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An artificial neural network (ANN)‐based model was developed to analyse high‐cycle fatigue crack growth rates (d a/d N ) as a function of stress intensity ranges (Δ K ) for dual phase (DP) steel. The training data consisted of d a/d N at Δ K ranges between 5 and 16 MPa √ for DP steel with martensite contents in the range 32 to 76%. The ANN back‐propagation model with Gaussian activation function exhibited excellent agreement with the experimental results. The fatigue crack growth rate predictions were made to demonstrate its practical significance in a given real‐life situation. Because of the wide range of data points used during training of the model, it will provide a useful predictor for fatigue crack growth in DP steels. 2008 7th World Congress on Intelligent Control and Automation Chongqing, China 2008 7th World Congress on Intelligent Control and Automation IEEE, (2008). 978-1-4244-2113-8 Binbin Dan, Kuisheng Chen, Ling Xiong, Zhijun Rong and Jiangang Yi Research on multi-BP NN-based control model for molten iron desulfurization, (2008).

6133 61, 10.1109/WCICA.20 • T. Srpčič, Fire analysis of steel frames with the use of artificial neural networks, Journal of Constructional Steel Research, 63, 10, (1396), (2007).

NeuroShell Predictor The NeuroShell Predictor contains state-of-the-art algorithms that train extremely fast, enabling you to effectively solve prediction, forecasting, and estimation problems in a minimum amount of time without going through the tedious process of tweaking neural network parameters. Designed to be extremely easy to use, this product contains our most powerful neural networks. Reads and writes text files for compatibility with many other programs.

The prediction algorithms are the crowning achievement of several years of research. Gone are the days of dozens of parameters that must be artistically set to create a good model without over-fitting. Gone are the days of hiring a neural network expert or a statistician to build your predictive models. Two of the most commonly heard complaints about previous prediction systems, aside from being too hard to use, are that they are too slow or that they do not accurately tell you how important each of the variables is to the model.

We've taken care of those problems. That's why we have two training models from which to choose: 1.

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The first training method, which we call the “neural method” is based on an algorithm called Turboprop2, a variant of the famous Cascade Correlation algorithm invented at Carnegie Mellon University by Scott Fahlman. TurboProp2 dynamically grows hidden neurons and trains very fast. TurboProp2 models are built (trained) in a matter of seconds compared to hours for older neural networks types. The second method, the “genetic training method”, is a genetic algorithm variation of the General regression neural network (GRNN) invented by Donald Specht. It trains everything in an out-of-sample mode; it is essentially doing a 'one-hold-out' technique, also called 'jackknife' or 'cross validation'.

If you train using this method, you are essentially looking at the training set out-of-sample. This method is therefore extremely effective when you do not have many patterns on which to train.

The genetic training method takes longer to train as more patterns are added to the training set. Both training methods provide an analysis of independent variables (inputs) to help you determine which ones are most important in your model. The NeuroShell Predictor is so easy to use that it doesn't need a manual! Instead, there is an 'Instructor' that guides you through making the predictive models. At every stage of the Instructor, our extensive help file will give you all the information you need. When you have learned from the Instructor, you can turn it off and work from the toolbar or menus. The program does include an on-line, context sensitive reference manual that you may print yourself or just browse from your computer.