Artificial Neural Networks

This chapter introduces the concept of the error back-propagation neural network (BPNN) as well as its applications in geosciences. For BPNN, the applying ranges and conditions, basic principles, calculation method, calculation flowchart, and case studies are provided. An optimal learning time count technique is presented for BPNN, explained in detail in an XOR problem and successfully used in 18 case studies in this book. There are five case studies in this chapter. Though the case studies are small, they reflect the whole process of calculation to benefit readers in understanding and mastering the applied techniques. In the latter two case studies, the results are much better than that of multiple regression analysis (MRA), since the studied problems are strongly nonlinear, but MRA is a linear algorithm whereas BPNN is a nonlinear one. The calculation results all coincide with practicality, and the specific structural parameters of BPNN are called mined knowledge . Finally, four exercises are provided.

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