Least Squares Maximum and Weighted Generalization-Memorization Machines

In this paper, we propose a new way of remembering by introducing a memory influence mechanism for the least squares support vector machine (LSSVM). Without changing the equation constraints of the original LSSVM, this mechanism, allows an accurate partitioning of the training set without overfitting. The maximum memory impact model (MIMM) and the weighted impact memory model (WIMM) are then proposed. It is demonstrated that these models can be degraded to the LSSVM. Furthermore, we propose some different memory impact functions for the MIMM and WIMM. The experimental results show that that our MIMM and WIMM have better generalization performance compared to the LSSVM and significant advantage in time cost compared to other memory models

Dynamic Analysis and Optimization of a Production Control System under Supply and Demand Uncertainties

This study investigates the dynamic performance and optimization of a typical discrete production control system under supply disruption and demand uncertainty. Two different types of uncertain demands, disrupted demand with a step change in demand and random demand, are considered. We find that, under demand disruption, the system’s dynamic performance indicators (the peak values of the order rate, production completion rate, and inventory) increase with the duration of supply disruption; however, they increase and decrease sequentially with the supply disruption start time. This change tendency differs from the finding that each kind of peak is independent of the supply disruption start time under no demand disruption. We also find that, under random demand, the dynamic performance indicators (Bullwhip and variance amplification of inventory relative to demand) increase with the disruption duration, but they have a decreasing tendency as demand variance increases. In order to design an adaptive system, we propose a genetic algorithm that minimizes the respective objective function on the system’s dynamic performance indicators via choosing appropriate system parameters. It is shown that the optimal parameter choices relate closely to the supply disruption start time and duration under disrupted demand and to the supply disruption duration under random demand

Volatility forecasting using deep neural network with time-series feature embedding

Volatility is usually a proxy indicator for market variation or tendency, containing essential information for investors and policymakers. This paper proposes a novel hybrid deep neural network model (HDNN) with temporal embedding for volatility forecasting. The main idea of our HDNN is that it encodes one-dimensional time-series data as two-dimensional GAF images, which enables the follow-up convolution neural network (CNN) to learn volatility- related feature mappings automatically. Specifically, HDNN adopts an elegant end-to-end learning paradigm for volatility forecasting, which consists of feature embedding and regression components. The feature embedding component explores the volatility-related temporal information from GAF images via the elaborate CNN in an underlying temporal embedding space. Then, the regression component takes these embedding vectors as input for volatility forecasting tasks. Finally, we examine the feasibility of HDNN on four synthetic GBM datasets and five realworld Stock Index datasets in terms of five regression metrics. The results demonstrate that HDNN has better performance in most cases than the baseline forecasting models of GARCH, EGACH, SVR, and NN. It confirms that the volatility-related temporal features extracted by HDNN indeed improve the forecasting ability. Furthermore, the Friedman test verifies that HDNN is statistically superior to the compared forecasting models

SDSS-IV MaNGA: The Roles of AGNs and Dynamical Processes in Star Formation Quenching in Nearby Disk Galaxies

We study how star formation (SF) is quenched in low-redshift disk galaxies with integral-field spectroscopy. We select 131 face-on spiral galaxies with stellar mass greater than $\rm 3\times10^{10}M_\odot$, and with spatially resolved spectrum from MaNGA DR13. We subdivide the sample into four groups based on the offset of their global specific star formation rate (SFR) from the star-forming main sequence and stack the radial profiles of stellar mass and SFR. By comparing the stacked profiles of quiescent and star-forming disk galaxies, we find that the decrease of the global SFR is caused by the suppression of SF at all radii, but with a more significant drop from the center to the outer regions following an inside-out pattern. As the global specific SFR decreases, the central stellar mass, the fraction of disk galaxies hosting stellar bars, and active galactic nuclei (AGNs; including both LINERs and Seyferts) all increase, indicating dynamical processes and AGN feedback are possible contributors to the inside-out quenching of SF in the local universe. However, if we include only Seyferts, or AGNs with ${\rm EW(H\alpha)>3\AA}$, the increasing trend of AGN fraction with decreasing global sSFR disappears. Therefore, if AGN feedback is contributing to quenching, we suspect that it operates in the low-luminosity AGN mode, as indicated by the increasing large bulge mass of the more passive disk galaxies.Comment: 12 pages, 7 figures, published in ApJ, typos corrected, references update