title: Cellular computational networks--a scalable architecture for learning the dynamics of large networked systems.
abstract: Neural networks for implementing large networked systems such as smart electric power grids consist of multiple inputs and outputs. Many outputs lead to a greater number of parameters to be adapted. Each additional variable increases the dimensionality of the problem and hence learning becomes a challenge. Cellular computational networks (CCNs) are a class of sparsely connected dynamic recurrent networks (DRNs). By proper selection of a set of input elements for each output variable in a given application, a DRN can be modified into a CCN which significantly reduces the complexity of the neural network and allows use of simple training methods for independent learning in each cell thus making it scalable. This article demonstrates this concept of developing a CCN using dimensionality reduction in a DRN for scalability and better performance. The concept has been analytically explained and empirically verified through application.
authors.affiliation: Real-Time Power and Intelligent Systems Laboratory, Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29634, USA. Electronic address: iambipul@ieee.org.
authors.forename: Bipul
authors.initials: B
authors.lastname: Luitel
authors.affiliation: Real-Time Power and Intelligent Systems Laboratory, Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29634, USA. Electronic address: ganeshv@mst.edu.
authors.forename: Ganesh Kumar
authors.initials: GK
authors.lastname: Venayagamoorthy
mesh_terms: D000465:Algorithms; D000818:Animals; D003199:Computer Systems; D006801:Humans; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics
keywords: CCN; DRN; Electric power network; Large networked systems; Scalability