Abstract. Credit scoring has gained more and more attentions both in academic world and the business community today. Many modeling techniques have been developed to tackle the credit scoring tasks. This paper presents a Structuretuning Particle Swarm Optimization (SPSO) approach for training feed-forward neural networks (NNs). The algorithm is successfully applied to a real credit problem. By simultaneously tuning the structure and connection weights of NNs, the proposed algorithm generates optimized NNs with problem-matched information processing capacity and it also eliminates some ill effects introduced by redundant input features and the corresponding redundant structure. Compared with BP and GA, SPSO can improve the pattern classification accuracy of NNs while speeding up the convergence of training process.

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Water pollution has posed a severe problem in modern society. Evaluation of water quality is a meaningful topic today. To identify the specific water category and predict the water quality in the future, a Particle Swarm Optimization (PSO) based Artificial Neural Network (ANN) approach is presented. The data investigated from the Yangtze River are chosen as the original cases to construct the ANN model and testify both the classification and prediction ability of this method. Compared with other classical methods, the proposed one can obtain high quality and efficiency without losing computational expense. Experimental results show PSO is a robust training algorithm and could be extended to other real world pattern classification and prediction applications.

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