Using an SN P System to Compute the Product of Any Two Decimal Natural Numbers

In this paper, a new SN P system is investigated in order to compute the product of any two decimal natural numbers. Firstly, an SN P system with two input neurons is constructed, which can be used to compute the product of any two binary natural numbers which have specified lengths. Secondly, the correctness of the SN P system is proved theoretically. However, the system can only be used to compute the product of any two binary natural numbers, but the product of any two decimal natural numbers often need to be computed in practical application. Therefore, it is necessary to construct a coding SN P system which converts a decimal number into a binary number and to construct a decoding SN P system which converts a binary number to a decimal number. In the end, an new SN P system is constructed to compute the product of any two decimal natural numbers. An example test shows that the SN P system can be used to compute the product of any two decimal natural numbers. Therefore, this paper provides a new method for constructing the SN P system which can compute the product of any two natural numbers.

[1]  Sergey Verlan,et al.  Fast Hardware Implementations of P Systems , 2012, Int. Conf. on Membrane Computing.

[2]  Linqiang Pan,et al.  Cell-like spiking neural P systems , 2016, Theor. Comput. Sci..

[3]  Miguel A. Gutiérrez-Naranjo,et al.  First Steps Towards a CPU Made of Spiking Neural P Systems , 2009, Int. J. Comput. Commun. Control.

[4]  Xiangxiang Zeng,et al.  Spiking Neural P Systems with Thresholds , 2014, Neural Computation.

[5]  Linqiang Pan,et al.  Asynchronous Extended Spiking Neural P Systems with Astrocytes , 2011, Int. Conf. on Membrane Computing.

[6]  Linqiang Pan,et al.  Tissue P Systems with Protein on Cells , 2016, Fundam. Informaticae.

[7]  Pan Linqiang,et al.  Spiking neural P systems with neuron division and budding , 2011 .

[8]  Ferrante Neri,et al.  An Optimization Spiking Neural P System for Approximately Solving Combinatorial Optimization Problems , 2014, Int. J. Neural Syst..

[9]  Mario J. Pérez-Jiménez,et al.  Computational efficiency and universality of timed P systems with membrane creation , 2015, Soft Comput..

[10]  Linqiang Pan,et al.  Computational efficiency and universality of timed P systems with active membranes , 2015, Theor. Comput. Sci..

[11]  Kamala Krithivasan,et al.  Control Words of Transition P Systems , 2012, BIC-TA.

[12]  Xiangxiang Zeng,et al.  Time-Free Spiking Neural P Systems , 2011, Neural Computation.

[13]  Linqiang Pan,et al.  Asynchronous spiking neural P systems with local synchronization , 2013, Inf. Sci..

[14]  Zhang Xing A Spiking Neural P System for Performing Multiplication of Two Arbitrary Natural Numbers , 2009 .