Intelligent recommendation method for offline course resources tax law based on chaos particle swarm optimization algorithm
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In view of the individual differences in learners’ abilities, learning objectives, and learning time, an intelligent recommendation method for offline course resources of tax law based on the chaos particle swarm optimization algorithm is proposed to provide personalized digital courses for each learner. The concept map and knowledge structure theory are comprehended to create the network structure map of understanding points of tax law offline courses and determine the learning objectives of learners; the project response theory is used to analyze the ability of different learners; According to the learners’ learning objectives and ability level, the intelligent recommendation model of offline course resources of tax law is established with the minimum concept difference, minimum ability difference, minimum time difference, and minimum learning concept imbalance as the objective functions; Through the cultural framework, the chaotic particle swarm optimization algorithm based on the cultural framework is obtained by combining the particle swarm optimization algorithm and the chaotic mapping algorithm; The algorithm is used to solve the intelligent recommendation model, and the intelligent recommendation results of offline course resources in tax law are obtained. The experiential outcomes indicate that the process has a smaller inverse generation distance, larger super-volume, and smaller distribution performance index when solving the model; that is, the convergence performance and distribution performance of the model is better; This method can effectively recommend offline course resources of tax law for learners intelligently, and the minimum normalized cumulative loss gain is about 0.75, which is significantly higher than other methods, that is, the effect of intelligent recommendation is better.The application of artificial intelligence-based techniques has covered a wide range of applications related to electric power systems (EPS). Particularly, a metaheuristic technique known as Particle Swarm Optimization (PSO) has been chosen for the tuning of parameters for Power System Stabilizers (PSS) with success for relatively small systems. This article proposes a tuning methodology for PSSs based on the use of PSO that works for systems with ten or even more machines. Our new methodology was implemented using the source language of the commercial simulation software DigSilent PowerFactory. Therefore, it can be translated into current practice directly. Our methodology was applied to different test systems showing the effectiveness and potential of the proposed technique.
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Chaotic economic time series are studied by using time series analyzing methods. Some simple theories and methods of building model about chaotic economic time series are introduced in this article. Extracting nonlinear (or chaotic) messages from chaotic time series and identifying system are set forth. In this paper, deciding region of chaotic critical spot on chaotic system is also discussed. Some good results are obtained by testing realistic case and programs calculating.
Identification
Chaotic systems
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Unimodel chaotic spread-spectrum sequence is easy to be attacked for its poor secrecy, a serial-fabric chaotic spread-spectrum sequence is therefore established by using the output of one chaotic system as the initial condition for another chaotic systems. These two chaotic systems are both ummodel maps and they are therefore easy for digital realization, and the initial value of the second chaotic system is changing all the time thereby improving its secrecy- The analysis of correlation property and secrecy with a mathematic model established ,and the attack of the chaotic sequence proposed in this paper and the unimodel chaotic sequence by the tuzzy neural network proved the effectiveness of thsi method.
Sequence (biology)
Realization (probability)
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The authors know that a common and effective way to protect digital images security are to encrypt these images into white noise image. In this study, the authors have designed a new two‐dimensional (2D) chaotic system which is derived from two existing one‐dimensional (1D) chaotic maps. The simulation results show that the new 2D chaotic system is able to produce many 2D chaotic maps by selecting different 1D chaotic maps, and which have the wider chaotic ranges and more complex chaotic behaviours compared with the existing 1D chaotic maps. In order to investigate its applications, using the proposed 2D chaotic maps, the authors design a novel image encryption algorithm. First of all, the original image is scrambled by using the chaotic sequences which are generated by new 2D chaotic maps, Arnold transform and Hilbert curve. Then the scrambled image is confused and diffused by chaotic sequences. Finally, the performance of the proposed encryption algorithm is simulated and the experimental results show that the validity and reliability of the proposed algorithm is validated.
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This paper introduces a series-wound framework to generate a large number of new one-dimensional (1D) chaotic maps using a combination of two different 1D chaotic maps (called seed maps). Examples and experimental analysis demonstrate that the newly generated chaotic maps have more parameters, larger chaotic ranges, and better chaotic behaviors than their corresponding seed maps.
Chaotic map
Chaotic systems
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In order to inverse divisional mechanic parameters and overcome shortcomings of conventional Particle Swarm Optimization(PSO),improved PSO algorithms such as stretching objective function based PSO,Metropolis algorithm based PSO and adaptive algorithm based PSO were used and compared with Quantum Particle Swarm Optimization Algo rithm and catastrophic PSO.The case study showed that improved PSO was more effective than conventional PSO,and fit ted to inverse divisional mechanic parameters of dam.
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Second Generation Particle Swarm Optimization (SGPSO) is a new swarm intelligence optimization algorithm. SGPSO is based on the PSO. But the SGPSO will sufficiently utilize the information of the optimum swarm. The optimum swarm consists of the local optimum solution of every particle. In the SGPSO, every particle in the swarm not only moves to the local optimum solution and the global optimum solution, but also moves to the geometric center of optimum swarm. SGPSO, PSO and PSO with Time-Varying Acceleration Coefficients(PSO TVAC) are compared on some benchmark functions. And experiment results show that SGPSO performs better in the accuracy and in getting red of the premature than PSO and PSO_TVAC. And according to the different swarm centers which every particle moves to, I will show some kinds of the variation of SGPSO.
Swarm intelligence
Benchmark (surveying)
Local optimum
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This paper studies the precision threshold of chaotic sequences.The results of simulation showed that the precision of classical chaotic sequences is very high.At present several modified methods of Logistic chaotic sequence decrease the need for precision obviously.These methods are analyzed and compared and the shrinking method with the best performance is improved,which can decrease calculation volume obviously.Furthermore,it is easier to realize in hardware than conventional chaotic sequences.
Sequence (biology)
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The presented paper contains comparison two algorithms for searching optimal value of PID controllers: particle swarm optimization (PSO) and fractional-order particle swarm optimization (F-PSO) for the control system of capsubot robot. A cost function which is used with PSO and F-PSO is presented. At the end, the obtained simulation results are shown and discussed.
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