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Review of Livestock Feed Formulation Techniques

机译:牲畜饲料配制技术综述

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This paper reviews animal feed formulation methods, the conventional methods and intelligent system method. Highlighting their cons and pros. The intelligent system method (neuro-fuzzy) incorporated fuzzy conjunctive into levenberge training of artificial neural network. The neuro-fuzzy system was trained with dataset and validated using Amino acid elements of chicks feed. With 0.05 level of significance on NCCS 2000 platforms, output of the neuro-fuzzy system produced a correlation coefficient of 0.888608 and p-value of 0.97. Intelligent system can be employed to increase productivity in the field of animal feed formulation.
机译:本文综述了动物饲料的配制方法,常规方法和智能系统方法。强调他们的利弊。智能系统方法(神经模糊)将模糊结语结合到人工神经网络的levenberge训练中。用数据集训练神经模糊系统,并使用雏鸡饲料中的氨基酸成分对其进行验证。在NCCS 2000平台上具有0.05的显着性水平时,神经模糊系统的输出产生的相关系数为0.888608,p值为0.97。可以采用智能系统来提高动物饲料配方领域的生产率。

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