Neural networks and the bias/variance dilemma
Neural Computation
Machine Learning
Optimal linear combinations of neural networks
Neural Networks
The how and why of electronic noses
IEEE Spectrum
Microprocessors: Principles and Applications
Microprocessors: Principles and Applications
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hi-index | 0.00 |
Electrochemical sensors, like ion-selective field transistors (ISFET), are electronic devices that merge solid-state electronic technology with chemical sensors so as to be sensitive to the concentration of a particular ion in a solution. However, as it has been previously reported, their response does not only depend on a single ion but also is affected by several interfering ions found in the solution to be measured. These interfering ions can be considered as noise and consequently, a post-processing stage that increases the SNR is obligatory. Our work shows how ensemble learning methods could be used in an array of chemical sensors in order to deal with this problem. In particular, we introduce a novel neural learning architecture for ISFET arrays, which employ ISFET models as prior knowledge. The proposed ensemble learning systems are RBF-like solutions based on bagging and optimal linear combination. Several experimental results are included, which demonstrate the interest and viability of the proposed solution.