Early damage detection not only improves the safety and reliability of structures but also reduces maintenance cost. However, damage detection is difficult to implement in large structures under ambient excitation bec...
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Early damage detection not only improves the safety and reliability of structures but also reduces maintenance cost. However, damage detection is difficult to implement in large structures under ambient excitation because of the limitation of sensors, the uncertainty of ambient excitation, and the global properties of modal frequencies and displacement modes. This paper proposes a new damage detection method that employs the real encoding multi-swarmparticle swarm optimization algorithm and fitness functions evolved from strain modes to find the optimal match between measured and simulated modal parameters and to determine the actual condition of structures. The proposed method requires low-frequency modes and incomplete modes and does not require mass normalization of parameters, thus making the method suitable for nondestructive dynamic damage detection of large structures under ambient excitation. Taking a concrete guide wall structure as an example, this paper studied the global searching performance and the sensitivity of the proposed method. The efficiency of the proposed method was analyzed by using different noise levels and sensor numbers. Results show that the proposed method is effective and can be applied in many types of large structures.
In this paper, a novel grey prediction model is proposed to enhance the performance of prediction for the amount of fixed-line and cellular phone subscribers in Japan. The cubic spline function is first integrated int...
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In this paper, a novel grey prediction model is proposed to enhance the performance of prediction for the amount of fixed-line and cellular phone subscribers in Japan. The cubic spline function is first integrated into grey prediction model to enhance its prediction capability. Then the particleswarmoptimization (PSO) algorithm is applied, so that the prediction performance can be improved further. The prediction results using proposed models are very satisfactory. (C) 2013 Elsevier Inc. All rights reserved.
This study is dedicated to propose a cluster analysis algorithm which is integration of artificial immune network (aiNet) and K-means algorithm (aiNetK). Four benchmark data sets, Iris, Wine, Glass, and Breast Cancer,...
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This study is dedicated to propose a cluster analysis algorithm which is integration of artificial immune network (aiNet) and K-means algorithm (aiNetK). Four benchmark data sets, Iris, Wine, Glass, and Breast Cancer, are employed to testify the proposed algorithm. The computational results reveal that aiNetK is superior to particle swam optimization and artificial immune system-related methods.
The electromagnetic shunt damping absorber (EMSDA) is developed based on electromagnetic shunt damping mechanism. The governing equation of planar vibration system equipped with the EMSDA is derived. An optimization m...
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The electromagnetic shunt damping absorber (EMSDA) is developed based on electromagnetic shunt damping mechanism. The governing equation of planar vibration system equipped with the EMSDA is derived. An optimization method is presented to determine the main working parameters of EMSDA on the basis of the built theoretical model. The objective function minimizing the response variance of system under white noise excitation is formulated. The particle swarm optimization algorithm is employed in optimization. The simulated and experimental studies on vibration control by use of EMSDA are conducted. The results show that the electromagnetic shunt damping absorber can attenuate significantly the structural vibration.
In order to achieve precise collision avoidance for large ships, a novel intelligent collision avoidance control approach is presented in this paper. To obtain the precise collision avoidance information capability, a...
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ISBN:
(纸本)9781479931972
In order to achieve precise collision avoidance for large ships, a novel intelligent collision avoidance control approach is presented in this paper. To obtain the precise collision avoidance information capability, a fuzzy set interpretation is developed to handle imprecise information. This allows the introduction of an innovative self-training optimizing search method. The optimizing process is based on the particle swarm optimization algorithm and off-line training data that is obtained from trial manoeuvres based on computer simulations. The resulting controller can decrease the ship operators' burden to deal with bridge data and help them to make timely and precise collision avoidance decision. The results show that the designed intelligent controller performed well to implement the optimizing control of ship collision avoidance.
At present, building energy conservation is a hot topic in urban construction and energy conservation research. Predicting the trend of energy consumption is very meaningful for a whole building energy management. Com...
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ISBN:
(纸本)9781479943678
At present, building energy conservation is a hot topic in urban construction and energy conservation research. Predicting the trend of energy consumption is very meaningful for a whole building energy management. Compared with the other feed-forward neural networks, RBF network learning faster and the ability of function approximation is stronger, but its performance still need to be improved. We use particle swarm optimization algorithm (PSO) to optimize RBF neural network and use the optimized RBF neural network to predict energy consumption in this article. Used the statistical data of the whole society's monthly electricity consumption published online as a sample, and simulated the forecasting method by MATLAB.
In manufacturing process of automobile, people always provide steady illumination during the time when automobile moves on the assembly line. Automobile is moving slowly on the assembly line for surface detection, pai...
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ISBN:
(纸本)9784907764456
In manufacturing process of automobile, people always provide steady illumination during the time when automobile moves on the assembly line. Automobile is moving slowly on the assembly line for surface detection, painting or other operations in the factory, for lower power consumption and giving a satisfaction sufficient to meet a requirement of illumination on the specified surface of automobile, we consider an surface illumination controlling system by establishing a virtual scene for these works. To achieve this target, we provide (i) Illumination Environment Simulation;(ii) Illumination Model;(iii) particle swarm optimization algorithm. The performance of the proposed algorithm shows that we achieved illumination rendering on the surface of automatic vehicle.
Common algorithms of selecting hidden unit data center in RBF neural networks were first discussed in this essay, i.e. k-means algorithm, subtractive clustering algorithm and orthogonal least squares. Meanwhile, a hyb...
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ISBN:
(纸本)9783037859155
Common algorithms of selecting hidden unit data center in RBF neural networks were first discussed in this essay, i.e. k-means algorithm, subtractive clustering algorithm and orthogonal least squares. Meanwhile, a hybrid algorithm mixed of k-means algorithm and particle swarm optimization algorithm was put forward. The algorithm used the position of the particles in particle swarm optimization algorithm to help deal with the defects of local clusters resulted from k-means algorithm and to make optimization with the optimal fitness of k-means particleswarm with the aim to make the final optimal fitness better satisfy the requirements.
The flexible job shop scheduling problem (FJSP) is an extension of the classic job shop scheduling problem (JSP), which breaks through the uniqueness of limit resources, allows a procedure in many machines processing ...
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ISBN:
(纸本)9781479937066
The flexible job shop scheduling problem (FJSP) is an extension of the classic job shop scheduling problem (JSP), which breaks through the uniqueness of limit resources, allows a procedure in many machines processing and one machine processing many kinds of different types of procedures. It is more practical and complex than JSP. The computational complexity of FjSP is much higher, which disables exact solution methods and makes heuristic approaches more qualified. A hybrid optimizationalgorithm, CPSO, based on the cultural algorithm and particle swarm optimization algorithm, is proposed in this paper to solve the FJSP. The objective is to minimize makespan. Computational results show that this hybrid method is able to solve efficiently these kinds of problems.
In this paper, based on particleswarmoptimization (PSO) algorithm to observe the different optimization results by changing the objective function. By comparing indicators of various types of objective function, cle...
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ISBN:
(纸本)9781479958252
In this paper, based on particleswarmoptimization (PSO) algorithm to observe the different optimization results by changing the objective function. By comparing indicators of various types of objective function, clearly showing its intuitive respective advantages and disadvantages. Herein we can derived from the comprehensive objective function is a relatively good target function, stability, accuracy and rapidity performance can better meet the requirements of people.
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