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by
Long-Hua Ma |
Total Records (
6 ) for
Long-Hua Ma |
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Long-Hua Ma
,
Yu Zhang
,
Chun-Ning Yang
and
Hui Li
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For signal processing and process control, the minimax problem is a crucial point in research subjects. But efficient solutions to equality and inequality constrained nonlinear general minimax problems are relatively scarce. A minimax neural network model was proposed to solve the general minimax problem based on penalty function. In this model, the unique requirement is that the objective function and constraint functions should be first-order differentiable. In addition to the global stability analysis based on the Lyapunov function, the proposed model was simulated and its validity was evaluated with numerical results. Experimental results demonstrated that the proposed minimax neural network model can solve the problem in seconds which is more efficient than the conventional genetic algorithm and simplex genetic algorithms. |
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Chun-Ning Yang
,
Long-Hua Ma
,
Ya-Zhou Yue
and
Hui Li
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In this study, a general scheme for implementation of attitude and velocity algorithm in Strapdown Inertial Navigation System (SINS) under real-time environment was presented. This real-time implementation scheme was formulated in a recursive form using two time-rate executions and utilized integer arithmetic only. The coding of the attitude and velocity algorithm was conducted by a navigation computer (such as DSP or FPGA). It is shown that the general real-time implementation of strapdown attitude and velocity algorithm can be used in strapdown inertial navigation system for any combinations of gyro incremental angle sample of mth minor interval and (m-1)th minor interval. |
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Long-Hua Ma
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Yu Zhang
,
Zhe-Ming Lu
and
Hui Li
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Particle degradation, as a main limitation of particle filter, can be resolved by making use of common re-sampling method, but it always bring about the problem of sample dilution. The Immune Particle Swarm Optimization (IMPSO) was introduced into particle filter and a new kind of particle filter named IMPSO-based particle filter was proposed. In the IMPSO-based particle filter algorithm, particles are driven to the area with a higher posterior probability density and maintain big particle diversity at the same time. Simulation results show that IMPSO-based particle filter can eliminates the degeneracy phenomenon, avoid the sample dilution problem and guarantee the effectiveness. |
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Long-Hua Ma
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Kai-Li Wang
and
Hui Li
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Land combat vehicles are inevitably subject to the vibration
disturbance by wind gust or engine idling, etc. in the stationary initial alignment
process of the Strapdown Inertial Navigation System (SINS). Obviously, it’s
necessary to consider the impact of vibration disturbance during the alignment
process to achieve better performance. In order to guarantee the alignment accuracy
on the rocking base and shorten the convergence time of alignment, a gyrocompass
alignment method of SINS based on Kalman filter pretreatment and dynamic gain
adjustment was proposed. The output of gyros and accelerometers was firstly
pre-filtered by Kalman filter to remove the impact of high-frequency small-amplitude
rocking interference. The low-frequency large-amplitude rocking interference
on vehicle was tracked through dynamic gain adjustment of gyrocompass alignment.
The vehicle test of a ring laser SINS showed that the new gyrocompass alignment
method can suppress high-frequency disturbances when the vehicle underwent low-frequency
large-amplitude rocking interference. And the alignment process can track the
attitude change of vehicle caused by low-frequency large-amplitude rocking interference.
Comparing with traditional gyrocompass alignment algorithm and Kalman filter
alignment method, the performance of the new gyrocompass alignment method is
much improved by filtering random noise caused by vibration disturbance of vehicle
effectively. |
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Long-Hua Ma
,
Ming Xu
,
Meng Shao
and
Zhe-Ming Lu
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Randomness and parameter selection in the Particle Swarm Optimization
(PSO) algorithm had great influence on its performance. This study presented
a formal convergence and spectral radius analysis of the standard PSO algorithm
model, where some of the parameters were stochastic. Based on the analysis of
the relationship of {ω, c1, c2}, a sufficient condition was given to guarantee
that the PSO algorithm was mean-square convergent, using the stochastic process
theory. Then, the mean spectral radius was constructed. According to the relationship
between the spectral radius and the convergent speed, it was shown that, a small
spectral radius lead to a faster convergent speed than a big one. By optimizing
the mean spectral radius of the PSO algorithm in the mean-square convergent
region, a minimum spectral radius and corresponding parameter selection guidelines
were derived to guarantee that the PSO algorithm was mean-square convergent
and had a fast convergent speed in the stochastic sense. Finally, one parameter
selection {c1 = c2 = 2, ω = 0.4222} was proposed. with the parameter, the
study gave examples whose performance on benchmark functions were superior to
previously published results. |
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Meng Shao
,
Zheng-Liang Huang
,
Long-Hua Ma
,
Zhe-Ming Lu
and
Xiao-Long Shi
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Security issues are increasingly obvious. An automated real-time
online alarm system to ensure the safety of property and personality while considering
numerous smart Terminal Equipments (TE) becomes a major challenge. At the same
time, this is representative of novel and emerging alarm system for assisted
living in the daily life. Two problems of current alarm system are identified.
A smart Cyber-Physical Alarm System (CPAS) based approach is proposed to address
these problems. A prototype system installed in a house to assist living has
been running stably and shows quite promising performance. |
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