A method of controlling a compressor rack having a plurality of variable capacity components, the method comprising: storing, in a computer readable medium accessible to a controller, a neural network having a plurality of neurons, each neuron being associated with a corresponding variable capacity component from the plurality of variable capacity components and having a neuron output based at least in part on whether a target rate of change of a compressor rack operating parameter is within a range of rates of change of said corresponding variable capacity component; evaluating said neuron output for each neuron of said neural network; selecting, with a controller, a variable capacity component from said plurality of variable capacity components as a designated variable capacity component based on said evaluating; operating, with said controller, said designated variable capacity component by varying a capacity of said designated variable capacity component; operating, with said controller, each variable capacity component of said plurality of variable capacity components, except for said designated variable capacity component, at a fixed capacity corresponding to only one of a maximum and a minimum capacity of said each variable capacity component; said method further comprising generating, with said controller, said neuron output for each neuron based on comparing said target rate of change with a range of rates of change for said each variable capacity component such that said neuron output is generated so that said variable capacity component having a largest variable capacity range, that meets said target rate of change, is selected as said designated variable capacity component. storing, in a computer readable medium accessible to a controller, a neural network having a plurality of neurons, each neuron being associated with a corresponding variable capacity component from the plurality of variable capacity components and having a neuron output based at least in part on whether a target rate of change of a compressor rack operating parameter is within a range of rates of change of said corresponding variable capacity component; evaluating said neuron output for each neuron of said neural network; selecting, with a controller, a variable capacity component from said plurality of variable capacity components as a designated variable capacity component based on said evaluating; operating, with said controller, said designated variable capacity component by varying a capacity of said designated variable capacity component; operating, with said controller, each variable capacity component of said plurality of variable capacity components, except for said designated variable capacity component, at a fixed capacity corresponding to only one of a maximum and a minimum capacity of said each variable capacity component; said method further comprising generating, with said controller, said neuron output for each neuron based on comparing said target rate of change with a range of rates of change for said each variable capacity component such that said neuron output is generated so that said variable capacity component having a largest variable capacity range, that meets said target rate of change, is selected as said designated variable capacity component.