Email: [e-mail]

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• ???
• Email ycliang_at_jlu.edu.cn
• 21st May 2009

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• ???????????
• ??(Title)
• ??(Abstract)
• ???(Keywords)
• ??(??,??,??)(Introduction)
• ??(Body)
• ??(Conclusions)
• ??(Acknowledgement)
• ????(References)

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• ??(Title)
• ??????????????????????
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• ??(Abstract)
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• ???(Keywords)
• 3-5???????

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• ?? 1-1.
• ????SVD?PCA??????
• ???????Frobenius????????(SVD)???????????????????,
  ???SVD???????????
• ?????????,?????,Karhunen-Loève??,?????

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• ?? 1-2.
• Title On the Equivalence of SVD and PCA
• Abstract A novel proof on the optimal
  performance of the proper orthogonal bases of the
  singular value decomposition (SVD) is presented
  based on the Frobenius norm. The equivalence of
  the SVD and the principal component analysis
  (PCA) is indicated.
• Keywords Proper orthogonal decomposition,
  principal component analysis, Karhunen-Loève
  decomposition, singular value decomposition

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• ?? 2-1.
• ?????Elman?????????????
• ?????Elman?????????????????-????Elman?????-????E
  lman????,?????????????,???????????????????Lyapunov
  ?????????????????????,?????????????????????????Elm
  an????????????????????????Elman????,??????????????
  ,????????????,???????????????,??????????????????,?
  ???????????????????????????????????
• ?????????????????

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• ?? 2-2.
• Title Improved Elman Model and Recurrent
  Back-propagation Control Neural Networks
• Abstract Two improved Elman neural networks,
  output-input feedback Elman network and
  output-hidden feedback Elman network, are
  presented based on the Elman neural network. A
  recurrent back propagation control neural network
  model is developed by using the output-input
  feedback Elman network as a passageway of the
  error back propagation. The stability of the
  improved Elman neural networks is proved in the
  sense of Lyapunov stability theory. The optimal
  adaptive learning rates are obtained, which could
  guarantee the stable convergence of the improved
  Elman networks. The ultrasonic motor is simulated
  using the Elman and improved Elman networks,
  respectively. Besides simulating the speed of the
  ultrasonic motor successfully some useful results
  are also obtained. According to the results we
  could choose different network models based on
  the sampling situation in the fieldwork.
  Numerical results show that the recurrent back
  propagation control neural network controller has
  good effectiveness for various kinds of reference
  speeds of the ultrasonic motor.
• Key words neural network feedback Lyapunov
  stability

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• ?? 3-1.
• ???????????????????????????
• ???????????????????????????????????????????????,?
  ??????????????????????,??????????????????,????????
  ??????????????????????????BAliBASE????????????????
  ??????,??Baum-Welch????????????,??????????????????
  ??,??????????????
• ????????????????????????

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• ?? 3-2.
• Title A Hidden Markov Model and Immune Particle
  Swarm Optimization-Based Algorithm for Multiple
  Sequence Alignment
• Abstract Multiple sequence alignment (MSA) is a
  fundamental and challenging problem in the
  analysis of biologic sequences. In this paper, an
  immune particle swarm optimization (IPSO) is
  presented, which is based on the models of the
  vaccination and the receptor editing in immune
  systems. The proposed algorithm is used to train
  hidden Markov models (HMM), further, an
  integration algorithm based on the HMM and IPSO
  for the MSA is constructed. The approach is
  examined by using a set of standard instances
  taken from the Benchmark Alignment database,
  BAliBASE. Numerical simulation results are
  compared with those obtained by using the
  Baum-Welch training algorithm. The comparisons
  show that the proposed algorithm not only
  improves the alignment abilities, but also
  reduces the time cost.
• Key words Hidden Markov models, particle
  optimization swarm, immune systems, multiple
  sequence

19
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• ??(??,??)(Introduction)
• ??,????????????
• ?????,????????,????????????????????
• ???????????,?????????,????????
• ??(Body)
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• ???????????????????????

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• ??(Conclusions)
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• ?????????,?????,???????,??????
• ??????????????
• ??(Acknowledgement)
• ?????????????????????,???????????????

23
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24
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• ???????????????????????,????????,?1,2??
• ????(Appendix)(???)
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27
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• ???SCI,EI????

29
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30
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31
?????????

• ????????(4/9?)
• Liang YC, Feng DP, Liu GR, Yang XW and Han X.
  Neural identification of rock parameters using
  fuzzy adaptive learning parameters. Computers
  Structures, 2003, 81(24-25) 2373-2382. (SCI, EI)
• Liang YC, Feng DP, Lee HP, Lim SP and Lee KH.
  Successive approximation training algorithm for
  feedforward neural networks. Neurocomputing,
  2002, 42 311-322. (SCI, EI)
• Liang YC, Feng DP, Cooper JE. Identification of
  restoring forces in nonlinear vibration systems
  based on improved fuzzy adaptive BP algorithm.
  Journal of Sound and Vibration. 2001, 242 (1)
  47-58. (SCI, EI)
• Liang YC, Feng DP, Lee HP, Lim SP and Lee KH. A
  study of packaging identification using fuzzy
  adaptive neural networks. Mechanics Research
  Communications. 2001, 28(2) 147-156. (SCI, EI)

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?????????(1)

• ????????(3/25?)
• Wu CG, Liang YC, Lee HP, Lu C. Generalized
  chromosome genetic algorithm for generalized
  traveling salesman problems and its applications
  for machining. Physical Review E, 2004, 70, 1-13.
  (SCI )
• Wu CG, Liang YC, Lee HP, Lu C, Yang XW. Study on
  optimization of agent positions in land combat
  simulation. Progress in Natural Science, 2004, 14
  (3) 257-261. (SCI)
• ???, ???, ???, ???, ???. ??SVD?PCA ??????. ?????,
  2004, 27 (2) 286-288.

33
?????????(2)

• ????????(3/24?)
• Shi XH, Liang YC, Lee HP, Lu C and Wang LM. A
  variable population-size genetic algorithm and a
  hybrid evolutionary algorithm based PSO-GA.
  Information Processing Letters, 2005,
  93(5)255-261. (SCI)
• Shi XH, Liang YC, Lee HP, Lin WZ, Xu X, Lim SP.
  Improved Elman networks and applications for
  controlling ultrasonic motors. Applied Artificial
  Intelligence, 2004, 18 (7) 603-629. (SCI)
• ???,???,??.???Elman?????????????.????, 2003,
  14(6) 1110-1119. (EI)

34
?????????(3)

• ????????(3/22?)
• Ge HW, Sun L, Liang YC, Qian F. An effective
  PSO-and-AIS-based hybrid intelligent algorithm
  for job-shop scheduling, IEEE Transactions on
  System, Man and Cybernetics, Part A, Systems and
  Humans, 2008, 38(2) 358-368. (SCI)
• Liang YC, Ge HW, Zhou CG. Solving traveling
  salesman problems by genetic algorithms. Progress
  in Natural Science, 2003, 13 (2) 135-141. (SCI)
• ???,???. ?????????????????????????.
  ????????,2006,43(8) 1330-1336. (EI)

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