基于遗传神经网络的机电产品绿色度评价

绿色产品被认为是人类走可持续发展战略的必由之路,采用科学、准确的评价方法对绿色产品进行认证至关重要。传统的评价方法主观因素太强,而人工神经网络模型克服了传统项目评价依赖专家经验的弊端,为产品绿色度评价开辟了新途径。然而神经网络训练时具有训练速度较慢、全局搜索能力弱、易陷于局部极小等缺点,这里提出了用遗传算法优化神经网络,从而提高了产品绿色度评价的准确性。这里首先阐述了遗传神经网络模型的原理,然后利用该模型对机电产品进行实例分析。分析表明,采用该模型获得结果是令人满意的。

The evaluation of product green degree based on GA- BP model

Green product will be the best way to meet the sustainable development, so the scientific and accurate evaluation method is vitally important for the evaluation of Green product. The traditional evaluation method is too subjective, but artificial neural network model overcomes the drawback of relying on expertise to evaluation, which opens up a new way for the evaluation of product green degree. But while training, the neural network has three great shortcomings, so this text has proposed to optimize the neural network by using Genetics Algorithm, which improved the accuracy of the evaluation of product green degree. In this paper, first of all, the author expounds the principle of GA--BP neural network, and then exhibits the new model combining the instance, which is applied in the evaluation of electromechanical product green degree. According to the records of the experiment, it shows perfectly satisfactory results.