Neural Networks and Dynamic Complex Systems

We describe the use of neural networks for optimization and inference associated with a variety of complex systems. We show how a string formalism can be used for parallel computer decomposition, message routing and sequential optimizing compilers. We extend these ideas to a general treatment of spatial assessment and distributed artificial intelligence

Neural Networks and Dynamic Complex Systems

We describe the use of neural networks for optimization and inference associated with a variety of complex systems. We show how a string formalism can be used for parallel computer decomposition, message routing and sequential optimizing compilers. We extend these ideas to a general treatment of spatial assessment and distributed artificial intelligence

Learning to Plan Near-Optimal Collision-Free Paths

A new approach to find a near-optimal collision-free path is presented. The path planner is an implementation of the adaptive error back-propagation algorithm which learns to plan “good”, if not optimal, collision-free paths from human-supervised training samples. Path planning is formulated as a classification problem in which class labels are uniquely mapped onto the set of maneuverable actions of a robot or vehicle. A multi-scale representational scheme maps physical problem domains onto an arbitrarily chosen fixed size input layer of an error back-propagation network. The mapping does not only reduce the size of the computation domain, but also ensures applicability of a trained network over a wide range of problem sizes. Parallel implementation of the neural network path planner on hypercubes or Transputers based on Parasoft EXPRESS is simple and efficient, Simulation results of binary terrain navigation indicate that the planner performs effectively in unknown environment in the test cases

Parallel Computers and Complex Systems

We present an overview of the state of the art and future trends in high performance parallel and distributed computing, and discuss techniques for using such computers in the simulation of complex problems in computational science. The use of high performance parallel computers can help improve our understanding of complex systems, and the converse is also true --- we can apply techniques used for the study of complex systems to improve our understanding of parallel computing. We consider parallel computing as the mapping of one complex system --- typically a model of the world --- into another complex system --- the parallel computer. We study static, dynamic, spatial and temporal properties of both the complex systems and the map between them. The result is a better understanding of which computer architectures are good for which problems, and of software structure, automatic partitioning of data, and the performance of parallel machines

Network Optimization of Dynamically Complex Systems

The aim of this research is to optimize large scale network handling capabilities for large system inventories and to implement strategies for the purpose of reducing capital expenses. As computers become more and more networked, it is easier to share files among storage media. In addition, more bandwidth will be consumed by network flow because customers will be connected through networks which will transfer files and data, such as video files (MPEGn, AVI, WMV, etc.) to be watched at a customer\u27s computer (host). Furthermore, these networks terminals will be used as mini warehouses to save files and data. Selective files will be transferred to the host computer depending on customers pre-requested profile and prioritization. The research will present techniques that optimize transfer storage media for the purpose of minimizing waiting time and hardware cost while maximizing efficiency and customer satisfaction