A Comparison of Rk-fourth Orders of Variety of Means and Embedded Means on Multilayer Raster Cnn Simulation
نویسندگان
چکیده
In this paper an adaptable algorithm for simulating CNN arrays is implemented using various RK-fourth order means such as Arithmetic Mean [AM], Centroidal Mean [CM], Harmonic Mean [HM], Contra Harmonic Mean [CoM], Heronian Mean [HeM], Geometric Mean [GM] and Root Mean Square [RMS] also, it is compared with RK-fourth order embedded means such as the RK-Embedded Heronian Mean, RK-Embedded Centroidal Mean, Harmonic Mean and Contra-Harmonic Mean. The role of the simulator is that it is capable of performing Raster Simulation for any kind as well as any size of input image. It is a powerful tool for researchers to investigate the potential applications of CNN. This article proposes an efficient pseudo code exploiting the latency properties of Cellular Neural Networks along with well known RK-Fourth Order numerical integration algorithms. Simulation results and comparison have also been presented to show the efficiency of the various means in Numerical integration Algorithms. It is observed that the RK-fourth order embedded means outperforms well in comparison with RK-fourth order means. In particular it is found that the RK-Embedded Heronian Mean outperforms well in comparison with the RK-Embedded Centroidal Mean, Harmonic Mean and Contra-Harmonic Mean.
منابع مشابه
Investigation on Multilayer Raster Cellular Neural Network by Arithmetic and Heronian Mean RKAHeM(4, 4)
We introduce a new technique for solving Initial Value Problems (IVPs) by formulating an embedded method involving RK methods based on Arithmetic Mean(AM) and Heronian Mean (HeM). The function of the simulator is that it is capable of performing Raster Simulation for any kind as well as any size of input image. It is a powerful tool for researchers to examine the potential applications of CNN. ...
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