An Improved Hopfield Neural Network Algorithm for Computational Codeword Design
An Improved Hopfield Neural Network Algorithm for Computational Codeword Design
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一种改进的Hopfield神经网络算法用于计算码字设计
DOI：
10.1166/jctn.2007.2407
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发表时间：
2007-11
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设计计算码字对于 DNA 计算至关重要。然而，这是一项麻烦的任务，因为在定义编码问题方面需要满足太多约束。本文证明了在随机生成的 DNA 序列集中找到最大计算码字数的问题不仅是 NP 困难的，而且还可以映射到最大团问题图的解上。这样，利用元启发式算法寻找最优或接近最优解，并预定随机生成集合中的计算码字是否需要用于后续的可控计算。在这里，我们提出了一种改进的 Hopfield 神经网络算法来解决这个问题。仿真结果表明，所提出的方法有助于用户选择一组合适的候选DNA序列来过滤并获得良好的计算码字。
Designing computational codeword is crucial in DNA computing. However, this is a bothersome task as too many constraints need to be satisfied in terms of definiting of encoding problem. This paper proves that the problem of finding the maximum number of computational codeword in a randomly generated set of DNA sequences is not only NP-hard, but it can also be mapped onto the solution of a graph of maximum clique problem. Thus, utilizing meta-heuristic algorithm to find an optimal or near optimal solution and predestinating whether or not the computational codeword in randomly generated set are required for the following controllable computation. Here we present an improved Hopfield neural network algorithm to solve this problem. The simulation results show that the proposed method is useful for a user to select an appropriate set of candidate DNA sequences to filter and obtain good computational codeword.