分析工业机器人选择基于一个模糊的神经网络在三角模糊数字下的模糊神经网络
Ihsan Ullah1, Saleem Abdullah1, Ariana Abdul Rahimzai2
1Department of Mathematics, Abdul Wali Khan University Mardan, Mardan, KP, Pakistan.
Scientific reports
|October 1, 2025
概括
选择最佳的工业机器人是一项挑战. 本研究引入了一种新的三角模糊神经网络 (TFNN) 决策模型,以确定最适合巴基斯坦制片公司的机器人,提高选择准确度.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 决策科学 决策科学 决策科学
背景情况:
- 选择合适的工业机器人是复杂的,因为许多可用的模型.
- 生产公司需要强大的方法来选择最佳的机器人,以满足特定需求.
- 现有的决策过程可能缺乏复杂工业环境所需的精度.
研究的目的:
- 开发和应用一个新的三角模糊神经网络 (TFNN) 决策模型用于工业机器人选择.
- 为了解决巴基斯坦制片公司在选择最适合机器人的特定挑战.
- 整合专家知识和模糊逻辑,以实现更准确的选择过程.
主要方法:
- 引入了一种新的三角模糊神经网络 (TFNN) 与Yager聚合运算符.
- 通过使用三角模糊数字 (TFN) 收集专家信息.
- 距离测量技术和Yager聚合被用于重量计算和跨网络层信息聚合.
主要成果:
- TFNN模型成功处理了专家信息,并计算了标准权重.
- 该模型通过隐藏和输出层汇总信息,生成分数值.
- 使用激活函数实现了机器人的最终排名,确定了最合适的选项.
结论:
- 拟议的TFNN决策模型为工业机器人选择提供了有效的框架.
- 模糊逻辑和神经网络的整合提高了复杂决策任务的精度.
- 这种方法为寻求优化机器人投资的生产公司提供了一个有价值的工具.
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