Luonan Chen

TL;DR: In this article, a transiently chaotic neural network (TCNN) model is proposed for combinatorial optimization problems, where the chaotic neurodynamics is temporarily generated for searching and self-organizing, and eventually vanishes with autonomous decrease of a bifurcation parameter corresponding to the temperature in the usual annealing process.

TL;DR: In this paper, a model-free method to detect early warning signals of critical transitions, even with only a small number of samples, was proposed. And the authors theoretically derive an index based on a dynamical network biomarker (DNB) that serves as a general early warning signal indicating an imminent bifurcation or sudden deterioration before the critical transition occurs.

TL;DR: In this paper, a model for genetic regulatory networks with time delays, described by functional differential equations or delay differential equations (DDE), provides necessary and sufficient conditions for simplifying the genetic network model, and further analyze nonlinear properties of the model in terms of local stability and bifurcation.

TL;DR: This paper aims to develop a load forecasting method for short-term load forecasting, based on an adaptive two-stage hybrid network with self-organized map (SOM) and support vector machine (SVM).

TL;DR: Both theoretical and numerical results show that optimizing the new criterion not only can resolve detailed modules that existing approaches cannot achieve, but also can correctly identify the number of communities.