Based on the Hopfield neural network model of combinatorial circuit test generation, this paper discusses and analyzes the effective algorithm for test generation using the global search capability of chaotic neural networks and the adaptive test generation based on genetic algorithms. The algorithm based on chaotic neural networks uses the ergodicity and internal randomness of chaos to perform global search; while the genetic algorithm is different from the traditional method, it does not require fault propagation, fallback and other processes, and has the ability of parallel computing. Computer simulation results show the feasibility and efficiency of these two test generation algorithms. Keywords: neural network, chaotic search, genetic algorithm, test generation
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