variables are set up by comparing the resistance variable over 594 cycles. The frequency and the degree of change sets up the other 4 variables. These variables are used to produce words, sentences and codes that trigger a response from the Ai

Resistance and resistance variance
the resistance value changes slowly so this value is used to associate to whole sentences, if the plant is moist i.e. just been watered this value is high so the sentence associated is 'I'm feeling damp'. The resistance variance produces two letters the first is the number of times the signal has raised over time the second is the number of times the resistance signal has dropped over time, I use the two letters for yes and no responses if it produces say 'cc' then not sure either way if its 'cf' then strong no if 'db' then strong yes etc..

Frequency and frequency variance over time
One of the variables produces letters, another the number of letters in the word then another is used to determine the length of the sentence. This sentence is fed into the main processing part of the program which then learns the words/puts the words into the data base, then I choose words from my head to associate to the created words. After the learning process it does the conversion process automatically. When I train the Ai I set it on learning mode and choose words that fit the mood, the other night was full moon so I did some training and used moon like words.

The WSB algorithm draws a graph showing in real time the breakdown of the signal

The WSB algorithm is assigned a reply quality (RQ) weight like all the other processing algorithms in the program.
Because the WSB is constantly producing output the RQ weight is quite low so the WSB response is used when all the other algorithms fail to produce an answer. The resistance response is used when the sentence code (SC) for 'how do you feel' is found. The resistance variance is used when the SC for 'yes or no' is found.

Results so far are 'interesting' experiments are ongoing, there are only about 100 words assigned but each training session produces more words. The next experiment I'm going to try is listening to the signal.

To add new words WSB learning needs to be ticked in settings, words are added automatically to the TF and are also assigned words already in the TF to the words created by the WSB. There is no intellegence involved when associating words the next unused word in the TF is used so changing words manually is needed to be done inorder to maintain sensible replies To feed the raw words created from the WSB routine into the input edit TF entry 108 and set the fields thus: f1=8, f4=40, f5=wsall, f11=putininput. words are automatically assigned to the words created by the WSB.