✍ Scribed by Khalid Md. Nor; Camile Nur; Azur Shah; Leha Alam; Kinah Semesta
No coin nor oath required. For personal study only.
We suggest a description of thermodynamical systems, focusing on near-to-equilibrium states of the systems, within the structure of random graphs to map them to neural nets. We then use the component subgraph configurations of the random graphs that are the stationary states of the near-to-equilibrium systems to represent concepts in an unstructured form. This enables the evolution property of random graphs to give temporal sequences of concepts. Such sequences can be interconnected to form an expert system for processing unstructured data.
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