Multimodality Prediction of Chaotic Time Series with Sparse Hard-Cut EM Learning of the Gaussian Process Mixture Model

The contribution of this work is twofold： （1） a multimodality prediction method of chaotic time series with the Gaussian process mixture （GPM） model is proposed, which employs a divide and conquer strategy. It automatically divides the chaotic time series into multiple modalities with different extrinsic patterns and intrinsic characteristics, and thus can more precisely fit the chaotic time series. （2） An effective sparse hard-cut expec- tation maximization （SHC-EM） learning algorithm for the GPM model is proposed to improve the prediction performance. SHO-EM replaces a large learning sample set with fewer pseudo inputs, accelerating model learning based on these pseudo inputs. Experiments on Lorenz and Chua time series demonstrate that the proposed method yields not only accurate multimodality prediction, but also the prediction confidence interval SHC-EM outperforms the traditional variational 1earning in terms of both prediction accuracy and speed. In addition, SHC-EM is more robust and insusceptible to noise than variational learning.

Adaptive learning rate GMM for moving object detection in outdoor surveillance for sudden illumination changes

A dynamic learning rate Gaussian mixture model（GMM）algorithm is proposed to deal with the problem of slow adaption of GMM in the case of moving object detection in the outdoor surveillance,especially in the presence of sudden illumination changes.The GMM is mostly used for detecting objects in complex scenes for intelligent monitoring systems.To solve this problem,a mixture Gaussian model has been built for each pixel in the video frame,and according to the scene change from the frame difference,the learning rate of GMM can be dynamically adjusted.The experiments show that the proposed method gives good results with an adaptive GMM learning rate when we compare it with GMM method with a fixed learning rate.The method was tested on a certain dataset,and tests in the case of sudden natural light changes show that our method has a better accuracy and lower false alarm rate.

Evolutionary Experience-Driven Particle Swarm Optimization with Dynamic Searching

Particle swarm optimization(PSO)algorithms have been successfully used for various complex optimization problems.However,balancing the diversity and convergence is still a problem that requires continuous research.Therefore,an evolutionary experience-driven particle swarm optimization with dynamic searching(EEDSPSO)is proposed in this paper.For purpose of extracting the effective information during population evolution,an adaptive framework of evolutionary experience is presented.And based on this framework,an experience-based neighborhood topology adjustment(ENT)is used to control the size of the neighborhood range,thereby effectively keeping the diversity of population.Meanwhile,experience-based elite archive mechanism(EEA)adjusts the weights of elite particles in the late evolutionary stage,thus enhancing the convergence of the algorithm.In addition,a Gaussian crisscross learning strategy(GCL)adopts cross-learning method to further balance the diversity and convergence.Finally,extensive experiments use the CEC2013 and CEC2017.The experiment results show that EEDSPSO outperforms current excellent PSO variants.