Mapping the team decision theory problem to Hopfield-like neural networks
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Team Decision Theory is a statistical discipline that has several applicationin areas such as decentralized control and distributed computing. In the middle to late 1970 s. this area was studied quite extensively. However, there were several limitations to the scope of the study due to the inherent mathematical intractability of the problem. There were severe restrictions on the nature of the system inputs and their probability distribution functions. Unless the underlying probability density functions of the system parameters were Gaussian, it was not possible to derive analytical solutions to the problems. In recent years, neural networks have become increasingly popular as a means to solve large optimization problems. The high interconnectivity and the nature of neuron layouts and interactions have led to success in mapping large optimization problems to neural networks. In particular the Hopfield-Tank Network and some derivations thereof have been successful for these problems. Neural networks are not sensitive to the underlying probability distributions of the systems they are trying to solve. With the advent of cheaper hardware and faster networks, distributed processing in a networked system has become increasingly popular. One of the key areas of study in distributed computing is the load balancing discipline: determining an optimal balancing of tasks or jobs among the various nodes in the system to maximize system performance and throughput. Several schemes have been studied with varying degrees of success.