Locate the Bounding Box of Neural Networks with Intervals

A novel hybrid method is proposed for neural network training. The method consists of two phases: in the first phase the bounds for the neural network parameters are estimated using a genetic algorithm that uses intervals as chromosomes. In the second phase a genetic algorithm is used to train the neural network inside the bounding box located by the first phase. The proposed method is tested on a series of well-known datasets from the relevant literature and the results are reported.

[1]  Alán Aspuru-Guzik,et al.  Neural Networks for the Prediction of Organic Chemistry Reactions , 2016, ACS central science.

[2]  W. Kruskal,et al.  Use of Ranks in One-Criterion Variance Analysis , 1952 .

[3]  Xin Yao,et al.  Evolving artificial neural networks , 1999, Proc. IEEE.

[4]  P. Mackowiak,et al.  A critical appraisal of 98.6 degrees F, the upper limit of the normal body temperature, and other legacies of Carl Reinhold August Wunderlich. , 1992, JAMA.

[5]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[6]  I-Cheng Yeh,et al.  Knowledge discovery on RFM model using Bernoulli sequence , 2009, Expert Syst. Appl..

[7]  Weitao Yang,et al.  Multiscale Quantum Mechanics/Molecular Mechanics Simulations with Neural Networks. , 2016, Journal of chemical theory and computation.

[8]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[9]  Dimitrios I. Fotiadis,et al.  Automatic Seizure Detection Based on Time-Frequency Analysis and Artificial Neural Networks , 2007, Comput. Intell. Neurosci..

[10]  Mohammad Sadeghzadeh Maharluie,et al.  Detecting and ranking cash flow risk factors via artificial neural networks technique , 2016 .

[11]  Hak-Keung Lam,et al.  Tuning of the structure and parameters of a neural network using an improved genetic algorithm , 2003, IEEE Trans. Neural Networks.

[12]  Igor V Tetko,et al.  A renaissance of neural networks in drug discovery , 2016, Expert opinion on drug discovery.

[13]  Deniz Erdogmus,et al.  Clustering Approach to Quantify Long-Term Spatio-Temporal Interactions in Epileptic Intracranial Electroencephalography , 2007, Comput. Intell. Neurosci..

[14]  Sergei Manzhos,et al.  Neural network‐based approaches for building high dimensional and quantum dynamics‐friendly potential energy surfaces , 2015 .

[15]  Antanas Zilinskas,et al.  Interval Arithmetic Based Optimization in Nonlinear Regression , 2010, Informatica.

[16]  Yu Li,et al.  Particle swarm optimisation for evolving artificial neural network , 2000, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions' (cat. no.0.

[17]  Lukas Falat,et al.  Quantitative Modelling in Economics with Advanced Artificial Neural Networks , 2015 .

[18]  K Lehnertz,et al.  Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  F. Hayes-Roth,et al.  Concept learning and the recognition and classification of exemplars , 1977 .

[20]  Eldon Hansen,et al.  Global optimization using interval analysis , 1992, Pure and applied mathematics.

[21]  Ronald Bartzatt,et al.  Prediction of Novel Anti-Ebola Virus Compounds Utilizing Artificial Neural Network (ANN) , 2018 .

[22]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[23]  Richard A. Lewis,et al.  Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[24]  D. Rubinfeld,et al.  Hedonic housing prices and the demand for clean air , 1978 .

[25]  Rohit Chandra,et al.  Parallel programming in openMP , 2000 .

[26]  M.Cs. Markót,et al.  New interval methods for constrained global optimization , 2006, Math. Program..

[27]  G. Payre,et al.  Modified quasi-Newton methods for training neural networks , 1996 .

[28]  Max A. Little,et al.  Suitability of Dysphonia Measurements for Telemonitoring of Parkinson's Disease , 2008, IEEE Transactions on Biomedical Engineering.

[29]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.