Run testrtrl and type a number between 1 and 6 to select a demo. The top plot is the error per sample. The rest of the plots show the inputs, targets, and outputs. Consider the first choice, 1. 2-bit xor, delay is 2, 1 cycles. For this task, the recurrent net is given two binary-valued inputs, contains four units, and the output of the fourth unit is trained to produce the xor of the two inputs that occurred two steps in the past. 1 cycle means that the error is backpropagated every step. So, the plots show, in order from top to bottom, - RMS error per sample - input 1 (x1) - input 2 (x2) - target (x1 xor x2 for values of x1, x2 at time t-2) - output of unit 4 (the trained one that should duplicate the target) - output of unit 3 - output of unit 2 - output of unit 1 Now consider choice 2, 2. 1-bit identity, 4 step delay, 1 cycle, random input This task has one binary-valued input, and four units including one trained unit. The target is the input four steps ago, so this is just learning a four-step delay. This example doesn't always converge before training quits. Choice 3, bit 1 followed indefinitely by bit 2, 1 cycle, random input, is a task with two binary-valued inputs, two units including one trained to produce a high output when the second input becomes nonzero the first time after the occurrence of a nonzero first input. The net must learn to remember that bit 1 has occurred until bit 2 appears. When bit 2 appears, the memory of bit 1 is erased. The last three choices are not working demos. I decided to include them here for others as a possible starting point for using this code to learn models of dynamic systems. Choice 4, second order system, given state variables as input, is a task with two real-valued inputs that are the two state variables of a simple linear system. The net has two units with one trained to duplicate the first state variable. Choice 5 is the same linear system, but now the input is just a history of one of the state variables. Choice 6 is for a third order system, a suspension system. The net is given one state variable and action as input.