用于决策处理的功能性神经网络，一种具有模糊逻辑的可编程神经元竞赛网络，其中目标操作模型依赖于网络本身
Functional neural network for decision processing, a racing network of programmable neurons with fuzzy logic where the target operating model relies on the network itself
摘要：
在本文中，我们介绍了一种新的人工智能模型，功能神经网络的人类决策过程建模。这种神经网络由多个人工神经元在网络中竞争组成。这些神经元中的每一个都具有类似的结构，由用户独立编程，由意图轮、运动核心和感觉核心组成，代表用户本身并以特定的速度比赛。将讨论神经元的数学公式和网络中多个节点的竞争机制，并将开发具有模糊逻辑的群体决策过程以及将这些概念方法转化为模拟和操作的实际方法。最后，我们将描述一些可能的未来研究方向，在金融，教育和医学领域，包括设计一个智能学习代理的机会，在业务运营监督的应用。我们相信，这种功能性神经网络具有很大的潜力，可以改变我们计算决策的方式，并导致新一代神经形态芯片的无缝人机交互。
英文摘要：
In this paper, we are introducing a novel model of artificial intelligence, the functional neural network for modeling of human decision-making processes. This neural network is composed of multiple artificial neurons racing in the network. Each of these neurons has a similar structure programmed independently by the users and composed of an intention wheel, a motor core and a sensory core representing the user itself and racing at a specific velocity. The mathematics of the neuron's formulation and the racing mechanism of multiple nodes in the network will be discussed, and the group decision process with fuzzy logic and the transformation of these conceptual methods into practical methods of simulation and in operations will be developed. Eventually, we will describe some possible future research directions in the fields of finance, education and medicine including the opportunity to design an intelligent learning agent with application in business operations supervision. We believe that this functional neural network has a promising potential to transform the way we can compute decision-making and lead to a new generation of neuromorphic chips for seamless human-machine interactions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
