An approach to solving the longest common subsequences based on string-coding functional and neural network

Presented is an approach, based on a string-coding functional and neural network, to solving the longest common subsequences (LCS) problem with a high degree of parallelism. In this approach, the parameters related to the input strings are contained entirely in the linear term of the neural network energy function, and the quadratic term only has to do with constraints. It is not necessary to modify the internal parameters and the connection weight matrix with new input strings. The complexities of both the network computing and the hardware implementation are substantially reduced.<
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