Data Mining is a powerful technology to help organization to concentrate on most important data by extracting useful information from large database. One of the most commonly used techniques in data mining is Artificial Neural Network due to its high performance in many application domains. Despite many advantages of Artificial Neural Network, one of its main drawbacks is its inherent black box nature which is the main problem of using Artificial Neural Network in data mining. Therefore, this paper proposes a rule extraction algorithm from neural network using classified and misclassified data to convert the black box nature of Artificial Neural Network into a white box. The proposed algorithm is a modification of the existing algorithm, Rule Extraction by Reverse Engineering (RxREN). The proposed algorithm extracts rules from trained neural network for datasets with mixed mode attributes using pedagogical approach. The proposed algorithm uses both classified as well as misclassified data to find out the data ranges of significant attributes in respective classes, which is the innovation of the proposed algorithm. The experimental results clearly show that the performance of the proposed algorithm is superior to existing algorithms.
This paper proposes a hybrid method that integrates the main features of particle swarm optimization (PSO) and evolutionary programming (EP) for solution of non-convex economic load dispatch (ELD) problems having non-linearities like valve point loadings. Algorithms based on PSO, Evolutionary programming (EP) and PSO embedded EP techniques have been developed and tested on a practical nonconvex ELD problem with valve point loading effects considered in the cost functions. Numerical results show that all the algorithms are capable of finding feasible near global solutions within a reasonable time but PSO embedded EP-algorithm with Gaussian mutation appears to outperform the other two in terms of convergence speed, solution time and quality of solution.
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