基于模糊权重的神经网络在延迟和实际时间内的二进制密钥转换

Amitesh Kumar Singam
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引用次数: 0

摘要

翻译的关键事实是为了验证模糊权重。此外,在我们的案例中,模糊权重是基于逻辑推理和逻辑思维的,尽管模糊逻辑与数据无关,但它依赖于基于人脑模仿的思维过程。一般来说,在某种程度上,人类依赖于自己的创造,并试图通过机器语言或所谓的机器教学来理解其用法,这是人类试图理解基于二进制语言的模糊概念,此外,这种翻译意味着我们越深入越复杂,但这里是我们引入模糊权重或逻辑的独创性基于交换理论逻辑设计,产生二进制密钥而不是值,我们专注于通过模糊权重来降低机器教学的复杂性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Translations Of Neural Networks based on Fuzzy Weights for Binary Keys within Delayed and Actual Time
The Key facts of Translations are meant to validated the Fuzzy Weights. Moreover, In our case Fuzzy weights are based on Logical Reasoning and Logical Thinking, even though fuzzy logics are not concerned with data rather it depends on thought process based on human brain Imitation. Generally, at some point mankind depends on his own creations and tries to understand its usage through Machine Language or so called Machine Teaching and this is were humans try to understand fuzzy concepts based on binary language, moreover this translations are meant to become complicated the more we go deeper but here comes our originality of introducing fuzzy weights or logic based on Switching Theory Logical design which produces binary keys instead of values, we concentrated on reducing complexity of Machine Teaching through fuzzy weights.
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