a dual-band sub-harmonic mixer make this new concurrent dual-band receiver architecture requires only a single frequency synthesizer in order the chip area could be reduced. This concurrent frequency synthesizer only needs to provide 0.784GHz and 1.24GHz local frequency signals in this dual-band topology. This receiver is operated at 1.8V, and the simulation shows a voltage gain of 27.7dB at 1.57GHz and 24.2dB at 2.45GHz, a noise figure of 4.13dB at 1.57GHz and 6.84dB at 2.45GHz with a total power consumption of 22.2mW.
Nonlinear System Control Using Functional-Link-Based Neuro-Fuzzy Network Model Embedded with Modified Particle Swarm Optimizer
(2012-03-01)
Su, Miin-Tsair
;
Lin, Chin-Teng
;
Hsu, Sheng-Chih
;
Li, Dong-Lin
;
Lin, Cheng-Jian
This study presents an evolutionary neural fuzzy system (NFS) for nonlinear system control. The proposed NFS model uses functional link neural networks (FLNNs) as the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. A learning algorithm, which consists of structure learning and parameter learning, is presented. The structure learning depends on the entropy measure to determine the number of fuzzy rules. The parameter learning, based on the particle swarm optimization (PSO) algorithm, can adjust the shape of the membership function and the corresponding weighting of the FLNN. The distance-based mutation operator, which strongly encourages a global search giving the particles more chance of converging to the global optimum, is introduced. The simulation results have shown the proposed method can improve the searching ability and is very suitable for the nonlinear system control applications.
Characteristics of GaN-based Vertical Cavity Surface Emitting Lasers with Hybrid Mirrors
(2011)
Lu, Tien-Chang
;
Cheng, Bo Siao
;
Wu, Tzeng-Tsong
;
Chen, Shih-Wei
;
Chen, Chien-Kang
We demonstrated CW current injection of GaN VCSELs with hybrid mirrors at room temperature. The laser characteristics, such as temperature dependent laser threshold current, spontaneous emission coupling factors, have been measured and discussed. (C)2011 Optical Society of America
Dynamic system identification using a recurrent compensatory fuzzy neural network
(2008-10-01)
Lee, Chi-Yung
;
Lin, Cheng-Jian
;
Chen, Cheng-Hung
;
Chang, Chun-Lung
;
電控工程研究所
This Study presents a recurrent compensatory fuzzy neural network (RCFNN) for dynamic system identification. The proposed RCFNN uses a compensatory fuzzy reasoning method, and has feedback connections added to the rule layer of the RCFNN. The compensatory fuzzy reasoning method can make the fuzzy logic system more effective, and the additional feedback connections can solve temporal problems as well. Moreover, an online learning algorithm is demonstrated to automatically construct the RCFNN. The RCFNN initially contains no rules. The rules are created and adapted as online learning proceeds via simultaneous structure and parameter learning. Structure learning is based on the measure of degree and parameter learning is based on the gradient descent algorithm. The simulation results from identifying dynamic systems demonstrate that the convergence speed of the proposed method exceeds that of conventional methods. Moreover, the number of adjustable parameters of the proposed method is less than the other recurrent methods.
Efficient reinforcement hybrid evolutionary learning for recurrent wavelet-based neuro-fuzzy systems
(2007)
Chen, Cheng-Hung
;
Lin, Cheng-Jian
;
Lee, Chi-Yung
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes a recurrent wavelet-based neuro-fuzzy system (RWNFS) with the reinforcement hybrid evolutionary learning algorithm (R-HELA) for solving various control problems. The proposed R-HELA combines the compact genetic algorithm (CGA) and the modified variable-length genetic algorithm (MVGA), performs the structure/parameter learning for dynamically constructing the RWNFS. That, is, both the number of rules and the adjustment of parameters in the RWNFS are designed concurrently by the R-HELA. In the R-HELA, individuals of the same length constitute the same group. There are multiple groups in a population. The evolution of a population consists of three major operations: group reproduction using the compact genetic algorithm, variable two-part crossover, and variable two-part mutation. An illustrative example was conducted to show the performance and applicability of the proposed R-HELA method.

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