Neuroet: An easy-to-use artificial neural network for ecological and biological modeling

Neuroet is an easy-to-use artificial neural network (NN) package designed to assist with determining relationships among variables in complex ecological and biological systems. The package, which is available for download from the web site http://noble.ce. washington.edu, features a procedure to optimize the architecture of NNs by adjusting the number of neurons in the hidden layer, and a novel procedure to identify the input variable, or combinations of input variables, that is/are important for predicting outputs. The package also includes a method to extract equations defining relationships among the data (independent of the NN package). The performance of Neuroet was assessed using benchmark standards for NNs. An example of the program’s utility is provided using an environmental data set.

[1]  Julian D. Olden,et al.  Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks , 2002 .

[2]  Michele Scardi,et al.  Developing an empirical model of phytoplankton primary production: a neural network case study , 1999 .

[3]  Peter A. Noble,et al.  Application of Neural Computing Methods for Interpreting Phospholipid Fatty Acid Profiles of Natural Microbial Communities , 2000, Applied and Environmental Microbiology.

[4]  John J. Kelly,et al.  Single-Base-Pair Discrimination of Terminal Mismatches by Using Oligonucleotide Microarrays and Neural Network Analyses , 2002, Applied and Environmental Microbiology.

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

[6]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[7]  Jose C. Principe,et al.  Neural and adaptive systems , 2000 .

[8]  Clifford M. Hurvich,et al.  Regression and time series model selection in small samples , 1989 .

[9]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[10]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[11]  Martin T. Hagan,et al.  Neural network design , 1995 .

[12]  Edward J. Callahan,et al.  Illuminating the 'Black Box' , 1998 .

[13]  A. Christopoulos,et al.  Fitting Models to Biological Data Using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting , 2004 .

[14]  Robert J. Marks,et al.  Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks , 1999 .