On the structure of multi-layer cellular neural networks (original) (raw)

2012, Journal of Differential Equations

Let Y ⊆ {−1, 1} Z∞×n be the mosaic solution space of an n-layer cellular neural network. We decouple Y into n subspaces, say Y (n) , and give a necessary and sufficient condition for the existence of factor maps between them. In such a case, Y (i) is a sofic shift for 1 i n. This investigation is equivalent to study the existence of factor maps between two sofic shifts. Moreover, we investigate whether Y (i) and Y ( j) are topological conjugate, strongly shift equivalent, shift equivalent, or finitely equivalent via the well-developed theory in symbolic dynamical systems. This clarifies, in a multi-layer cellular neural network, each layer's structure. As an extension, we can decouple Y into arbitrary k-subspaces, where 2 k n, and demonstrates each subspace's structure.

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On dynamical properties of neural networks

Complex Systems, 1991

Abstract. A transform is introduced that maps cellular automata and discrete neural networks to dynamical systems on the unit in-terval. This transform is a topological conjugacy except at countably many points. In many cases, it gives rise to continuous full conjugates, in which ...

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Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications

The stability and dynamics of a class of Cellular Neural Networks (CNN's) in the central linear pan is investigated using the decoupling lechnique based on discrete spatial transforms, Nyquist and root locus techniques.

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Journal of Differential Equations, 2009

This study investigates the complexity of the global set of output patterns for one-dimensional multi-layer cellular neural networks with input. Applying labeling to the output space produces a sofic shift space. Two invariants, namely spatial entropy and dynamical zeta function, can be exactly computed by studying the induced sofic shift space. This study gives sofic shift a realization through a realistic model. Furthermore, a new phenomenon, the broken of symmetry of entropy, is discovered in multi-layer cellular neural networks with input.

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Information and Computation, 2009

We consider continuous, translation-commuting transformations of compact, translation-invariant families of mappings from finitely generated groups into finite alphabets. It is well-known that such transformations and spaces can be described "locally" via families of patterns and finitary functions; such descriptions can be re-used on groups larger than the original, usually defining non-isomorphic structures. We show how some of the properties of the "induced" entities can be deduced from those of the original ones, and vice versa; then, we show how to "simulate" the smaller structure into the larger one, and obtain a characterization in terms of group actions for the dynamical systems admitting of presentations via structures as such. Special attention is given to the class of sofic shifts.

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Complex Systems, 1990

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Complex dynamic phenomena in space-invariant cellular neural networks

IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 2002

It is shown that first-order autonomous space-invariant cellular neural networks (CNNs) may exhibit a complex dynamic behavior (i.e., equilibrium point and limit cycle bifurcation, strange and chaotic attractors). The most significant limit cycle bifurcation processes, leading to chaos, are investigated through the computation of the corresponding Floquet's multipliers and Lyapunov exponents. It is worth noting that most practical CNN implementations exploit first-order cells and space-invariant templates: so far no example of complex dynamics has been shown in first-order autonomous space-invariant CNNs.

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Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290), 2002

Cellular neural networks (CNNs) are analog dynamic processors that have found several applications for the solution of complex computational problems. The mathematical model of a CNN consists in a large set of coupled nonlinear differential equations that have been mainly studied through numerical simulations; the knowledge of the dynamic behavior is essential for developing rigorous design methods and for establishing new applications. CNNs can be divided in two classes: stable CNNs, with the property that each trajectory (with the exception of a set of measure zero) converges towards an equilibrium point; unstable CNNs with either a periodic or a non/periodic (possibly complex) behavior. The manuscript is devoted to the comparison of the dynamic behavior of two CNN models: the original Chua-Yang model and the Full Range model, that was exploited for VLSI implementations.

Dominating Topological Analysis and Comparison of the Cellular Neural Network

2021

Graph theory is a discrete branch of mathematics for designing and predicting a network. Some topological invariants are mathematical tools for the analysis of connection properties of a particular network. The Cellular Neural Network (CNN) is a computer paradigm in the field of machine learning and computer science. In this article we have given a close expression to dominating invariants computed by the dominating degree for a cellular neural network. Moreover, we have also presented a 3D comparison between dominating invariants and classical degree-based indices to show that, in some cases, dominating invariants give a better correlation on the cellular neural network as compared to classical indices.

Complex dynamics in cellular neural networks

ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196), 2001

The occurrence of complex dynamic behavior (i.e bifurcation processes, strange and chaotic attractors) in autonomous space-invariant cellular neural networks (CNNs) is investigated. Firstly some sufficient conditions for the instability of CNNs are provided; then some classes of unstable template are identified. Finally it is shown that unstable CNNs often exhibit complex dynamics and for a case study the most significant bifurcation processes are described. It is worth noting that most CNN implementations exploit spaceinvariant templates and so far no example of complex dynamics has been shown in autonomous space-invariant CNNs.

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