CPSO: Chaotic Particle Swarm Optimization for Cluster Analysis

Background:To solve the cluster analysis better,we propose a new method based on the chaotic particle swarm optimization(CPSO)algorithm.Methods:In order to enhance the performance in clustering,we propose a novel method based on CPSO.We first evaluate the clustering performance of this model using the variance ratio criterion(VRC)as the evaluation metric.The effectiveness of the CPSO algorithm is compared with that of the traditional particle swarm optimization(PSO)algorithm.The CPSO aims to improve the VRC value while avoiding local optimal solutions.The simulated dataset is set at three levels of overlapping:non-overlapping,partial overlapping,and severe overlapping.Finally,we compare CPSO with two other methods.Results:By observing the comparative results,our proposed CPSO method performs outstandingly.In the conditions of non-overlapping,partial overlapping,and severe overlapping,our method has the best VRC values of 1683.2,620.5,and 275.6,respectively.The mean VRC values in these three cases are 1683.2,617.8,and 222.6.Conclusion:The CPSO performed better than other methods for cluster analysis problems.CPSO is effective for cluster analysis.

Trait variation and functional diversity maintenance of understory herbaceous species coexisting along an elevational gradient in Yulong Mountain,Southwest China

Characterizing trait variation across different ecological scales in plant communities has been viewed as a way to gain insights into the mechanisms driving species coexistence.However,little is known about how changes in intraspecific and interspecific traits across sites influence species richness and community assembly,especially in understory herbaceous communities.Here we partitioned the variance of four functional traits(maximum height,leaf thickness,leaf area and specific leaf area)across four nested biological scales:individual,species,plot,and elevation to quantify the scale-dependent distributions of understory herbaceous trait variance.We also integrated the comparison of the trait variance ratios to null models to investigate the effects of different ecological processes on community assembly and functional diversity along a 1200-m elevational gradient in Yulong Mountain.We found interspecific trait variation was the main trait variation component for leaf traits,although intraspecific trait variation ranged from 10% to 28% of total variation.In particular,maximum height exhibited high plasticity,and intraspecific variation accounted for 44% of the total variation.Despite the fact that species composition varied across elevation and species richness decreased dramatically along the elevational gradient,there was little variance at our largest(elevation)scale in leaf traits and functional diversity remained constant along the elevational gradient,indicating that traits responded to smaller scale influences.External filtering was only observed at high elevations.However,strong internal filtering was detected along the entire elevational gradient in understory herbaceous communities,possibly due to competition.Our results provide evidence that species coexistence in understory herbaceous communities might be structured by differential niche-assembled processes.This approach ee integrating different biological scales of trait variation ee may provide a better understanding of the mechanisms involved in the structure of communities.

Blind spectrum sensing based on the ratio of mean square to variance

Anderson-Darling （AD） sensing, characteristic function （CF） sensing and order statistic （OS） sensing are three common spectrum sensing （SS） methods based on goodness of fit （GOF） testing. However, AD and OS sensing needs the prior information of noise variance; CF and OS sensing have high computation complexity. To circumvent those difficulties, in this paper, the ratio of the mean square to variance （RM2V） of the samples, after deriving its probability density function （PDF）, is employed as a test statistic to detect the availability of the vacant spectrum in the cognitive radio （CR） system. Then a blind SS method based on RM2V is proposed, which is dubbed as RM2V sensing, and its exact theoretical threshold is obtained via the derived PDF of RM2V. The performance of RM2V sensing is evaluated by theoretical analysis and Monte Carlo simulations. Comparing with the conventional energy detection （ED）, AD, CF and OS sensing, RM2V sensing, with no need of noise variance, has advantages from the aspect of computation complexity and detection performance.

Dynamic Hedging Based on Markov Regime-Switching Dynamic Correlation Multivariate Stochastic Volatility Model

It is important to consider the changing states in hedging.The Markov regime-switching dynamic correlation multivariate stochastic volatility( MRS-DC-MSV) model was proposed to solve this issue. DC-MSV model and MRS-DC-MSV model were used to calculate the time-varying hedging ratios and compare the hedging performance. The Markov chain Monte Carlo( MCMC) method was used to estimate the parameters. The results showed that,there were obviously two economic states in Chinese financial market. Two models all did well in hedging,but the performance of MRS-DCMSV model was better. It could reduce risk by nearly 90%. Thus,in the hedging period,changing states is a factor that cannot be neglected.