Abstract: This paper presents a new recursive neural network to solve optimization problems. It is made of binary neutrons with feedbacks. From a random initial state, the dynamics alternate successively several pulsation and constraint satisfaction phases. Applying the previous neural network has the following advantages: (1) There is no need to precisely adjust some parameters in the motion equations to obtain good feasible solutions. (2) During each constraint satisfaction phase, the network converges to a feasible solution. (3) The convergence time in the constraint satisfaction phases is very fast: only a few updates of each neuron are necessary. (4) The end user can limit the global response time of the network which regularly provides feasible solutions. This paper describes such a neural network to solve a complex real time resource allocation problem and compare the performances to a simulated annealing algorithm. !8
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