Xu Zhipeng

TL;DR: A novel ship classification model combining kernel extreme learning machine (KELM) and dragonfly algorithm in binary space (BDA), named BDA-KelM, is proposed which can achieve a better classification performance than these four widely used models with an classification accuracy as high as 97% and encouraging results of other three multi-class classification evaluation metrics.

TL;DR: The experimental results show that the proposed OCS–CMPSO–WLSSVM method not only has higher reliability prediction accuracy and robustness, but also has its superiority and applicability in other fields including time-ordered, feature-based regression problem and classification problem.

TL;DR: A novel approach for cutting pattern recognition with an optimal Online Correcting Strategy (OCS) combined with Least Square Support Vector Machine (LSSVM) and Chaos Modified Particle Swarm Optimization (CMPSO) algorithm is proposed, where LSSVM models the functional relationship between input and output of the system, CMPSO optimizes the parameters of L SSVM, and OCS modifies the model to reduce its mismatch as the system runs.

TL;DR: A new cutting pattern recognition model based on the combination of Relevance Vector Machine (RVM) and Chaotic Gravitational Search Algorithm (CGSA) with chaotic mapping for increasing the search diversity of the algorithm.

TL;DR: In this article, a swarm intelligence optimization fault diagnosis system based on hybrid optimized parameters is presented, which is used for performing fault diagnosis on a Tennessee Eastman process and comprises a data preprocessing module, a principal component analysis module, and a relevance vector machine module.