# Help in implementing self organizing map for quantizing time series based on a paper

Chapter titled, Self Organized Partitioning of Chaotic Attractors for Control in Lecture Notes in Computer Science in book: Artificial Neural Networks — ICANN 2001, pp.851-856

uses multiple self organizing map to quantize time series. The Authors then use the codebook to assign symbols. A codebook of k prototypes, best representing the data, is first designed. I am having difficulties in implementing the self organizing map as I do not understand how the quantization is being performed -- specifically what is the input to the SOM and how the output is used to assign symbols.

For my case, the data consists of N sensor variables (electrodes) : X = {x_i(t)} for i =1:N and t = 1:T number of time series. Then a new time series is compared / matched using the symbols representation. COuld somebody please explain illustrating with only a single SOM map and then I can apply it to multiple SOM maps.

An explanation with a code would be really helpful. My understanding is that the input to SOM would be X and after training using LBG the codebook is assigned. The codebook for SOM is the weight. Any toy example would be very useful to understand the concept. Thank you.