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Full-Text Articles in Physical Sciences and Mathematics
A Vlsi Implementation Of A Parallel, Self-Organizing Learning Model, Tony R. Martinez, George L. Rudolph, Linton G. Salmon, Matthew G. Stout
A Vlsi Implementation Of A Parallel, Self-Organizing Learning Model, Tony R. Martinez, George L. Rudolph, Linton G. Salmon, Matthew G. Stout
Faculty Publications
This paper presents a VLSI implementation of the Priority Adaptive Self-organizing Concurrent System (PASOCS) learning model that is built using a multi-chip module (MCM) substrate. Many current hardware implementations of neural network learning models are direct implementations of classical neural network structures - a large number of sample computing nodes connected by a dense number of weighted links. PASOCS is one of a class of ASOCS (Adaptive Self-Organizing Concurrent System) connectionist models whose overall goal is the same as classical neural networks models, but whose functional mechanisms differ significantly. This model has potential application in areas such as pattern recognition, …
Towards A General Distributed Platform For Learning And Generalization, Brent W. Hughes, Tony R. Martinez
Towards A General Distributed Platform For Learning And Generalization, Brent W. Hughes, Tony R. Martinez
Faculty Publications
Different learning models employ different styles of generalization on novel inputs. This paper proposes the need for multiple styles of generalization to support a broad application base. The Priority ASOCS model (Priority Adaptive Self-organizing Concurrent System) is overviewed and presented as a potential platform which can support multiple generalization styles. PASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. The PASOCS can operate in either a data processing mode or a learning mode. During data processing mode, the system acts as a parallel hardware circuit. During leaming mode, the PASOCS incorporates rules, with …
A Self-Organizing Binary Decision Tree For Incrementally Defined Rule-Based Systems, Douglas M. Campbell, Tony R. Martinez
A Self-Organizing Binary Decision Tree For Incrementally Defined Rule-Based Systems, Douglas M. Campbell, Tony R. Martinez
Faculty Publications
This paper presents an adaptive self-organizing concurrent system (ASOCS) model for massively parallel processing of incrementally defined rule systems in such areas as adaptive logic, robotics, logical inference, and dynamic control. An ASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. This paper focuses on adaptive algorithm 3 (AA3) and details its architecture and learning algorithm. It has advantages over previous ASOCS models in simplicity, implementability, and cost. An ASOCS can operate in either a data processing mode or a learning mode. During the data processing mode, an ASOCS acts as a parallel …