模糊优选神经网络模型在水资源价值评价中的应用

A Model of Fuzzy Optimization Neural Networks and Its Application in Water Resources Value Evaluation

为了克服水资源价值评价中可变模糊综合评价模型的平方准则、距离参数及权重确定方法选取主观性的不足,本文在综合模糊优选模型和神经网络理论的基础上,运用神经网络的自学习能力,通过网络结构的调整自主获得指标权重,提出了基于模糊优选神经网络的水资源价值评价模型。提出的模型通过利用水资源价值模糊综合评价指数,将反映水资源价值不同层次的各种因素综合起来,反映与水资源有关的诸要素的影响。将提出的模型运用到济南市的水资源价值的综合评价中,结果表明模糊优选神经网络模型的评价结果不但与可变模糊综合评价模型相近,而且能够克服可变模糊综合评价模型中的人工定参数的不足,具有较强的实用性,为水资源价值评价提供了一个很好的途径。

In order to overcome the insufficient of strong subjectivity of the square criterion, the distance parameter selection and weight determination method of the variable fuzzy comprehensive evaluation model in the evaluation of the value of water resources, on the basis of the comprehensive fuzzy optimization model and neural network theory, this paper, using neural network self-learning ability and through the adjustment of the network independent to obtain index weight, proposes the water resources value evaluation model based on the fuzzy optimization neural network. The proposed model will be integrated with various factors, which can reflect the different levels of water resources value, and reflect the influence and water resource related factors. The proposed model is used in the comprehensive evaluation of Jinan water resources value, and the results show that the evaluation results of the fuzzy optimization neural network model not only are similar to variable fuzzy comprehensive evaluation model, but also can overcome the strong subjectivity from artificial set parameters in variable fuzzy comprehensive evaluation model. It has stronger practicability, and provides a good way for the water resources value evaluation.