This paper is concerned with the L2- L∞state estimation problem for discrete-time delay neural networks with missing measurements and randomly occurring sensor linearity. The phenomena of missing measurements and ran...
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Dynamic optimization problems(DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-di...
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Dynamic optimization problems(DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-differentiable or explicit mathematical descriptions do not exist. Recently, evolutionary algorithms are gaining popularity for DOPs as they can be used as robust alternatives when the deterministic techniques are invalid. In this article, a technology named ranking-based mutation operator(RMO) is presented to enhance the previous differential evolution(DE) algorithms to solve DOPs using control vector parameterization. In the RMO, better individuals have higher probabilities to produce offspring, which is helpful for the performance enhancement of DE algorithms. Three DE-RMO algorithms are designed by incorporating the RMO. The three DE-RMO algorithms and their three original DE algorithms are applied to solve four constrained DOPs from the literature. Our simulation results indicate that DE-RMO algorithms exhibit better performance than previous non-ranking DE algorithms and other four evolutionary algorithms.
Coevolutionary particle swarm optimization（CPSO） algorithm has been investigated and applied in the real world *** tackling the large-scale and complex real time optimization problems,the running time of CPSO algori...
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ISBN:
(纸本)9781538629185
Coevolutionary particle swarm optimization（CPSO） algorithm has been investigated and applied in the real world *** tackling the large-scale and complex real time optimization problems,the running time of CPSO algorithm is a *** this paper,Graphics processing Unit（GPU） is introduced to provide speedup in order to meet the real time *** CPSO algorithm has been implemented on GPU concurrently using the CUDA *** performance and run time of CPU-based and GPU-based CPSO algorithms are compared in *** experiment result shows that the GPU-based parallel computation mode can shorten the run time of CPSO algorithm apparently.
This paper investigates the problem of H∞ filter design for a class of nonlinear networked system based on T-S fuzzy model. Multiple stochastic time-varying delays and some incomplete information are considered simul...
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ISBN:
(纸本)9781467374439
This paper investigates the problem of H∞ filter design for a class of nonlinear networked system based on T-S fuzzy model. Multiple stochastic time-varying delays and some incomplete information are considered simultaneously. Incomplete information includes randomly occurring sensor saturation and packet dropouts. Stochastic time-varying delays are depicted as a sequence of stochastic and independent variables, which take values on 0 and 1. Two sets of Bernoulli distributed white noises are introduced to describe randomly occurring sensor saturation and packet dropouts. System conservatism is reduced due to introduce an approach of piecewise quadratic Lyapunov function. By solving a set of linear matrix inequalities(LMIs), the filter parameters are obtained. Finally, a simulation example is provided to illustrate the effectiveness of the proposed filter design approach.
This paper focuses on the problem of adaptive output feedback stabilization for a class of stochastic nonlinear system with unknown control *** using a linear state transformation,the unknown control coefficients are ...
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ISBN:
(纸本)9781479900305
This paper focuses on the problem of adaptive output feedback stabilization for a class of stochastic nonlinear system with unknown control *** using a linear state transformation,the unknown control coefficients are lumped together, such that the original system is transformed to a new system for which control design becomes *** employing the inputdriven observer,a novel adaptive neural network(NN) output-feedback controller which only contains one adaptive parameter is developed for such systems by using backstepping technique and NNs' *** proposed control design guarantees that all the signals in the closed-loop systems are 4-moment semi-globally uniformly ultimately bounded.
The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid （TPA）. The liquid-phase catalytic oxidation of p-xylene （PX） to TPA is regarded as a critical and efficient chemi...
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The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid （TPA）. The liquid-phase catalytic oxidation of p-xylene （PX） to TPA is regarded as a critical and efficient chemicalprocess in industry [ 1 ]. PX oxidation reaction involves many complex side reactions, among which acetic acid combustion and PX combustion are the most important. As the target product of this oxidation process, the quality and yield of TPA are of great concern. However, the improvement of the qualified product yield can bring about the high energy consumption, which means that the economic objectives of this process cannot be achieved simulta- neously because the two objectives are in conflict with each other. In this paper, an improved self-adaptive multi-objective differential evolution algorithm was proposed to handle the multi-objective optimization prob- lems. The immune concept is introduced to the self-adaptive multi-objective differential evolution algorithm （SADE） to strengthen the local search ability and optimization accuracy. The proposed algorithm is successfully tested on several benchmark test problems, and the performance measures such as convergence and divergence metrics are calculated. Subsequently, the multi-objective optimization of an industrial PX oxidation process is carried out using the proposed immune self-adaptive multi-objective differential evolution algorithm （ISADE）. optimization results indicate that application oflSADE can greatly improve the yield of TPA with low combustion loss without degenerating TA quality.
In the paper, a new process monitoring approach is proposed for handling the multimode problem in the industrial processes. The original space can be separated into two different parts, which are the common part and t...
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This paper is concerned with the problem of distributed state estimation for linear discrete system with packet loss.A state prediction consensus mechanism is introduced to promote the estimation *** applying matrix t...
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ISBN:
(纸本)9781538629185
This paper is concerned with the problem of distributed state estimation for linear discrete system with packet loss.A state prediction consensus mechanism is introduced to promote the estimation *** applying matrix theory,a sufficient condition for the convergence of the estimation error system is ***,a numerical example is given to show the algorithm proposed in this paper has better consensus performance compared with a classical Kalman consensus filtering（KCF） by some simulations in real and packet-dropping cases,***,the influence of consensus coefficient and prediction consensus coefficient on the estimation error covariance are presented by simulation.
Wireless sensor networks utilize large numbers of sensors to perform sensing and forwarding tasks in the monitoring *** to the resources constraints of sensors and the energy efficiency QoS requirement,prolonging the ...
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ISBN:
(纸本)9781509009107
Wireless sensor networks utilize large numbers of sensors to perform sensing and forwarding tasks in the monitoring *** to the resources constraints of sensors and the energy efficiency QoS requirement,prolonging the network lifetime is a major concern in *** is an efficient approach to reduce energy consumption since it can reduce data transmission of the *** most traditional clustering protocols,the cluster heads（CHs） aggregate the coming data from their cluster members（CMs） and send the aggregated data to the remote sink node directly or visa other ***,assigning a node to serve as CHs and relay nodes simultaneously may result in unbalanced energy *** energy efficient overlapping clustering and relay node selection algorithm is proposed,which selects different nodes as CHs and relay node to attain the balancing of energy consumption of the network.A direction fitness metric is designed to select the proper boundary nodes in the overlapping area to serve as relay *** results show that the proposed algorithm significantly prolongs the network lifetime.
The leakage of hazardous gases poses a significant threat to public security and causes environmental *** effective and accurate source term estimation(STE)is necessary when a leakage accident ***,most research genera...
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The leakage of hazardous gases poses a significant threat to public security and causes environmental *** effective and accurate source term estimation(STE)is necessary when a leakage accident ***,most research generally assumes that no obstacles exist near the leak source,which is inappropriate in practical *** solve this problem,we propose two different frameworks to emphasize STE with obstacles based on artificial neural network(ANN)and convolutional neural network(CNN).Firstly,we build a CFD model to simulate the gas diffusion in obstacle scenarios and construct a benchmark ***,we define the structure of ANN by searching,then predict the concentration distribution of gas using the searched model,and optimize source term parameters by particle swarm optimization(PSO)with well-performed cost ***,we propose a one-step STE method based on CNN,which establishes a link between the concentration distribution and the location of ***,we propose a novel data processing method to process sensor data,which maps the concentration information into feature *** comprehensive experiments illustrate the performance and efficiency of the proposed methods.
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