The invention relates to a random
neural network hardware realization apparatus. The apparatus comprises an input layer, a
hidden layer and an output layer, wherein the input layer consists of m input neurons I, each input
neuron I comprises a random number converter A, and after an input vector passes through the random number converter A, a random data sequence I is output; the
hidden layer consists of s hidden neurons J, each hidden
neuron J comprises a random number converter B, a random
function generator and a definite number converter C, a parameter code
stream passes through the random number converter B, is aligned to the random data sequence and passes through the random
function generator, so a random data sequence II is obtained, and the random number sequence II passes through the definite number converter C and a definite number I is output; the output layer consists of n output neurons K, each output
neuron K comprises a definite number converter D and a linear function processor, and a parameter code
stream II passes through the definite number converter D, is aligned to the definite number and passes through the linear function processor, so an object vector is output. According to the apparatus, hardware logic and wiring resources can be greatly reduced, circuit cost and
power consumption can be reduced, the network operation precision is high, and the fitting capability of training samples is enhanced.