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This paper presents the comparison of various neural networks and algorithmsbased on accuracy, quickness, and consistency for antenna modelling. UsingMATLAB Nntool, 22 different combinations of networks and training algorithmsare used to predict the dimensions of a rectangular microstrip antenna usingdielectric constant, height of substrate, and frequency of oper-ation as input.Comparison and characterization of networks is done based on accuracy, meansquare error, and training time. Algorithms, on the other hand, are analyzed bytheir accuracy, speed, reliability, and smoothness in the training process.Finally, these results are analyzed, and recommendations are made for eachneural network and algorithm based on uses, advantages, and disadvantages. Forexample, it is observed that Reduced Radial Bias network is the most accuratenetwork and Scaled Conjugate Gradient is the most reliable algorithm forelectromagnetic modelling. This paper will help a researcher find the optimumnetwork and algorithm directly without doing time-taking experimentation.

Comparison of Neural Network based Soft Computing Techniques for Electromagnetic Modeling of a Microstrip Patch Antenna