CIRCUIT IMPLEMENTATION OF A PEAK DETECTOR NEURAL-NETWORK

Peak detection is a basic data analysis problem which is essential in a large number of applications. In applications such as image processing, the large computational effort to locate peaks may prohibit operation in real-time. A Hopfield neural network is proposed for the peak detector to solve the real-time problem. Analytical expressions are derived for input separation, neuron gain, and restrictions on initial conditions. Hardware limitations are discussed and a modified circuit model is suggested for the Hopfield neuron. Solution time under thirty microseconds is obtainable with general purpose operational amplifiers independent of the number of inputs. Results obtained from a twenty-five neuron hardware implementation of the network lend credence to the theoretical results.