The aim of this book is to present and evaluate a novel way of optimizing nonlinear RNP problems utilizing artificial intelligence techniques. The research has been
developed by utilizing Artificial Neural Network models (ANN), computational artificial intelligence algorithms and mathematical modeles of RFID network planning
(RNP) to develop an efficient artificial intelligence paradigm to optimize nonlinear
RNP problems. Starting from introducing existing ANN models, it defines which
structure is required in order to optimize functions. Different artificial intelligence
algorithms, which can satisfy the required capabilities for optimizing of defined
RFID network planning problem that can be represented as mathematical models,
are presented and discussed. This effort has led to proposing a novel artificial intelligence algorithm which has been named hybrid artificial intelligence optimization
technique to perform the optimization of RNP as a hard learning problem. The proposed hybrid optimization technique has been made of two different optimization
phases. The first phase is optimizing RNP by Redundant Antenna Elimination (RAE)
algorithm and the second phase which completes the RNP optimization process is
Ring Probabilistic Logic Neural Networks (RPLNN).