Kongzhi yu Juece/Control and Decision
Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) is a monthly peer-reviewed scopus indexed journal originally founded in 1986. It is sponsored by the Ministry of Education, china and Northeastern University, china. Kongzhi yu Juece/Control and Decision (ISSN:1001-0920) has always adhered to the correct purpose of running the journal, and has been committed to gathering and disseminating excellent academic achievements, inspiring technological innovation, and promoting the development of disciplines in my country.Aiming at major national needs and international frontiers, this journal has published a large number of original and high-level research result. The journal was selected into the "China Science and Technology Journal Excellence Action Plan Project" in December 2019.In the future, it will strive to build an open innovation, collaborative integration.
// Latest Journals
Interval observer design of LPV system with mixed H2/H∞ performance
A robust output tracking controller is necessary for the safe and reliable operation of aeroengines. This paper aims at developing an 𝐻2/𝐻∞ output tracking approach for aeroengines. In order to improve the tracking performance of the traditional robust tracker, the proposed control structure is designed as a combination of a nominal controller and a compensator. Concretely, an 𝐻2/𝐻∞ nominal controller is derived from game algebraic Raccati equation (GARE), which facilitates establishing a compensator for the system. Since the reference is usually unknown in advance f
Hybrid search direction based learning algorithm for feedforward complex-valued neural networks
Currently, dealing directly with in-phase and quadrature time series data using the deep learning method is widely used in signal modulation classification. However, there is a relative lack of methods that consider the complex properties of signals. Therefore, to make full use of the inherent relationship between in-phase and quadrature time series data, a complex-valued hybrid neural network (CV-PET-CSGDNN) based on the existing PET-CGDNN network is proposed in this paper, which consists of phase parameter estimation, parameter transformation, and complex-valued signal feature extraction
An ε improved moth-flame optimization algorithm for solving constrained optimization problems and engineering applications
In this paper, an improved moth-flame optimization algorithm (IMFO) is presented to solve engineering problems. Two novel effective strategies composed of Lévy flight and dimension-by-dimension evaluation are synchronously introduced into the moth-flame optimization algorithm (MFO) to maintain a great global exploration ability and effective balance between the global and local search. The search strategy of Lévy flight is used as a regulator of the moth-position update mechanism of global search to maintain a good research population diversity and expand the algorithm’s
Fast self-learning fault diagnosis method for industrial pneumatic control valves based on multi-feature fusion
The complex characteristics of pneumatic control valves make it difficulty to describe valve faults by establishing a accurate mathematical model, data-driven technology thus attracts widespread attention in the filed of its fault diagnosis. The existing control systems of commercial regulating valves, however, are always equipped with limited hardware equipment, which puts forward higher requirements for the fault diagnosis model and learning efficiency. Therefore, this paper presents a fast self-learning fault diagnosis method for pneumatic control valves based on multi-feature fusion. Fi
A real-time prediction method of carbon content in converter steelmaking based on DDMCN flame image feature extraction
As an important operation at the end of converter steelmaking, the key to the end-point control is the accurate and real-time prediction of carbon content. And the oxidation rate of carbon content in the molten pool can be reflected in the variation of the flame texture at the furnace mouth. Therefore, the extraction of accurate characteristics of flame texture is the key to predict end-point carbon content. However, the difficulty of flame texture feature description lies in its multi-directional and multi-scale irregular characteristics. This paper proposes a derivative nonlinear mapping