# Optimization of models (ANN, radial basis, etc.) in R to target predictor levels to produce a desired response

I've learned how to use some packages like neuralnet and caret to create models based on designed experiments and am getting to the point where I think my models are relatively decent.

There's a program called ISight that some others at my company use for radial basis function analysis. I prefer R for a bunch of reasons, but one of the features it has is an "optimizer."

This lets you input a desired value for your response(s) and it chugs away and produces it's best suggestion of what input variable values will achieve this output. It also allows you to weight each output response importance (if you have multiple responses) and will drive toward a weighted/balanced response set based on your ranking.

I'm wondering if there's something like this for R?

I could just generate a huge data frame of various combinations, run it through the predict functions of my models and then look for the responses around my target value, which will tell me at what approximate input values I can achieve that value?

That will probably be fine for one response, but when I start trying to model multiple responses it will be much more difficult to figure out which input levels optimize across the whole array of desired output values.

Feel free to provide any terminology used to describe this. I'm new to the area and only know that ISight calls it the "optimizer." My google searches may have been poor due to not knowing what to call what I'm looking for.

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