农作物虫情的模糊神经网络预测模型
Acta Agriculturae Zhejiangensis（2013）
Weinan Normal University
Abstract
利用BP神经网络建立的农作物虫情预测模型,其算法存在收敛速度慢、网络泛化能力差等缺点,可影响预测精度。为进一步提高预测精度,将人工神经网络与模糊系统结合,建立基于模糊神经网络的农作物虫情预测模型;并将该模型与基于BP神经网络算法的预测模型进行比较。结果表明,模糊神经网络的预测模型预测精确度比较高,训练速度比较快;该模型给农作物虫情预测提供了一种新方法。
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fuzzy neural network,pest forecasting model,neuron
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Pretraining has recently greatly promoted the development of natural language processing (NLP)We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performanceWe propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generationThe model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in ChineseExperimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performanceUpload PDF to Generate Summary
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