1 research outputs found
Layered Cellular Automata
Layered Cellular Automata (LCA) extends the concept of traditional cellular
automata (CA) to model complex systems and phenomena. In LCA, each cell's next
state is determined by the interaction of two layers of computation, allowing
for more dynamic and realistic simulations. This thesis explores the design,
dynamics, and applications of LCA, with a focus on its potential in pattern
recognition and classification. The research begins by introducing the
limitations of traditional CA in capturing the complexity of real-world
systems. It then presents the concept of LCA, where layer 0 corresponds to a
predefined model, and layer 1 represents the proposed model with additional
influence. The interlayer rules, denoted as f and g, enable interactions not
only from adjacent neighboring cells but also from some far-away neighboring
cells, capturing long-range dependencies. The thesis explores various LCA
models, including those based on averaging, maximization, minimization, and
modified ECA neighborhoods. Additionally, the implementation of LCA on the 2-D
cellular automaton Game of Life is discussed, showcasing intriguing patterns
and behaviors. Through extensive experiments, the dynamics of different LCA
models are analyzed, revealing their sensitivity to rule changes and block size
variations. Convergent LCAs, which converge to fixed points from any initial
configuration, are identified and used to design a two-class pattern
classifier. Comparative evaluations demonstrate the competitive performance of
the LCA-based classifier against existing algorithms. Theoretical analysis of
LCA properties contributes to a deeper understanding of its computational
capabilities and behaviors. The research also suggests potential future
directions, such as exploring advanced LCA models, higher-dimensional
simulations, and hybrid approaches integrating LCA with other computational
models.Comment: This thesis represents the culmination of my M.Tech research,
conducted under the guidance of Dr. Sukanta Das, Associate Professor at the
Department of Information Technology, Indian Institute of Engineering Science
and Technology, Shibpur, West Bengal, India. arXiv admin note: substantial
text overlap with arXiv:2210.13971 by other author