Fuzzy systems theory and its applications
Fuzzy systems theory and its applications
An introduction to wavelets
Computational intelligence systems and applications: neuro-fuzzy and fuzzy neural synergisms
Computational intelligence systems and applications: neuro-fuzzy and fuzzy neural synergisms
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Principles of Neurocomputing for Science and Engineering
Principles of Neurocomputing for Science and Engineering
Fast learning in networks of locally-tuned processing units
Neural Computation
A taxonomy for wavelet neural networks applied to nonlinear modelling
International Journal of Systems Science
Application of neural networks as a non-linear modelling technique in food mycology
Expert Systems with Applications: An International Journal
A constructive algorithm for wavelet neural networks
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
Fuzzy wavelet networks for function learning
IEEE Transactions on Fuzzy Systems
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
Using wavelet network in nonparametric estimation
IEEE Transactions on Neural Networks
Analysis of input-output clustering for determining centers of RBFN
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
A new class of wavelet networks for nonlinear system identification
IEEE Transactions on Neural Networks
Analysis and synthesis of feedforward neural networks using discrete affine wavelet transformations
IEEE Transactions on Neural Networks
Modeling of survival curves in food microbiology using fuzzy wavelet neural networks
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
Hi-index | 12.05 |
The aim of the present work is to investigate the capabilities of a wavelet neural network for describing the inactivation pattern of Listeria monocytogenes by high hydrostatic pressure in milk, and to compare its performance against classic neural network architectures and models utilised in food microbiology. A new wavelet network is being proposed that includes a ''product operation'' layer between wavelet functions and output layer, while the connection output-layer weights have been replaced by a local linear model. Milk was artificially inoculated with an initial population of the pathogen and exposed to a range of high pressures (350, 450, 550, 600MPa) for up to 40min at ambient temperature (25^oC). Models were validated at 400 and 500MPa with independent experimental data. First or second order polynomial models were employed to relate the inactivation parameters to pressure, whereas all learning-based networks were utilised in a standard identification approach. The prediction performance of the proposed local linear wavelet network was better at both validation pressures. The development of accurate models to describe the survival curves of microorganisms in high pressure treatment would be very important to the food industry for process optimisation, food safety and would eventually expand the applicability of this non-thermal process.