An investigation of the precision impact on the Hopfield-Tank neural network model for the TSP

An advantage of the use of neural networks is the utilization of a number of processing units to obtain the solution in short time. In the design of a neural network, different bit precisions alter the computer architecture and organization on design. The authors study the impact of the precision by using the Hopfield-Tank neural network model for the traveling salesman problem (TSP). In order to simulate the TSP problem using the Hopfield-Tank model, the authors have used a number of previous studies to determine some of the required parameters. To investigate the influence of the precision, the authors have simulated the TSP problem in a MIPS R3000. The authors have considered: five different bit precisions (8- 16- 24- 32- and double precision mantissas), three values of the sigmoid generation parameters, and convergency within 1000 neuron update cycles. The authors have run a total of 7,080 simulations for the established benchmark in the MIPS-3000 machines; the simulation results are extensively discussed. Additionally, two novel approaches to measure the performance of the network, namely the average network performance and computational efficiency, are introduced and used in the evaluation of the performance of the model. Further information extraction is done by using Dempster's rule of combination for the average network performance and computational efficiency.<
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