Improved pattern recognition with complex artificial immune system

In this paper, we introduce the application of transformation pattern recognition based on a complex artificial immune system. The key feature of the complex artificial immune system is the introduction of complex data representation. We use complex numbers as the data representation instead of binary numbers used before, besides the weight between different layers. The complex partial autocorrelation coefficients of input antigen which are considered as the antigen presentation are calculated in major histocompatibility complex (MHC) layer of the complex artificial immune system. In the simulations, the transformation of patterns, such as translation, scale or rotation, are recognized in much higher accuracy, and it has obviously higher noise tolerance ability than traditional real artificial immune system and even the complex PARCOR model.