Informatics Publishing Limited

R. Rajkumar ¹, A. James Albert ², D. Chandrakala ³

Affiliations
1 Department of Mathematics, Kumaraguru College of Technology, Coimbatore- 641049, IN
2 Department of Science and Humanities, Hindustan College of Engineering and Technology, Coimbatore-641032, IN
3 Department of Computer Science, Kumaraguru College of Technology, Coimbatore-641049, IN

Abstract

Background: Weather forecasting has become one of the active research areas due to major scientific and technological problems. An analysis is made to utilize of data mining approaches in weather forecast. Methods: Fuzzy Neural Network (FNN) and Hierarchical Particle Swarm Optimization (HPSO) are used for predicting the weather changes occur in the atmosphere. The FNN uses biological neurons for exact calculation and the neural network adjust their weights by the practice of training. The HPSO is used here for better optimization of weights, also that to improve the overall performance Adaptive Inertia Weight Algorithm (AWA) is proposed. Results: As a result, the total error is reduced with little tolerance which results in accurate weather prediction. The network is trained in such a manner that the model will provide 94% of optimum results. Conclusion: The performances of the proposed algorithm were compared with other existing algorithm using other standard performance metrics which gives best results for the mean weather variables. The results show that the proposed approach is efficient in determining weather forecasting and moreover helps in climate change investigations.

Keywords

Fuzzy Neural Network, Hierarchy Particle Swarm Optimization Technique, Network Forecasting, Numerical Weather Prediction, Weather Forecasting

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D. Chandrakala ¹, S. Sumathi ², S. Karthi ²

Affiliations
1 Department of Computer Science and Engineering, Kumaraguru College of Technology, Coimbatore, IN
2 Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, IN

Abstract

The pattern recognition methods like speech recognition, text classification, image recognition results in the solving of multi-class problems. This can be achieved by means of classifying multi-class problems into several two class problems using the Soft Computing techniques such as Neural Networks and Support Vector Machines. The best code matrix for a given problem cannot be designed taking into account only the features of the code matrix, viz., overall classifier accuracy, minimum hamming distance and margin of classification, but also the features of the problem, viz., attributes, samples and classes are to be considered. Conventionally, code matrix is designed based on either the features of the problem or the features of the code matrix. The proposed work, focused on designing a new code matrix based on both the features of the problem and code matrix. In order to improve the accuracy and reduce computation time, the generation of feature dependent code matrix through an evolutionary algorithm is proposed. This model aims at developing a hybrid version of Recursive Error Correcting Output Code with Biogeography Based Optimization to achieve maximum classification accuracy and minimum computational time.

Keywords

Biogeography Based Optimization, C5.0 Binary Search Tree, Radial Basis Function Neural Network, Recursive Error Correcting Output Codes, Support Vector Machine.

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