Time series: theory and methods
Time series: theory and methods
Multilayer feedforward networks are universal approximators
Neural Networks
What size net gives valid generalization?
Neural Computation
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural networks and operations research: an overview
Computers and Operations Research - Special issue on neural networks and operations research
Predicting salinity in the Chesapeake Bay using back propagation
Computers and Operations Research - Special issue on neural networks and operations research
Practical neural network recipes in C++
Practical neural network recipes in C++
Neural networks: a systematic introduction
Neural networks: a systematic introduction
Neural Computation
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Mathematical Methods for Neural Network Analysis and Design
Mathematical Methods for Neural Network Analysis and Design
Handbook of Neural Computing Applications
Handbook of Neural Computing Applications
Acceleration Techniques for the Backpropagation Algorithm
Proceedings of the EURASIP Workshop 1990 on Neural Networks
Computational capabilities of recurrent NARX neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning long-term dependencies in NARX recurrent neural networks
IEEE Transactions on Neural Networks
Capabilities of a four-layered feedforward neural network: four layers versus three
IEEE Transactions on Neural Networks
ANNSTLF-a neural-network-based electric load forecasting system
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Backpropagation neural nets with one and two hidden layers
IEEE Transactions on Neural Networks
Recurrent neural networks and robust time series prediction
IEEE Transactions on Neural Networks
Neural network modeling of sorption of pharmaceuticals in engineered floodplain filtration system
Expert Systems with Applications: An International Journal
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Feedforward artificial neural networks (ANNs) that are trained with the back-propagation algorithm are a useful tool for modelling environmental systems. They have already been successfully used to model salinity, nutrient concentrations, air pollution, and algal growth. These successes, coupled with their suitability for modelling complex systems, have resulted in an increase in their popularity and their application in an ever increasing number of areas. They are generally treated as black box models that are able to capture underlying relationships when presented with input and output data. In many instances, little consideration is given to potential input data and the internal workings of ANNs. This can result in inferior model performance and an inability to accurately compare the performance of different ANN models. Back-propagation networks employ a modelling philosophy that is similar to that of statistical methods in the sense that unknown model parameters (i.e., connection weights) are adjusted in order to obtain the best match between a historical set of model inputs and corresponding outputs. Consequently, the principles that are considered good practice in the development of statistical models should be considered. In this paper, a systematic approach to the development of ANN based forecasting models is presented, which is intended to act as a guide for potential and current users of feedforward ANNs that are trained with the back-propagation algorithm. Issues that need to be considered in the model development phase are discussed and ways of addressing them presented. The major areas covered include data transformation, the determination of appropriate model inputs, the determination of an appropriate network geometry, the optimisation of connection weights, and validation of model performance.