SUMMARY In this paper, we propose a stochastic dynamic local search(SDLS) method for Multiple-Valued Logic (MVL)learning by introducing stochastic dynamics into the traditional local search method. The proposed learning network maintains some trends of quick descent to either global minimum or a local minimum, and at the same time has some chance of escaping from local minima by permitting temporary error increases during learning. Thus the network may eventually reach the global minimum state or its best approximation with very high probability. Simulation results show that the proposed algorithm has the superior abilities to find the global minimum for the MVL network learning within reasonable number of iterations.
This paper describes a learning technique and implementation for the multiple-valued logic networks. The learning problem of the multiple-valued logic networks is formulated as a minimization of an error function that is defined to represent a distortion measure between actual output and desired output. We adopt gradient-based least-square error minimization algorithm to minimize the error function, which in contrast to backpropagation algorithm, does not involve a sigmoid function and requires only a simple sgn function in the learning rule. The algorithm trains the networks using examples and appears to be available in practice for most multiple-valued problems of interest. The paper also describes circuit implementations of the learning multiple-valued logic networks using CMOS current-mode circuits.
In this paper, a chaotic clonal selection algorithm (CCSA) is proposed to synthesize multiple-valued logic (MVL) functions. The MVL function is realized in a multiple-valued sum-of-products expression where product is indicated by MIN and sum by TSUM. The proposed CCSA, in which chaos is incorporated into the clonal selection algorithm to initialize antibodies and maintain the population diversity, is utilized to learn a given target MVL truth table. Furthermore, an adaptive length strategy of antibodies is also introduced to reduce the computational complexity, whereas an improved affinity function enables the algorithm to find less product terms for an MVL function. Simulation results based on a large number of MVL functions demonstrate the efficiency of the proposed method when compared with other traditional methodologies.
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