通过统计模型对神经网络进行比较,标准化酵母发酵过程规范
1Indian Statistical Institute, SQC & OR Unit, Kolkata, India.
Journal of applied statistics
|May 31, 2024
概括
本研究比较了用于控制商业酵母发酵的统计模型和人工神经网络. 反向传播神经网络在预测酵母菌生长模式方面表现出卓越的准确性,提供了强大的控制系统.
科学领域:
- 生物技术是生物技术.
- 化学工程是化学工程的重要组成部分.
- 计算生物学 计算生物学
背景情况:
- 商业酵母发酵涉及复杂的生物化学反应,使得一致的批量生产具有挑战性.
- 传统的方法很难模拟和控制这些工业过程的动态性和时间变化的性质.
研究的目的:
- 开发一个强大的控制系统,用于时间变化的酵母发酵过程.
- 为了比较统计建模技术与非参数人工神经网络的有效性.
主要方法:
- 利用来自工业面包师酵母料批量发酵过程的数据.
- 应用统计方法,包括预测追踪回归.
- 采用非参数的人工神经网络技术,特别是反向传播网络.
主要成果:
- 反向传播神经网络在预测商业酵母菌生长模式方面表现出最高的准确性.
- 预测追踪回归也显示出显著的预测准确性.
- 开发的模型有助于优化参数,以最大限度地减少酵母生产的变化.
结论:
- 人工神经网络,特别是反向传播,为模拟和控制复杂的酵母发酵提供了卓越的方法.
- 统计模型提供了有价值的见解,但在这种应用中神经网络的表现优于神经网络.
- 开发的模型可以导致更一致和优化的商业酵母生产.
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