A local-minima-free neural network approach to building A/D converters and associative adders

Summary form only given, as follows. The theory of neural networks is extended to include discrete neurons called quantrons (quantum neurons, or Q´trons). Q´trons are featured in their multiple output levels whose number is usually greater than two. The dynamics of the Q´tron neural network (NN) are studied and the property of the embedded system energy, referred to as Lyapunov energy, is derived. If a problem can be reformulated as one which minimizes the Lyapunov energy, then it can be solved by a suitably constructed Q´tron NN. Two typical examples, an A/D converter and an associative adder, are realized by using such Q´tron NNs. In order to make this NN approach complete, i.e., so that it never provides false solutions, a mechanism which prevents the NN from being stuck at some local minimum of the Lyapunov energy is incorporated into each Q´tron. To demonstrate the effectiveness of the proposed NN, computer simulations have been performed