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An idea of gate model networks as a logic minimization method for multiple-valued logic functions is proposed. The gate model network is introduced as a kind of neural networks and is constructed like AND-OR two-level circuits by using two gate models; one is AND type gate model and the other is OR type gate model. By applying the back-propagation (BP) method to the networks, we train the network until the network realizes the minimal solution. Then, a solution is derived from the weights and thresholds. First, the gate model networks are applied to binary AND-OR-circuits minimization. As the result, the solutions are almost always minimal for 8-or-less variable binary functions. Second, this method is applied to the multiple-valued max-of-min's expression minimization. Finally, it is shown that the gate model network is also applicable to minimize multiple-valued sum-of-products expressions where sum refers to TSUM.

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