Modeling the emergence of informational content by adaptive networks for temporal factorisation and criterial causation

Treur, Jan

doi:10.1016/j.cogsys.2020.10.018

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…Some more philosophically focused background for mental models and their modeling can be found in Treur (2021d) about neural correlates for mental models and (Treur 2021e) about the emerging informational content of mental models; see also Van Ments (2022, Chap. 15), andVan Ments (2022, Chap 16), respectively.…”

Section: Discussionmentioning

confidence: 99%

Dynamics, Adaptation and Control for Mental Models: A Cognitive Architecture

Ments¹,

Treur

2022

Studies in Systems, Decision and Control

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Section: Discussionmentioning

confidence: 99%

Dynamics, Adaptation and Control for Mental Models: A Cognitive Architecture

Ments¹,

Treur

2022

Studies in Systems, Decision and Control

Self Cite

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“…It is mainly based on material from (Treur 2021). The core of the idea is that according to some adaptive process, (past) brain patterns or world patterns occur, which as patterns lead to emerging brain configurations or world configurations in the present; these configurations in turn drive or affect the (future) brain pattern or world pattern.…”

Section: Discussionmentioning

confidence: 99%

How the Brain Creates Emergent Information by the Development of Mental Models: An Analysis from the Perspective of Temporal Factorisation and Criterial Causation

Treur

2022

Studies in Systems, Decision and Control

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“…Others use the fuzzy algorithm to solve the performance of the neural network controller based on the neuron adaptive strategy, which is 0.02 times better than the traditional robust control method. Others have established mathematical models to achieve the design of neuron dynamic configuration and on-line optimization based on the synergistic algorithm of neural network, and verified the algorithm performance [7][8] with practical applications. Therefore, this paper studies and controls the coordination prediction of distributed systems based on the network information mode.…”

Section: Introductionmentioning

confidence: 99%

Distributed System Coordination Predictive Control for Network Information Mode

2022

DPS

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With the rapid development of network information technology, distributed system coordinated control has become an indispensable part of the network information industry in the world today. In this paper, the concepts and classifications of the distributed system coordinated control method for network information mode are introduced first, then the problems existing in the traditional network structure are analyzed in detail. Then, from the point of view of genetic algorithm theory, a support vector machine (SVM), fuzzy logic and transfer function are established to solve the actual non-linear load dispatch, in which the data is processed by neural calculation. Finally, the improved decision table achieves the same control effect as the original model through experimental validation, and improves the system coordination performance index and stability. The experimental results show that the prediction time of this model is within the range of 4s-7s, and its stability, feasibility and prediction accuracy are up to 90%. This indicates that the distributed system coordination prediction model under network information mode performs well.

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Modeling the emergence of informational content by adaptive networks for temporal factorisation and criterial causation

Cited by 9 publications

References 17 publications

Dynamics, Adaptation and Control for Mental Models: A Cognitive Architecture

Dynamics, Adaptation and Control for Mental Models: A Cognitive Architecture

How the Brain Creates Emergent Information by the Development of Mental Models: An Analysis from the Perspective of Temporal Factorisation and Criterial Causation

Distributed System Coordination Predictive Control for Network Information Mode

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