scholarly journals Variance-Constrained Multiobjective Control and Filtering for Nonlinear Stochastic Systems: A Survey

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Lifeng Ma ◽  
Zidong Wang ◽  
Hongli Dong ◽  
Guoliang Wei
Keyword(s):  
Stochastic Systems ◽  
Practical Method ◽  
Future Research ◽  
Engineering Practice ◽  
Nonlinear Stochastic Systems ◽  
Design Problems ◽  
Multiobjective Control ◽  
Multi Objective ◽  
Objective Control ◽  
Future Research Directions

The multiobjective control and filtering problems for nonlinear stochastic systems with variance constraints are surveyed. First, the concepts of nonlinear stochastic systems are recalled along with the introduction of some recent advances. Then, the covariance control theory, which serves as a practical method for multi-objective control design as well as a foundation for linear system theory, is reviewed comprehensively. The multiple design requirements frequently applied in engineering practice for the use of evaluating system performances are introduced, including robustness, reliability, and dissipativity. Several design techniques suitable for the multi-objective variance-constrained control and filtering problems for nonlinear stochastic systems are discussed. In particular, as a special case for the multi-objective design problems, the mixedH2/H∞control and filtering problems are reviewed in great detail. Subsequently, some latest results on the variance-constrained multi-objective control and filtering problems for the nonlinear stochastic systems are summarized. Finally, conclusions are drawn, and several possible future research directions are pointed out.

scholarly journals Recent Advances on Recursive Filtering and Sliding Mode Design for Networked Nonlinear Stochastic Systems: A Survey

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Jun Hu ◽  
Zidong Wang ◽  
Hongli Dong ◽  
Huijun Gao
Keyword(s):  
Stochastic Systems ◽  
Sliding Mode ◽  
Future Research ◽  
Missing Measurements ◽  
Nonlinear Stochastic Systems ◽  
Design Problems ◽  
Recursive Filtering ◽  
Mode Design ◽  
Recent Advances ◽  
Randomly Occurring Uncertainties

Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.

Optimization of Antenna Arrays and Microwave Filters Using Differential Evolution Algorithms

2019 ◽  
pp. 1281-1296
Author(s):  
Sotirios K. Goudos
Keyword(s):  
Antenna Arrays ◽  
Future Research ◽  
Design Problems ◽  
Design And Optimization ◽  
De Algorithm ◽  
Future Research Directions ◽  
Microwave Design ◽  
Evolution Algorithms ◽  
Differential Evolution Algorithms ◽  
Self Adaptive

The purpose of this chapter is to briefly describe the DE algorithm and its variants and present their application to antenna and microwave design problems. This chapter presents results from design cases using self-adaptive DE. The chapter discusses the issues, problems, and trends with DE for wireless communications. A brief description of different DE algorithms is also given. The numerical results for different design cases are reported. Moreover, an outline of future research directions is provided. Finally, the chapter concludes and the advantages of using a self-adaptive DE-based approach in the design and optimization of microwave systems and antennas is discussed.

scholarly journals A Review on Analysis and Synthesis of Nonlinear Stochastic Systems with Randomly Occurring Incomplete Information

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Hongli Dong ◽  
Zidong Wang ◽  
Xuemin Chen ◽  
Huijun Gao
Keyword(s):  
Incomplete Information ◽  
Stochastic Systems ◽  
Future Research ◽  
Missing Measurements ◽  
Parameter Uncertainties ◽  
Nonlinear Stochastic Systems ◽  
Randomly Occurring Uncertainties ◽  
Analysis And Synthesis ◽  
System States ◽  
And Control

In the context of systems and control, incomplete information refers to a dynamical system in which knowledge about the system states is limited due to the difficulties in modeling complexity in a quantitative way. The well-known types of incomplete information include parameter uncertainties and norm-bounded nonlinearities. Recently, in response to the development of network technologies, the phenomenon of randomly occurring incomplete information has become more and more prevalent. Such a phenomenon typically appears in a networked environment. Examples include, but are not limited to, randomly occurring uncertainties, randomly occurring nonlinearities, randomly occurring saturation, randomly missing measurements and randomly occurring quantization. Randomly occurring incomplete information, if not properly handled, would seriously deteriorate the performance of a control system. In this paper, we aim to survey some recent advances on the analysis and synthesis problems for nonlinear stochastic systems with randomly occurring incomplete information. The developments of the filtering, control and fault detection problems are systematically reviewed. Latest results on analysis and synthesis of nonlinear stochastic systems are discussed in great detail. In addition, various distributed filtering technologies over sensor networks are highlighted. Finally, some concluding remarks are given and some possible future research directions are pointed out.

Application of Biogeography-Based Optimization to Antennas and Wireless Communications

2021 ◽  
pp. 950-966
Author(s):  
Sotirios K. Goudos
Keyword(s):  
Wireless Communications ◽  
Patch Antenna ◽  
Wireless Systems ◽  
Future Research ◽  
Design Problems ◽  
Research Directions ◽  
Design And Optimization ◽  
Future Research Directions ◽  
Conclusion Section ◽  
Ofdm Signals

The purpose of this chapter is to briefly describe the BBO algorithm and present its application to antenna and wireless communications design problems. This chapter presents results from design cases that include patch antenna, linear antenna array, and a partial transmit sequence (PTS) scheme for OFDM signals based on BBO. The chapter is supported with an adequate number of references. This chapter is subdivided into five sections. The “background” section presents the issues, problems, and trends with BBO. Then the authors briefly present the main BBO algorithm. In the next section, they describe the design cases and present the numerical results. An outline of future research directions is provided in the following section while in the “conclusion” section the authors conclude the chapter and discuss the advantages of using a BBO-based approach in the design and optimization of wireless systems and antennas. Finally, an “additional reading section” gives a list of readings to provide the interested reader with useful sources in the field.

