Based on the existing intelligent algorithms such as particle swarm optimization (PSO) and gene expression programming (GEP), this paper proposes a hybrid intelligent algorithm to diagnose the faults of a certain type...
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ISBN:
(纸本)9781728125992
Based on the existing intelligent algorithms such as particle swarm optimization (PSO) and gene expression programming (GEP), this paper proposes a hybrid intelligent algorithm to diagnose the faults of a certain type of equipment combination and floor quickly. At the same time, the quantum theory is introduced, and the adaptive quantum particle swarm optimization algorithm and the quantum neural network are constructed to extract fault features and fault diagnosis algorithm. A hybrid intelligent diagnosis core algorithm library based on Genetic Wavelet particle swarm optimization neural network is established and verified by simulation.
For numerical high-dimensional real parameter optimization problem, a new clonal selection memory algorithm is proposed in the paper by introducing a short-term memory mechanism into the optimization algorithm. Throug...
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For numerical high-dimensional real parameter optimization problem, a new clonal selection memory algorithm is proposed in the paper by introducing a short-term memory mechanism into the optimization algorithm. Through non-genetic information storage and guiding the subsequent evolution the algorithm can effectively increase the local convergence rate. On the other hand, the search depth threshold setting, supplement operator and crossover operator makes the algorithm can effectively escape local optimum traps. Combined with other intelligent algorithms, the simulation experiment of the 20 and 30 dimensional standard test functions show that the new algorithm has obvious advantages in convergence speed, convergence precision and global convergence. Furthermore, the simulation experiment of the 100 and 200 dimensional standard test functions shows that, the new algorithm shows better global convergence speed, convergence precision and stability.
This paper focuses on the optimal design of the near space navigation network configuration concerning a specific target *** design not only ensure navigation service for specific area with high accuracy and low cost;...
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This paper focuses on the optimal design of the near space navigation network configuration concerning a specific target *** design not only ensure navigation service for specific area with high accuracy and low cost;but also enable all users in the target area to obtain a controlled *** this design,a double-layer Y configuration is adopted as the optimal basic *** algorithm and genetic algorithm are selected to detect the boundary of the configuration to realize the synchronization optimization of cost and *** with the working principle of aerostat in the stratosphere airspace,we design a platform dynamic trajectory and provide an algorithm optimization of layout for the partial users’worse precision in dynamic *** simulation results show that the optimal design method proposed in this paper can meet the configuration design requirements of the dynamic navigation network based on near space with different size area and different precision requirement.
Considering the shortage of intelligent algorithm in the multi-objective optimization,in this paper,we proposed a particle swarm optimization(PSO) algorithm which adaptive adjustment learning *** algorithm dynamically...
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ISBN:
(纸本)9781467397155
Considering the shortage of intelligent algorithm in the multi-objective optimization,in this paper,we proposed a particle swarm optimization(PSO) algorithm which adaptive adjustment learning *** algorithm dynamically adjust the learning factor to make it avoid falling into local minima,then with the help of average thought,choosing the best through the comparison of fitness and average *** introducing shrinkage factor,improve the convergence of *** compared with the standard pso algorithm and other improved pso algorithm in the classic functions,the effect is ***,simulating on the aeroengine multivariable robust H controller design,the simulation results show that the algorithm has good performance.
Compared with chance constraints model,integrated chance constraints model has better property about feasible solution set and measures risk more accurately in economy *** this paper,we introduce four definitions of I...
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Compared with chance constraints model,integrated chance constraints model has better property about feasible solution set and measures risk more accurately in economy *** this paper,we introduce four definitions of ICC models,and discuss the properties including convexity,continuum and *** we present a hybrid intelligent algorithm,which is test efficiently by numerical *** last,as a new methord to measure risk,conditional valu-at risk is studied as the application of ***-CVaR problem can be computed efficiently using our model and algorithm,which dramatically improves the portfolios of investment.
DE, which is a well-known intelligence optimization algorithm, has been widely used in solving science and engineering problems. However, the search performance of DE algorithm depends on control parameter settings. I...
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DE, which is a well-known intelligence optimization algorithm, has been widely used in solving science and engineering problems. However, the search performance of DE algorithm depends on control parameter settings. It is possible that poor settings for the parameters may cause the population to move towards undesirable values. In order to improve the performance of DE, this paper proposed an improved optimization algorithm based on JADE(MJADE). In MJADE, the number of best individuals selected from the current population is dynamically adjusted to keep a balance between exploration and exploitation. Moreover, a modified mutation operation is designed. To verify the effectiveness of MJADE, numerical experiments are carried on ten benchmark problems from CEC2014. In addition, an application problem of artificial neural network is examined. The experimental results show that MJADE is competitive with respect to other compared algorithms in this simulation example.
Simple and friendly operation panel, intelligent algorithm, high performance control and driving circuit and precision and reliable plant are the necessary conditions for music playing robot design. In this paper, a h...
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ISBN:
(纸本)9781467317900
Simple and friendly operation panel, intelligent algorithm, high performance control and driving circuit and precision and reliable plant are the necessary conditions for music playing robot design. In this paper, a hierarchical control structure, host controller (PC) and local controller (FPGA), is proposed to implement the anthropomorphic piano robot control with parallel controlling of two hands and ten fingers. In order to simplify the programming on the host controller and increase the performance of the whole system, the actuators of the hands (linear motors) and fingers (servo motors) are controlled and driving by local controllers, the FPGA-based controllers. The host PC is in charge of to integrate and encode the music codes of playing music to command the robot via the local controller, namely FPGA controller or distributed control module. The host controller is programmed with an intelligent algorithm to generate the music control code and an interactive man-machine interface. Giving a music score, the intelligent algorithm will generate a series of optimum positions commands for the hands and fingers of the piano robot to play the piano. The series of optimum positions commands are programmed with crashing protection and minimum movement for the hands and fingers to anthropomorphize the robot. After the codes receiving, the local controller will quick decode the commanding such as the position of two hands, opening angle of ten fingers and key rapping of ten fingers to control the driving circuits of actuators of the hands and fingers.
A block oriented nonlinear system consists of a series of blocks. These blocks represent both memoryless nonlinearity and linear dynamics that comprise the overall input-output dynamics of system. Under this category,...
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ISBN:
(纸本)9781510806450
A block oriented nonlinear system consists of a series of blocks. These blocks represent both memoryless nonlinearity and linear dynamics that comprise the overall input-output dynamics of system. Under this category, Hammerstein model is one of the block oriented model. The parameter identification method of Hammerstein model is proposed in this paper. The basic idea is that the nonlinear transfer function of the Hammerstein model can be changed to an intermediate model initially. Then, the parameters of the intermediate model are obtained via an improved particle swarm optimization algorithm. Next, through the relations of the parameters of the intermediate model and those of the Hammerstein model, the estimates of the parameters of the Hammerstein model are obtained. Finally, in simulation experiments, compared with other methods, the feasibility of the presented method is demonstrated.
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