Design of function-generating mapping networks by interactive neural-network simulation

We apply a new interactive simulation environment for neural-network development to the development of mapping networks, which produce learned or preset functions of real inputs. Function-mapping networks are useful for adaptive control and as general-purpose, self-learning function generators. DESIRE/NEUNET describes neural networks in a reasonable matrix language. A built-in, extra-fast compiler lets screen-edited programs execute immediately, without annoying translation delays, and simulations run faster than Microsoft FORTRAN.