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中文摘要：
由于水质的非线性、不确定性等特性，水质预测与评价是很复杂的一个问题；最小二乘支持向量机已经成功地应用于解决非线性问题和时间级数问题。提出一种新的IGALSSM模型，即基于一种新型遗传算法——智能遗传算法参数优选的最小二乘支持向量机模型，并且将提出的模型应用于长江水质的分类识别和预测。实验结果表明，所提出的模型比神经网络有更准确的识别率和更高的预测精度，具有较强的实用价值。
英文摘要：
Forecasting and evaluation water quality is a complicated problem due to its nonlinearity and uncertainty. Least square support vector machine（LSSVM） has been successfully employed to solve regression and time series problem. This paper proposed a novel IGALSSVM model. The model based on a new genetic algorithm, intelligent genetic algorithm to optitnize the parameters of LSSVM. In addition, applied the model to classify and forecast water quality of Changjiang River. Experimental results show that IGALSSVM model performs better than neural networks ,implying that IGALSSVM is very practical.
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The general result of Gol'dberg's theorem concerning the growth of meromorphic solutions of algebrai
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