Signal and image processing with neural networks: a C++ sourcebook
Signal and image processing with neural networks: a C++ sourcebook
Circuit complexity and neural networks
Circuit complexity and neural networks
High-level power estimation and the area complexity of Boolean functions
ISLPED '96 Proceedings of the 1996 international symposium on Low power electronics and design
Unveiling the ISCAS-85 Benchmarks: A Case Study in Reverse Engineering
IEEE Design & Test
Trace Cache Performance Parameters
ICCD '02 Proceedings of the 2002 IEEE International Conference on Computer Design: VLSI in Computers and Processors (ICCD'02)
Causal probabilistic input dependency learning for switching model in VLSI circuits
GLSVLSI '05 Proceedings of the 15th ACM Great Lakes symposium on VLSI
IEEE Transactions on Computers
Applicability of feed-forward and recurrent neural networks to Boolean function complexity modeling
Expert Systems with Applications: An International Journal
An efficient estimation of the ROBDD's complexity
Integration, the VLSI Journal
Role of function complexity and network size in the generalization ability of feedforward networks
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Preprocessing the data is an important step while creating neural network (NN) applications because this step usually has a significant effect on the prediction performance of the model. This paper compares different data processing strategies for NNs for prediction of Boolean function complexity (BFC). We compare NNs' predictive capabilities with (1) no preprocessing (2) scaling the values in different curves based on every curve's own peak and then normalizing to [0,1] range (3) applying z-score to values in all curves and then normalizing to [0,1] range, and (4) logarithmically scaling all curves and then normalizing to [0,1] range. The efficiency of these methods was measured by comparing RMS errors in NN-made BFC predictions for numerous ISCAS benchmark circuits. Logarithmic preprocessing method resulted in the best prediction statistics as compared to other techniques.