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Proceedings ArticleDOI

Evaluating the investment risk of electrical project based on particle swarm optimization with support vector machine optimized

30 Oct 2009-pp 332-335
TL;DR: A hybrid intelligent system is applied to Evaluation of electrical equipment, combining Particle Swarm Optimize Algorithm and Support Vector Machines (SVM) to evaluate the Investment risk of electrical project.
Abstract: In this paper, we use Particle Swarm Optimization with Support Vector Machine Optimized to evaluate the Investment risk of electrical project. A hybrid intelligent system is applied to Evaluation of electrical equipment, combining Particle Swarm Optimize Algorithm (PSO) and Support Vector Machines (SVM). At first, we can make use of PSO obtaining appropriate parameters in order to improve the general recognizing ability of SVM. And then, these parameters are used to develop classification rules and train SVM. The effectiveness of our methodology was verified by experiments comparing BP neural networks with our approach.
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Proceedings ArticleDOI
17 Jun 1990
TL;DR: A comparison of the predictive abilities of both the neural network and the discriminant analysis method for bankruptcy prediction shows that neural networks might be applicable to this problem.
Abstract: A neural network model is developed for prediction of bankruptcy, and it is tested using financial data from various companies. The same set of data is analyzed using a more traditional method of bankruptcy prediction, multivariate discriminant analysis. A comparison of the predictive abilities of both the neural network and the discriminant analysis method is presented. The results show that neural networks might be applicable to this problem

767 citations


"Evaluating the investment risk of e..." refers methods in this paper

  • ...From the table we can see the model based on improved SVM is valid and effective to recognize the investment risk [7]....

    [...]

Journal ArticleDOI
TL;DR: A hybrid intelligent system that predicts the failure of firms based on the past financial performance data, combining rough set approach and neural network is proposed, which implies that the number of evaluation criteria such as financial ratios and qualitative variables is reduced with no information loss through roughSet approach.
Abstract: This paper proposes a hybrid intelligent system that predicts the failure of firms based on the past financial performance data, combining rough set approach and neural network. We can get reduced information table, which implies that the number of evaluation criteria such as financial ratios and qualitative variables is reduced with no information loss through rough set approach. And then, this reduced information is used to develop classification rules and train neural network to infer appropriate parameters. The rules developed by rough set analysis show the best prediction accuracy if a case does match any of the rules. The rationale of our hybrid system is using rules developed by rough sets for an object that matches any of the rules and neural network for one that does not match any of them. The effectiveness of our methodology was verified by experiments comparing traditional discriminant analysis and neural network approach with our hybrid approach. For the experiment, the financial data of 2400 Korean firms during the period 1994–1997 were selected, and for the validation, k-fold validation was used.

308 citations


"Evaluating the investment risk of e..." refers methods in this paper

  • ...The BPN were executed MATLAB NN toolbox [5,6]....

    [...]

Journal ArticleDOI
TL;DR: In this paper, an extension to the case of SVMs with quadratic slack penalties is given and a simple approximation for the evidence is derived, which can be used as a criterion for model selection.

176 citations

Proceedings ArticleDOI
07 Nov 2005
TL;DR: The simulating results show that this modified particle swarm optimization algorithm not only has great advantage of convergence property over standard simple PSO, but also can avoid the premature convergence problem effectively.
Abstract: A modified particle swarm optimization (PSO) algorithm is proposed in this paper to avoid premature convergence with the introduction of mutation operation. The performance of this algorithm is compared to the standard PSO algorithm and experiments indicate that it has better performance with little overhead.

138 citations


"Evaluating the investment risk of e..." refers methods in this paper

  • ...Consequently, the GA-SVM model provides a promising alternative for forecasting electricity load [2]....

    [...]

Proceedings ArticleDOI
15 Jun 2004
TL;DR: A modified particle swarm optimization (PSO) algorithm is proposed in this paper to avoid premature convergence with the introduction of mutation operation and experiments indicate that it has better performance with little overhead.
Abstract: A modified particle swarm optimization (PSO) algorithms is proposed. This method integrates the particle swarm optimization with the simulated annealing algorithm. It can solve the problem of local minimum of the particle swarm optimization, and narrow the field of search continually, so it has higher efficiency of search. This algorithm is applied to the function optimization problem and simulation shows that the algorithm is effective.

24 citations