Abstract
This study examines the value of utilizing neural net modeling for issues relating to optimization across a network of cities in space. Neural nets are made up of many nonlinear computational elements that operate in parallel and are arranged in a manner similar to biological neural nets. Defining a neural net model involves specifying a net topology, arrangement of nodes, training or learning rules, adjustment of weights associated with connections, node characteristics, and rules of transformation from input to output. All of these are the major issues in such regional problems as labor force migration and firm location.
Highlights
The purpose of this paper is to shed light on the value of utilizing existing neural network modeling techniques for issues relating to optimization and classification across a network of city-centric economic markets in space
Neural nets are made up of many nonlinear computational elements that operate in parallel and are arranged in a manner similar to biological neural nets
Defining a neural net model involves specifying a net topology, such as an interstate highway system; arrangement of nodes, such as the geographic arrangement of cities; training or learning rules, such as microeconomic decision theory or macroeconomic policy making; adjustment of weights associated with connections, such as location decision criteria; node characteristics; and rules of transformation from input to output
Summary
The purpose of this paper is to shed light on the value of utilizing existing neural network modeling techniques for issues relating to optimization and classification across a network of city-centric economic markets in space This could be the optimization of costs by a firm in a location decision or of income by a worker. Defining a neural net model involves specifying a net topology, such as an interstate highway system; arrangement of nodes, such as the geographic arrangement of cities; training or learning rules, such as microeconomic decision theory or macroeconomic policy making; adjustment of weights associated with connections, such as location decision criteria; node characteristics; and rules of transformation from input to output All of these are major issues in fundamental regional science problems, such as labor force migration, firm location, and structural unemployment. Supervised Learning: (1) Multilayer Perceptron, (2) Hopfield Model, (3) Hamming Net, and (4) Boltzman Machines
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