tailieunhanh - Handbook of Reliability, Availability, Maintainability and Safety in Engineering Design - Part 73

Handbook of Reliability, Availability, Maintainability and Safety in Engineering Design - Part 73 studies the combination of various methods of designing for reliability, availability, maintainability and safety, as well as the latest techniques in probability and possibility modelling, mathematical algorithmic modelling, evolutionary algorithmic modelling, symbolic logic modelling, artificial intelligence modelling, and object-oriented computer modelling, in a logically structured approach to determining the integrity of engineering design. . | 704 5 Safety and Risk in Engineering Design close to reality and associative . include typical profiles but not descriptive. Examining the artificial neural network itself only shows meaningless numeric values. The ANN model is fundamentally a black box. On the other hand being continuous and derivable one can explore ANN models beyond simple statistical interrogation to determine typical profiles explicative variables network inputs and apply example data to determine their associated probabilities. Artificial neural networks have the ability to account for any functional dependency by discovering . learning and then modelling the nature of the dependency without needing to be prompted. The process goes straight from the data to the model without intermediary interpretation or problem simplification. There are no inherent conditions placed on the predicted variable which can be a yes no output a continuous value or one or more classes among n etc. However artificial neural networks are insensitive to unreliability in the data. Artificial neural networks have been applied in engineering design in predictive modelling of system behaviour using simulation augmented with ANN model interpolation Chryssolouris et al. 1989 as well as in interpolation of Taguchi robust design points so that a full factorial design can be simulated to search for optimal design parameter settings Schmerr et al. 1991 . An artificial neural network is a set of elements . neurodes or more commonly neurons linked to one another and that transmit information to each other through connected links. Example data a to i are given as the inputs to the ANN model. Various values of the data are then transmitted through the connections being modified during the process until on arrival at the bottom of the network they have become the predicted values for example the pair of risk probabilities P1 andP2 indicated in Fig. . a The Building Blocks of Artificial Neural Networks Artificial neural

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