Title :
Reducing the effect of quantization by weight scaling
Author_Institution :
Dept. of Electr. Eng., Eindhoven Univ. of Technol., Netherlands
fDate :
27 Jun-2 Jul 1994
Abstract :
By a statistical analysis of the behaviour of feedforward neural networks to errors in the weights, we show that an optimal scaling factor for the weights exists when the number of inputs to a neuron increases. When this scaling technique is used, the error in the output of a neuron due to quantization errors is not influenced by the size of the network anymore. This technique is especially interesting for the implementation of neural networks using analog electronics
Keywords :
feedforward neural nets; roundoff errors; statistical analysis; feedforward neural networks; optimal scaling factor; quantization errors; statistical analysis; weight scaling; Artificial neural networks; Feedforward neural networks; Multi-layer neural network; Neural network hardware; Neural networks; Neurons; Quantization; Signal to noise ratio; Statistical analysis; Stochastic processes;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374544
