A Novel Strategy for Improving the Counter Propagation Artificial Neural Networks in Classification Tasks

Abstract

Counter-Propagation-Artificial-Neural-Networks (C P-ANNs) have been applied in several domains due to their learning and classification abilities. Regardless of their strength, the CP-ANNs still have some limitations in pattern recognition tasks when they encounter ambiguities during the learning process, which leads to the inaccurate classification of the Kohonen-Self-Organizing-Map (K-SOM). This problem has an impact on the performance of the CP-ANNs. Therefore, this paper proposes a novel strategy to improve the CP-ANNs by the Gram-Schmidt algorithm (GSHM) as a pre-processing step of the original data without changing their architecture. Three datasets examples from various domains, such as correlation, crop, and fertilizer, were employed for experimental validation. To obtain the results, we relied on two simulations. The first simulation uses CP-ANNs, and the datasets are inputted into the network without any prior pre-processing. The second simulation uses MCP-ANNs, and the datasets are pre-processed through the GSHM block. Experiment results show that the proposed MCP-ANNs recognize all patterns with a classification accuracy of 100% versus 62.5% for CP-ANNs in the Correlation Dataset. Furthermore, the proposed MCP-ANNs reduce the execution time and training parameter values in all datasets versus CP-ANNs. Thus, the proposed approach based on the GSHM algorithm significantly improves the performance of the CP-ANNs.

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