To generate a test set for a given circuit (including both combinational and sequential circuits), choice of an algorithm within a number of existing test generation algorithms to apply is bound to vary from circuit t...To generate a test set for a given circuit (including both combinational and sequential circuits), choice of an algorithm within a number of existing test generation algorithms to apply is bound to vary from circuit to circuit. In this paper, the genetic algorithms are used to construct the models of existing test generation algorithms in making such choice more easily. Therefore, we may forecast the testability parameters of a circuit before using the real test generation algorithm. The results also can be used to evaluate the efficiency of the existing test generation algorithms. Experimental results are given to convince the readers of the truth and the usefulness of this approach.展开更多
The ability to perform short-term traffic flow forecasting is a crucial component of intelligent transportation systems. However, accurate and reliable traffic flow forecasting is still a significant issue due to the ...The ability to perform short-term traffic flow forecasting is a crucial component of intelligent transportation systems. However, accurate and reliable traffic flow forecasting is still a significant issue due to the complexity and variability of real traffic systems. To improve the accuracy of short-term traffic flow forecasting, this paper presents a novel hybrid prediction framework based on Support Vector Regression （SVR） that uses a Random Forest （RF） to select the most informative feature subset and an enhanced Genetic Algorithm （GA） with chaotic characteristics to identify the optimal forecasting model parameters. The framework is evaluated with real-world traffic data collected from eight sensors located near the 1-605 interstate highway in California. Results show that the proposed RF- CGASVR model achieves better performance than other methods.展开更多
For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model（HMM） and least square support vector machin...For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model（HMM） and least square support vector machine（LS-SVM） is presented. The multi-agent genetic algorithm（MAGA） is used to estimate parameters of HMM to overcome the problem that the Baum-Welch algorithm is easy to fall into local optimal solution. The state condition probability is introduced into the HMM modeling process to reduce the effect of uncertain factors. MAGA is used to estimate parameters of LS-SVM. Moreover, pruning algorithms are used to estimate parameters to get the sparse approximation of LS-SVM so as to increase the ranging performance. On the basis of these, the combined forecast model of electronic equipment states is established. The example results show the superiority of the combined forecast model in terms of forecast precision,calculation speed and stability.展开更多
In this era of VLSI circuits, testability is truly a very crucial issue.To generate a test set for a given circuit, choice of an algorithm from a number ofexisting test generation algorithms to apply is bound to vary ...In this era of VLSI circuits, testability is truly a very crucial issue.To generate a test set for a given circuit, choice of an algorithm from a number ofexisting test generation algorithms to apply is bound to vary from circuit to circuit.In this paper, the Genetic Algorithm is used in order to construct an accurate modelfor some existing test generation algorithms that are being used everywhere in theworld. Some objective quantitative measures are used as an effective tool in makingsuch choice. Such measures are so important to the analysis of algorithms that theybecome one of the subjects of this work.展开更多
基金This work was supported by National Natural Science Foundation of China (NSFC) under the grant !No. 69873030
文摘To generate a test set for a given circuit (including both combinational and sequential circuits), choice of an algorithm within a number of existing test generation algorithms to apply is bound to vary from circuit to circuit. In this paper, the genetic algorithms are used to construct the models of existing test generation algorithms in making such choice more easily. Therefore, we may forecast the testability parameters of a circuit before using the real test generation algorithm. The results also can be used to evaluate the efficiency of the existing test generation algorithms. Experimental results are given to convince the readers of the truth and the usefulness of this approach.
基金supported by the Science and Technology Department of Sichuan Province of China (Nos. 2017JY0007, 2016JY0073, and 2016JZ0031)the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministrythe Fundamental Research Funds for the Central Universities (No. ZYGX2015J063)
文摘The ability to perform short-term traffic flow forecasting is a crucial component of intelligent transportation systems. However, accurate and reliable traffic flow forecasting is still a significant issue due to the complexity and variability of real traffic systems. To improve the accuracy of short-term traffic flow forecasting, this paper presents a novel hybrid prediction framework based on Support Vector Regression （SVR） that uses a Random Forest （RF） to select the most informative feature subset and an enhanced Genetic Algorithm （GA） with chaotic characteristics to identify the optimal forecasting model parameters. The framework is evaluated with real-world traffic data collected from eight sensors located near the 1-605 interstate highway in California. Results show that the proposed RF- CGASVR model achieves better performance than other methods.
文摘For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model（HMM） and least square support vector machine（LS-SVM） is presented. The multi-agent genetic algorithm（MAGA） is used to estimate parameters of HMM to overcome the problem that the Baum-Welch algorithm is easy to fall into local optimal solution. The state condition probability is introduced into the HMM modeling process to reduce the effect of uncertain factors. MAGA is used to estimate parameters of LS-SVM. Moreover, pruning algorithms are used to estimate parameters to get the sparse approximation of LS-SVM so as to increase the ranging performance. On the basis of these, the combined forecast model of electronic equipment states is established. The example results show the superiority of the combined forecast model in terms of forecast precision,calculation speed and stability.
文摘In this era of VLSI circuits, testability is truly a very crucial issue.To generate a test set for a given circuit, choice of an algorithm from a number ofexisting test generation algorithms to apply is bound to vary from circuit to circuit.In this paper, the Genetic Algorithm is used in order to construct an accurate modelfor some existing test generation algorithms that are being used everywhere in theworld. Some objective quantitative measures are used as an effective tool in makingsuch choice. Such measures are so important to the analysis of algorithms that theybecome one of the subjects of this work.
