Algorithm for Calculating Noise Immunity of Cognitive Dynamic Systems in the State Space

Q2 Social Sciences Open Education Studies Pub Date : 2023-08-31 DOI:10.21686/1818-4243-2023-4-52-59
A. Solodov, T. G. Trembach, K. E. Zhovnovatiy
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Abstract

The research method consists in applying the state space method, widely used in the study of automatic dynamical systems, to describe the behavior of cognitive systems. It is assumed, that at the input of the cognitive system, there is a signal and interference described by Poisson point processes, modeling the amount of information, the amount of emotional stress, etc., corresponding to each event. The cognitive properties of the system in the paper are taken into account by two circumstances. Firstly, events localized in time are characterized in the paper not only by the Poisson distribution of the times of their occurrence, but also by some random variables that characterize the importance (significance) events for the system. A typical example is the attribution of a certain amount of information to each event, if an information processing system is modeled. Another example is the emotional reaction of a person to the appearance of stress, described in a classic work on psychology. In this case, the point is the event that causes stress, and the effects of stress on the system are modeled by the relative magnitude of stress in accordance with the Holmes and Rahe scale. Secondly, the cognitive system processes, assimilates, adapts to the impact that each event has on it with its inherent speed. In this paper, this phenomenon is modeled as the passage of a point process through a dynamic system described by differential equations. Such processes are called filtered point processes. Examples of impacts are given and, for simplicity, an assumption is made about the magnitude of the impact as the amount of information received by the system when an event occurs. Thus, the model of a cognitive system is a dynamic system described by a differential equation in the state space, at the input of which messages with a certain information load appear at random discrete moments of time.As for any technical system, the cognitive system faces the task of evaluating the quality of its work. In this regard, the paper substantiates the use of a convenient quality index from an engineering point of view and an appropriate criterion in the form of a signal – interference ratio. The new results are differential equations in the state space for the mathematical expectations of the signal and interference, as well as an algorithm for calculating the noise immunity of the cognitive system. As an example, a graph of the noise immunity of a particular cognitive system is calculated and presented, confirming an intuitive idea of its behavior.In conclusion, it is noted that the main result of the paper is an algorithm for calculating the noise immunity of cognitive systems using differential equations that allow calculating the behavior of non-stationary cognitive systems under any point impacts described by a non-stationary function of the intensities of the appearance of points. The equations of behavior of the mathematical expectation of the processed information are reduced to a canonical form, which allows them to be applied to a variety of practical tasks, for example, to the description of hierarchical cognitive structures when the output of one level is the input of another.
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认知动态系统状态空间噪声抗扰度计算算法
研究方法是应用在自动动力系统研究中广泛使用的状态空间方法来描述认知系统的行为。假设,在认知系统的输入处,存在泊松点过程描述的信号和干扰,模拟了与每个事件相对应的信息量、情绪压力量等。本文中系统的认知特性考虑了两种情况。首先,本文不仅用事件发生时间的泊松分布来描述局域事件,而且用一些随机变量来描述事件对系统的重要性。一个典型的例子是,如果对信息处理系统进行建模,则将一定数量的信息归属于每个事件。另一个例子是一个人对压力的情绪反应,在一本经典的心理学著作中有描述。在这种情况下,重点是引起应力的事件,而应力对系统的影响是根据Holmes和Rahe量表的相对应力大小来建模的。其次,认知系统以其固有的速度处理、同化和适应每个事件对它的影响。本文将这一现象建模为一个点过程通过一个用微分方程描述的动态系统。这样的过程称为过滤点过程。文中给出了影响的例子,为简单起见,假设影响的大小为事件发生时系统接收到的信息量。因此,认知系统的模型是一个用状态空间中的微分方程描述的动态系统,在该系统的输入处,具有一定信息负荷的消息在随机的离散时刻出现。对于任何技术系统,认知系统都面临着评估其工作质量的任务。在这方面,本文从工程的角度论证了使用一种方便的质量指标和一种适当的信号干扰比形式的判据。新的结果是状态空间中的微分方程,用于信号和干扰的数学期望,以及计算认知系统的噪声免疫的算法。作为一个例子,计算并给出了一个特定认知系统的噪声抗扰度图,从而证实了其行为的直观概念。总之,值得注意的是,本文的主要成果是一种使用微分方程计算认知系统的噪声抗扰度的算法,该算法允许计算非平稳认知系统在由点的外观强度的非平稳函数描述的任何点影响下的行为。处理信息的数学期望的行为方程被简化为规范形式,这使得它们可以应用于各种实际任务,例如,当一个层次的输出是另一个层次的输入时,用于描述层次认知结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Open Education Studies
Open Education Studies Social Sciences-Social Sciences (miscellaneous)
CiteScore
1.80
自引率
0.00%
发文量
19
审稿时长
27 weeks
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