Dynamic Moore-Penrose Inversion With Unknown Derivatives: Gradient Neural Network Approach.

Finding dynamic Moore-Penrose inverses (DMPIs) in real-time is a challenging problem due to the time-varying nature of the inverse. Traditional numerical methods for static Moore-Penrose inverse are not efficient for calculating DMPIs and are restricted by serial processing. The current state-of-the-art method for finding DMPIs is called the zeroing neural network (ZNN) method, which requires that the time derivative of the associated matrix is available all the time during the solution process.

Attention-Emotion-Enhanced Convolutional LSTM for Sentiment Analysis.

Long short-term memory (LSTM) neural networks and attention mechanism have been widely used in sentiment representation learning and detection of texts. However, most of the existing deep learning models for text sentiment analysis ignore emotion's modulation effect on sentiment feature extraction, and the attention mechanisms of these deep neural network architectures are based on word- or sentence-level abstractions. Ignoring higher level abstractions may pose a negative effect on learning text sentiment features and further degrade sentiment classification performance.

Harmonious Genetic Clustering.

To automatically determine the number of clusters and generate more quality clusters while clustering data samples, we propose a harmonious genetic clustering algorithm, named HGCA, which is based on harmonious mating in eugenic theory. Different from extant genetic clustering methods that only use fitness, HGCA aims to select the most suitable mate for each chromosome and takes into account chromosomes gender, age, and fitness when computing mating attractiveness. To avoid illegal mating, we design three mating prohibition schemes, i.