A Comprehensive Survey on Grey Wolf Optimization

2020 ◽  
Vol 13 ◽  
Author(s):  
Isha Sharma ◽  
Vijay Kumar ◽  
Sanjeewani Sharma
Keyword(s):  
Real Life ◽  
Future Research ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Design Problems ◽  
Life Problems ◽  
Engineering Design Problems ◽  
Comprehensive Survey ◽  
Pros And Cons ◽  
Future Research Directions

: Grey wolf optimizer is a recently developed metaheuristic algorithm that mimics the hunting and social behaviour. It has been applied in most of the engineering design problems. Grey wolf optimizer and its variants have been effectively used to solve the real-life applications. For some complex problems, grey wolf optimizer has been hybridized with other metaheuristics. This paper summarizes the overview of grey wolf optimizer and its variants. The pros and cons of these variants have been discussed. The application of grey wolf optimizer have also been discussed with future research directions. This paper will encourage the researchers to use this algorithm for their real-life problems.

Introduction to Fireworks Algorithm

2013 ◽  
Vol 4 (4) ◽  
pp. 39-70 ◽  
Author(s):  
Ying Tan ◽  
Chao Yu ◽  
Shaoqiu Zheng ◽  
Ke Ding
Keyword(s):  
Graphic Processing Unit ◽  
Optimization Process ◽  
Future Research ◽  
Processing Unit ◽  
Research Directions ◽  
Running Time ◽  
Fireworks Algorithm ◽  
Multi Objective ◽  
Speed Up ◽  
Future Research Directions

Inspired by fireworks explosion at night, conventional fireworks algorithm (FWA) was developed in 2010. Since then, several improvements and applications were proposed to improve the efficiency of FWA. In this paper, the conventional fireworks algorithm is first summarized and three improved fireworks algorithms are provided. By changing the ways of calculating the numbers and amplitudes of sparks in fireworks' explosion, the improved FWA algorithms become more reasonable and explainable. In addition, the multi-objective fireworks algorithm and the graphic processing unit (GPU) based fireworks algorithm are also presented, particularly the GPU based fireworks algorithm is able to speed up the optimization process considerably. Extensive experiments on 13 benchmark functions demonstrate that the three improved fireworks algorithms significantly increase the accuracy of found solutions, yet decrease the running time dramatically. At last, some applications of fireworks algorithm are briefly described, while its shortcomings and future research directions are identified.

scholarly journals Recent Advances on Filtering and Control for Nonlinear Stochastic Complex Systems with Incomplete Information: A Survey

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Bo Shen ◽  
Zidong Wang ◽  
Jinling Liang ◽  
Yurong Liu
Keyword(s):  
Complex Systems ◽  
Incomplete Information ◽  
Stochastic Systems ◽  
Future Research ◽  
Control Problems ◽  
Missing Measurements ◽  
Recent Advances ◽  
Future Research Directions ◽  
Control Technologies ◽  
And Control

Some recent advances on the filtering and control problems for nonlinear stochastic complex systems with incomplete information are surveyed. The incomplete information under consideration mainly includes missing measurements, randomly varying sensor delays, signal quantization, sensor saturations, and signal sampling. With such incomplete information, the developments on various filtering and control issues are reviewed in great detail. In particular, the addressed nonlinear stochastic complex systems are so comprehensive that they include conventional nonlinear stochastic systems, different kinds of complex networks, and a large class of sensor networks. The corresponding filtering and control technologies for such nonlinear stochastic complex systems are then discussed. Subsequently, some latest results on the filtering and control problems for the complex systems with incomplete information are given. Finally, conclusions are drawn and several possible future research directions are pointed out.

scholarly journals Time Variant Multi-Objective Interval-Valued Transportation Problem in Sustainable Development

2019 ◽  
Vol 11 (21) ◽  
pp. 6161 ◽  
Author(s):  
Gurupada Maity ◽  
Sankar Roy ◽  
Jose Verdegay
Keyword(s):  
Sustainable Development ◽  
Transportation Problem ◽  
Future Research ◽  
Negative Externalities ◽  
Multi Objective ◽  
Future Research Directions ◽  
The Cost ◽  
Interval Valued

Sustainable development is treated as the achievement of continued economic development without detriment to environmental and natural resources. Now-a-days, in a competitive market scenario, most of us are willing to pay less and to gain more in quickly without considering negative externalities for the environment and quality of life for future generations. Recalling this fact, this paper explores the study of time variant multi-objective transportation problem (MOTP) with consideration of minimizing pollution. Time of transportation is of utmost importance in reality; based on this consideration, we formulate a MOTP, where we optimize transportation time as well as the cost function. The parameters of MOTP are interval-valued, so this form of MOTP is termed as a multi-objective interval transportation problem (MOITP). A procedure is taken into consideration for converting MOITP into deterministic form and then for solving it. Goal programming is applied to solve the converted transportation problem. A case study is conducted to justify the methodology by utilizing the environmental impact. At last, conclusions and future research directions are included regarding our study.

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