1997 | OriginalPaper | Buchkapitel
Local Analysis of Weakly Connected Maps
verfasst von : Frank C. Hoppensteadt, Eugene M. Izhikevich
Erschienen in: Weakly Connected Neural Networks
Verlag: Springer New York
Enthalten in: Professional Book Archive
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
In previous chapters we studied dynamics of weakly connected networks governed by a system of ordinary differential equations. It is also feasible to consider weakly connected networks of difference equations, or mappings, of the form 1$$ X_i \mapsto F_i \left( {X_i ,\lambda } \right) + \varepsilon G_i \left( {X_i ,\lambda ,\rho ,\varepsilon } \right),{\text{ i = 1,}} \ldots {\text{,n, }}\varepsilon \ll {\text{1,}} $$ where the variables X i ∈ ℝm, the parameters λ ∈ Λ,ρ ∈ R and the functions F i and G i have the same meaning as in previous chapters. The weakly connected mapping (7.1) can be also written in the form $$ X_i^{k + 1} = F_i \left( {X_i^k ,\lambda } \right) + \varepsilon G_i \left( {X^k ,\lambda ,\rho ,\varepsilon } \right),{\text{ i = 1,}} \ldots {\text{,n, }}\varepsilon \ll {\text{1,}} $$ where X k is the kth iteration of the variable X. In this chapter we use form (7.1) unless we explicitly specify otherwise.